Source: https://patents.google.com/patent/JP5213670B2/en
Timestamp: 2019-12-12 14:14:37
Document Index: 714102874

Matched Legal Cases: ['art 152', 'art 12', 'art 16', 'art 52', 'art 53', 'art 54', 'art 150']

JP5213670B2 - Imaging apparatus and blur correction method - Google Patents
Imaging apparatus and blur correction method Download PDF
JP5213670B2
JP5213670B2 JP2008306307A JP2008306307A JP5213670B2 JP 5213670 B2 JP5213670 B2 JP 5213670B2 JP 2008306307 A JP2008306307 A JP 2008306307A JP 2008306307 A JP2008306307 A JP 2008306307A JP 5213670 B2 JP5213670 B2 JP 5213670B2
JP2008306307A
JP2009207118A (en
2008-01-16 Priority to JP2008007169 priority Critical
2008-01-16 Priority to JP2008007169 priority
2008-02-01 Priority to JP2008023075 priority
2008-12-01 Application filed by 三洋電機株式会社 filed Critical 三洋電機株式会社
2008-12-01 Priority to JP2008306307A priority patent/JP5213670B2/en
2009-09-10 Publication of JP2009207118A publication Critical patent/JP2009207118A/en
2013-06-19 Publication of JP5213670B2 publication Critical patent/JP5213670B2/en
The present invention relates to a photographing apparatus such as a digital still camera having a function of correcting image blur. The present invention also relates to a shake correction method for realizing the function.
The camera shake correction technique is a technique for reducing camera shake at the time of photographing, and is regarded as important as a differentiation technique in an imaging apparatus such as a digital still camera.
Among camera shake correction methods, methods using reference images taken with a short exposure time have been proposed (see Patent Documents 1 to 5 below). In this method, a correction target image is shot with an appropriate exposure time, while a reference image is shot with an exposure time shorter than the appropriate exposure time, and blurring of the correction target image is corrected using the reference image.
Since the blur of the reference image taken with a short exposure time is relatively small, the blur state of the correction target image can be estimated using the reference image. If the blur state of the correction target image can be estimated, the blur of the correction target image can be reduced by image restoration processing or the like.
An image restoration process using a Fourier iteration method has been proposed (see Non-Patent Document 1 below). FIG. 37 shows a block diagram of a configuration for realizing the Fourier iteration method. In the Fourier iteration method, the final restored image is estimated from the degraded image by repeatedly executing Fourier transform and inverse Fourier transform while correcting the restored image and the point spread function. In order to execute the Fourier iteration method, it is necessary to give an initial restored image (initial value of the restored image). Generally, the initial restored image is a random image or a degraded image as a camera shake image. Used.
JP 2001-346093 A JP 2002-258351 A JP 2002-290811 A JP 2006-101447 A JP 2007-267053 A G. R. Ayers and J. C. Dainty, "Iterative blind deconvolution method and its applications", OPTICS LETTERS, 1988, Vol. 13, No. 7, p.547-549
The camera shake correction method based on image processing using a reference image does not require a camera shake detection sensor such as an angular velocity sensor, and thus greatly contributes to a reduction in cost of the imaging apparatus.
However, although it is expected that the blur of the reference image shot with a short exposure time is expected to be small, in reality, the reference image may include a blur that cannot be ignored depending on the shooting technique of the photographer. In order to obtain a sufficient blur correction effect, it is necessary to use a reference image having no blur or small blur. However, there are cases where such a reference image cannot be shot in actual shooting. Also, since the exposure time is short, the signal-to-noise ratio of the reference image is necessarily relatively low. In order to obtain a sufficient blur correction effect, the signal-to-noise ratio of the reference image needs to be increased to some extent, but such a reference image may not be captured in actual shooting. Even if a blur correction process is performed using a reference image having a large blur or a reference image having a large signal-to-noise ratio, it is difficult to obtain a satisfactory blur correction effect, and a corrupted image may even be obtained. It is self-evident that it is better to avoid the execution of the shake correction process that hardly obtains the correction effect or the execution of the shake correction process that generates a corrupted image.
SUMMARY An advantage of some aspects of the invention is that it provides an imaging apparatus and a shake correction method that contribute to stabilization of a shake correction effect.
The first image pickup apparatus according to the present invention includes an image pickup unit that acquires an image by shooting, and blurring of the first image obtained by shooting is greater than an exposure time at the time of shooting the first image and the first image. The image processing apparatus includes: a shake correction processing unit that performs correction based on the second image captured with a short exposure time; and a control unit that controls whether or not the correction by the blur correction processing unit can be performed.
This makes it possible to perform correction only when an effective blur correction effect can be obtained. As a result, this contributes to stabilization of the blur correction effect.
Specifically, for example, the control unit includes a blur estimation unit that estimates the degree of blur of the second image, and controls whether or not the correction by the blur correction processing unit is executable based on the estimation result.
When the second image has a relatively large blur, it is often impossible to obtain an effective blur correction effect even if the blur correction process based on the second image is performed. Considering this, whether or not to perform correction by the shake correction processing means is controlled based on the estimation result of the degree of shake of the second image. As a result, it is possible to avoid the occurrence of a situation in which an image (or a corrupted image) having almost no blur correction effect is generated by forcibly performing the blur correction process, and as a result contributes to stabilization of the blur correction effect. To do.
More specifically, for example, the blur estimation unit estimates the degree of blur of the second image based on a result of comparison between the edge strength of the first image and the edge strength of the second image.
For example, the sensitivity for adjusting the brightness of the captured image differs between when the first image is captured and when the second image is captured. The comparison is performed through a process for suppressing a difference between edge intensities of the first and second images caused by a difference between the sensitivity at the time of shooting and the sensitivity at the time of shooting the second image.
Thereby, the influence of the difference in sensitivity with respect to the blur degree estimation can be suppressed, and higher accuracy of the blur degree estimation is expected.
Alternatively, for example, the blur estimation unit estimates the degree of blur of the second image based on a positional deviation amount between the first image and the second image.
Further alternatively, for example, the blur estimation means estimates the degree of blur of the second image based on the estimated image degradation function of the first image obtained using the first image and the second image.
And, for example, the blur estimation means refers to the value of each element of the estimated image degradation function when the estimated image degradation function is expressed in a matrix, and among the referenced values, the value deviating from a specified numerical range. The degree of blurring of the second image is estimated based on the sum of the extracted values.
The second image pickup apparatus according to the present invention includes an image pickup unit that acquires an image by shooting, and blurring of the first image obtained by shooting is greater than an exposure time at the time of shooting the first image and the first image. Based on one or more second images taken with a short exposure time, a shake correction processing unit that corrects based on the first image, and whether or not correction by the shake correction processing unit can be executed based on the shooting parameters of the first image, or Control means for controlling the number of the second images used for the correction.
Actual shooting environment conditions are reflected in the shooting parameters of the first image as the correction target image. With reference to the shooting parameters of the first image, it is possible to determine whether or not correction sufficient to bring about the necessary blur correction effect can be performed, or the number of second images sufficient to bring about the necessary blur correction effect. Can be determined. Therefore, as described above, whether or not the blur correction processing unit can execute correction or the number of the second images used for the correction is controlled based on the shooting parameters of the first image. Thereby, a stable blur correction effect can be obtained.
Specifically, for example, the control means determines whether or not the second image can be shot based on the shooting parameters of the first image, and controls the shooting means, and the second image shooting control means. Correction control means for controlling whether or not to perform correction by the shake correction processing means according to the determination result of whether or not photographing is possible.
For example, when it is determined that the necessary blur correction effect cannot be obtained based on the shooting parameters of the first image, the shooting of the second image and the correction by the blur correction processing unit are not executed. As a result, it is possible to avoid the occurrence of a situation in which an image (or a corrupted image) having almost no blur correction effect is generated by forcibly performing the blur correction process, and as a result contributes to stabilization of the blur correction effect. To do.
Alternatively, for example, the control unit determines the number of the second images to be used for correction by the blur correction processing unit based on the shooting parameters of the first image, and the second images corresponding to the determined number are captured. Second image shooting control means for controlling the image pickup means, the second image shooting control means determines the number of the second images to be one or a plurality, and the blur correction processing means, When the number of the second images is plural, one synthetic image is generated by adding and synthesizing the plural second images, and the first image is generated based on the first image and the synthetic image. Correct blurring.
For example, when it is determined that a necessary blur correction effect cannot be obtained with one second image, a composite having a signal-to-noise ratio sufficient to provide the necessary blur correction effect from a plurality of second images. Generate an image. Thereby, a stable blur correction effect can be obtained.
More specifically, for example, the shooting parameters of the first image include a focal length, an exposure time, and a sensitivity for adjusting the brightness of the image at the time of shooting the first image.
More specifically, for example, the second image capturing control unit sets the capturing parameter of the second image based on the capturing parameter of the first image.
More specifically, for example, in the first or second imaging apparatus, the blur correction processing unit handles an image based on the first image and an image based on the second image as a deteriorated image and an initial restored image, respectively. Then, blurring of the first image is corrected using a Fourier iteration method.
More specifically, for example, in the first or second imaging apparatus, the blur correction processing unit includes an image degradation function deriving unit that obtains an image degradation function representing an overall blur of the first image, and The blur of the first image is corrected based on an image degradation function, and the image degradation function derivation means converts the image based on the first image into a frequency domain and obtains the first function and the second image obtained by the transformation. The image degradation function on the frequency domain is tentatively obtained from the second function obtained by transforming the image based on the frequency domain, and the image degradation function on the obtained frequency domain is transformed on the spatial domain. The image degradation function is finally obtained through a process of correcting the function using a predetermined constraint condition.
Alternatively, for example, in the first or second imaging apparatus, the blur correction processing unit includes the first image, the second image, and a third image obtained by reducing noise in the second image. Is combined to generate a shake-corrected image in which the shake of the first image is corrected.
In addition to the first image photographed with a relatively long exposure time and the second image photographed with a relatively short exposure time, a third image in which noise of the second image is reduced is synthesized. As a result, an output image is generated. For this reason, the edge portion increases the synthesis ratio of the second and third images to increase the sharpness of the output image, while the non-edge portion increases the synthesis ratio of the first image to suppress the noise amount of the output image. It becomes possible. At this time, since the edge portion and the non-edge portion can be classified using the third image in which noise is suppressed, and each synthesis ratio can be obtained from the classification result, the non-edge portion including noise is defined as the edge portion. It is possible to suppress mixing of noise of the second image into the output image due to erroneous determination.
More specifically, for example, the blur correction processing unit generates a fourth image that is a composite image of the first image and the third image, and then combines the second image and the fourth image. The blur correction image is generated.
More specifically, for example, a composition ratio when compositing the first image and the third image is set based on a difference between the first image and the third image, and the second image and the third image are combined. The composition ratio when composing the four images is set based on the edges included in the third image.
According to the first blur correction method of the present invention, the first image obtained by shooting is shot with an exposure time shorter than the exposure time at the time of shooting the first image and the first image. The image processing apparatus includes: a shake correction processing step that is corrected based on the second or more second images; and a control step that controls whether or not the correction by the shake correction processing step is executable.
For example, the control step includes a blur estimation step for estimating the degree of blur of the second image, and controls whether or not the correction by the blur correction processing step can be performed based on the estimation result.
In the second blur correction method according to the present invention, the blur of the first image obtained by shooting is shot with an exposure time shorter than the exposure time at the time of shooting the first image and the first image. Further, a shake correction processing step for correcting based on one or more second images, and whether or not the correction by the shake correction processing step can be executed based on the imaging parameters of the first image, or the second used for the correction. And a control step for controlling the number of images.
According to the present invention, it is possible to provide an imaging apparatus and a shake correction method that contribute to stabilization of a shake correction effect.
Hereinafter, embodiments of the present invention will be specifically described with reference to the drawings. In each of the drawings to be referred to, the same part is denoted by the same reference numeral, and redundant description regarding the same part is omitted in principle. The first to fourth embodiments will be described later. First, matters that are common to the embodiments or items that are referred to in the embodiments will be described.
FIG. 1 is an overall block diagram of an imaging apparatus 1 according to an embodiment of the present invention. The imaging device 1 is a digital still camera capable of capturing and recording still images, or a digital video camera capable of capturing and recording still images and moving images.
The imaging device 1 includes an imaging unit 11, an AFE (Analog Front End) 12, a main control unit 13, an internal memory 14, a display unit 15, a recording medium 16, and an operation unit 17. The operation unit 17 is provided with a shutter button 17a.
FIG. 2 shows an internal configuration diagram of the imaging unit 11. The imaging unit 11 drives and controls the optical system 35, the diaphragm 32, the imaging element 33 including a CCD (Charge Coupled Devices), a CMOS (Complementary Metal Oxide Semiconductor) image sensor, and the like, and the optical system 35 and the diaphragm 32. And a driver 34. The optical system 35 is formed from a plurality of lenses including the zoom lens 30 and the focus lens 31. The zoom lens 30 and the focus lens 31 are movable in the optical axis direction. The driver 34 drives and controls the positions of the zoom lens 30 and the focus lens 31 and the opening degree of the diaphragm 32 based on the control signal from the main control unit 13, so that the focal length (view angle) and focus of the imaging unit 11 are controlled. The position and the amount of light incident on the image sensor 33 are controlled.
The image sensor 33 photoelectrically converts an optical image representing a subject incident through the optical system 35 and the diaphragm 32 and outputs an electrical signal obtained by the photoelectric conversion to the AFE 12. More specifically, the image sensor 33 includes a plurality of light receiving pixels arranged two-dimensionally in a matrix, and in each photographing, each light receiving pixel stores a signal charge having a charge amount corresponding to the exposure time. An analog signal from each light receiving pixel having a magnitude proportional to the amount of stored signal charge is sequentially output to the AFE 12 in accordance with a drive pulse generated in the imaging device 1. In the following description, “exposure” means exposure of the image sensor 33. Further, the length of the exposure time is controlled by the main control unit 13.
The AFE 12 amplifies the analog signal output from the imaging unit 11 (image sensor 33), and converts the amplified analog signal into a digital signal. The AFE 12 sequentially outputs this digital signal to the main control unit 13. The amplification degree of signal amplification in the AFE 12 is controlled by the main control unit 13.
The main control unit 13 includes a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM (Random Access Memory), and the like, and functions as a video signal processing unit. Based on the output signal of the AFE 12, the main control unit 13 generates a video signal representing an image captured by the imaging unit 11 (hereinafter also referred to as “captured image”). The main control unit 13 also has a function as display control means for controlling the display content of the display unit 15, and performs control necessary for display on the display unit 15.
The internal memory 14 is formed by SDRAM (Synchronous Dynamic Random Access Memory) or the like, and temporarily stores various data generated in the imaging device 1. The display unit 15 is a display device including a liquid crystal display panel and the like, and displays a photographed image, an image recorded on the recording medium 16, and the like under the control of the main control unit 13. The recording medium 16 is a non-volatile memory such as an SD (Secure Digital) memory card, and stores captured images and the like under the control of the main control unit 13.
The operation unit 17 receives an operation from the outside. The content of the operation on the operation unit 17 is transmitted to the main control unit 13. The shutter button 17a is a button for instructing photographing and recording of a still image. By pressing the shutter button 17a, the photographing and recording of a still image is instructed.
The shutter button 17a can be pressed in two stages. When the photographer lightly presses the shutter button 17a, the shutter button 17a is half pressed, and when the shutter button 17a is further pressed from this state. The shutter button 17a is fully pressed.
A still image as a photographed image can include a blur caused by camera shake. The main control unit 13 has a function of correcting the blur of the still image by image processing. FIG. 3 shows an internal block diagram of the main control unit 13 showing only parts particularly related to blur correction. As shown in FIG. 3, the main control unit 13 includes an imaging control unit 51, a correction control unit 52, and a shake correction processing unit 53.
The blur correction processing unit 53 corrects the blur of the normal exposure image based on the normal exposure image obtained by the normal exposure shooting and the short exposure image obtained by the short exposure shooting. Normal exposure photography means photography performed at an appropriate exposure time, and short exposure photography means photography performed at an exposure time shorter than the exposure time of normal exposure photography. The normal exposure image is a captured image (still image) obtained by normal exposure shooting, and the short exposure image is a captured image (still image) obtained by short exposure shooting. Further, the process for correcting the shake executed by the shake correction processing unit 53 is referred to as a shake correction process. The shooting control unit 51 includes a short exposure shooting control unit 54 that controls shooting of short exposure shooting. The shooting control for short exposure shooting includes control over the focal length, exposure time, and ISO sensitivity during short exposure shooting. The significance of symbols (f 1 etc.) shown in FIG. 3 will be clear from the following description.
As embodiments for explaining the operation of the imaging apparatus 1 including the detailed operation of each part shown in FIG. 3, first to fourth embodiments will be described below. In the imaging apparatus 1, control is performed on whether or not the blur correction process can be performed. The control is roughly classified into those based on the shooting parameters of the normal exposure image and those based on the blur degree of the short exposure image. Those based on the shooting parameters of the normal exposure image are described in the first and second embodiments, and those based on the blurring degree of the short exposure image are described in the third embodiment. The input of the normal exposure image and the short exposure image to the correction control unit 52 shown in FIG. 3 functions effectively in the third embodiment.
In this specification, data representing an image is referred to as image data, and in a sentence explaining that some processing (recording, saving, reading, etc.) is performed on the image data of a certain image, the description is simplified. The description of the image data may be omitted. For example, the expression of recording still image data is synonymous with the expression of recording still images. Further, for simplification of description, in the following description, it is assumed that the aperture value of the aperture 32 (the opening of the aperture 32) is constant.
A first embodiment will be described. The short exposure image is usually less blurred than the normal exposure image, and if the normal exposure image is corrected with the edge state of the short exposure image as a target, the blur of the normal exposure image is reduced. However, in order to obtain a sufficient blur correction effect, it is necessary to increase the signal-to-noise ratio (hereinafter referred to as the SN ratio) of the short exposure image to some extent. However, in actual shooting, there may be a case where a short-exposure image that can obtain a sufficient blur correction effect cannot be shot. In this case, it is difficult to obtain a satisfactory blur correction effect even if the short-exposure shooting is performed forcibly and a blur correction process is performed (an even worse image may be obtained). Considering this, in the first embodiment, when it is determined that a short-exposure image for obtaining a sufficient blur correction effect cannot be obtained, shooting of the short-exposure image and blur correction processing are not performed.
With reference to FIG. 4, shooting and correction operations of the imaging apparatus 1 according to the first embodiment will be described. FIG. 4 is a flowchart showing the flow of the operation. Each process of steps S1 to S10 is executed in the imaging apparatus 1.
First, in step S1, the main control unit 13 in FIG. 1 checks whether or not the shutter button 17a is in a half-pressed state. When it is confirmed that it is in a half-pressed state, the process proceeds from step S1 to step S2.
In step S2, the shooting control unit 51 acquires shooting parameters of the normal exposure image. The shooting parameters for the normal exposure image include the focal length f 1 , the exposure time t 1, and the ISO sensitivity is 1 when shooting the normal exposure image.
The focal length f 1 is determined based on the position of the lens in the optical system 35 at the time of shooting the normal exposure image, known information, and the like. The focal length in the following description including the focal length f 1 is assumed to be the focal length when converted to 35 mm film. The imaging control unit 51 includes the brightness of the subject (in other words, the amount of incident light on the imaging unit 11) based on the output signal of a photometric sensor (not shown) provided in the imaging device 1 or the output signal of the imaging element 33. A photometric unit (not shown) for measuring the light intensity is provided. The imaging control unit 51 determines the exposure time t 1 and the ISO sensitivity is 1 so that a normal exposure image having appropriate brightness is obtained based on the measurement result.
The ISO sensitivity means sensitivity defined by ISO (International Organization for Standardization), and the brightness (luminance level) of a captured image can be adjusted by adjusting the ISO sensitivity. Actually, the amplification factor of the signal amplification in the AFE 12 is determined according to the ISO sensitivity. The amplification degree is proportional to the ISO sensitivity. If the ISO sensitivity is doubled, the degree of amplification is also doubled, whereby the luminance value of each pixel of the photographed image is also doubled (however, saturation is ignored).
Needless to say, when other conditions are the same, the luminance value of each pixel of the captured image is proportional to the exposure time, and if the exposure time is doubled, the luminance value of each pixel of the captured image is also doubled. (However, saturation is ignored). The luminance value means the value of the luminance signal of the pixels forming the captured image. For a certain pixel, if the luminance value increases, the brightness of the pixel increases.
After step S2, in step S3, the main control unit 13 confirms whether or not the shutter button 17a is fully pressed. If it is in the fully pressed state, the process proceeds to step S4, whereas if it is not fully pressed, the process returns to step S1.
In step S4, the imaging apparatus 1 (imaging unit 11) performs normal exposure shooting to acquire a normal exposure image. The shooting control unit 51 controls the imaging unit 11 and the AFE 12 so that the focal length, the exposure time, and the ISO sensitivity at the time of shooting the normal exposure image become the focal length f 1 , the exposure time t 1, and the ISO sensitivity is 1 .
In subsequent step S5, the short exposure shooting control unit 54 determines whether or not the short exposure image can be shot based on the shooting parameter of the normal exposure image and sets the shooting parameter of the short exposure image. The determination method and the setting method will be described later, and the processing after step S6 subsequent to step S5 will be described first.
In step S6, a branch determination is made based on the determination result of whether or not a short-exposure image can be shot, and the short-exposure shooting control unit 54 controls shooting by the shooting unit 11 according to the determination result. Specifically, when it is determined in step S5 that a short exposure image can be taken, the process proceeds from step S6 to step S7. In step S7, the short exposure shooting control unit 54 controls the shooting unit 11 so that the short exposure shooting is performed. As a result, a short exposure image is acquired. In order to minimize changes in the shooting environment (including subject movement) between the normal exposure image and the short exposure image, the short exposure image is shot immediately after the normal exposure image is shot. On the other hand, if it is determined in step S5 that the short exposure image cannot be captured, the short exposure image is not captured (that is, the short exposure capture control unit 54 performs control for causing the short exposure image to be captured). This is not performed for the imaging unit 11).
The determination result of whether or not the short-exposure image can be captured is transmitted to the correction control unit 52 in FIG. 3, and the correction control unit 52 controls whether or not the shake correction processing by the shake correction processing unit 53 is executed according to the determination result. That is, when it is determined that a short-exposure image can be captured, the blur correction process can be performed, and when it is determined that a short-exposure image cannot be captured, the blur correction process cannot be performed.
In step S8, which is shifted to after taking the short exposure image, the shake correction processing unit 53 treats the normal exposure image obtained in step S4 and the short exposure image obtained in step S7 as a correction target image and a reference image, respectively. After that, the image data of the correction target image and the reference image is received. Thereafter, in step S <b> 9, the shake correction processing unit 53 executes a shake correction process for reducing the shake of the correction target image based on the correction target image and the reference image. The correction target image after blur reduction generated by the blur correction process is referred to as a blur correction image. The generated image data of the shake correction image is recorded on the recording medium 16 in step S10 following step S9.
With reference to FIG. 5, a method for determining whether or not a short-exposure image can be captured and a method for setting a capturing parameter for the short-exposure image will be described. FIG. 5 corresponds to a detailed flowchart of step S5 in FIG. 4, and the process of step S5 is realized by the short exposure shooting control unit 54 executing the processes of steps S21 to S26 in FIG.
Each process of step S21-S26 is demonstrated in order. First, the process of step S21 is performed. In step S <b> 21, the short exposure shooting control unit 54 temporarily sets shooting parameters for the short exposure image based on the shooting parameters for the normal exposure image. At this time, the shooting parameters are temporarily set so that the blur of the short-exposure image is negligible and the brightness of the short-exposure image is approximately the same as that of the normal exposure image. The shooting parameters for the short exposure image include the focal length f 2 , the exposure time t 2, and the ISO sensitivity is 2 when shooting the short exposure image.
In general, the reciprocal of the focal length of the optical system when converted to 35 mm film is called the camera shake limit exposure time. When a still image is taken with an exposure time shorter than the camera shake limit exposure time, the still image blur is Small enough to be ignored. For example, when the focal length at the time of 35 mm film conversion is 100 [mm], 1/100 [second] is the camera shake limit exposure time. In general, when the exposure time becomes 1 / a of the appropriate exposure time, in order to obtain an image with appropriate brightness, it is necessary to increase the ISO sensitivity by a (where a is a positive value). value). In step S21, the focal length of short exposure shooting is set to be the same as the focal length of normal exposure shooting.
Therefore, in step S21, the shooting parameters for the short exposure image are temporarily set so that “f 2 = f 1 , t 2 = 1 / f 1 and is 2 = is 1 × (t 1 / t 2 )”. .
After tentatively set in step S21, in step S22, usually based on the limit ISO sensitivity IS 2TH exposure time t 1 and the ISO sensitivity IS 1 and the short-exposure image of the exposure image, wherein "t 2TH = t 1 × (is 1 / Is 2TH ) ”, the limit exposure time t 2TH of the short exposure image is calculated.
The limit ISO sensitivity is 2TH is the ISO sensitivity at the boundary related to the quality of the S / N ratio of the short exposure image, and is set in advance according to the characteristics of the imaging unit 11 and the AFE 12. When a short-exposure image is acquired with an ISO sensitivity greater than the limit ISO sensitivity is 2TH , the S / N ratio of the acquired image becomes so bad that it is difficult to sufficiently obtain the blur correction effect. The limit exposure time t 2TH derived from the limit ISO sensitivity is 2TH is the boundary exposure time related to the quality of the S / N ratio of the short exposure image.
Thereafter, in step S23, by comparing the limit exposure time t 2TH calculated in the exposure time temporarily set short-exposure image t 2 and step S22 in step S21, the following three case analysis Do. Specifically, any one of the first inequality “t 2 ≧ t 2TH ”, the second inequality “t 2TH > t 2 ≧ t 2TH × k t ”, and the third inequality “t 2TH × k t > t 2 ” Is determined, and the following branch processing is performed according to the determination result. Here, k t is a preset limit exposure time coefficient, and 0 <k t <1.
When the first inequality is satisfied, a short exposure image having a sufficient S / N ratio can be taken even if the exposure time of the short exposure image is set to the camera shake limit exposure time (1 / f 1 ). A sufficient S / N ratio means a S / N ratio sufficient to provide a sufficient blur correction effect.
Therefore, when the first inequality is satisfied, the process directly proceeds from step S23 to step S25, 1 is substituted into the photographing / correction enable / disable flag FG, and the photographing parameter temporarily set in step S21 is used as it is. Short exposure shooting is performed. That is, when the first inequality is satisfied, the focal length, the exposure time, and the ISO sensitivity at the time of shooting the short exposure image in step S7 in FIG. 4 are the focal length f 2 (= f 1 ) calculated in step S21, The short exposure shooting control unit 54 controls the imaging unit 11 and the AFE 12 so that the exposure time t 2 (= 1 / f 1 ) and the ISO sensitivity is 2 (= is 1 × (t 1 / t 2 )) are obtained.
The imaging / correction enable / disable flag FG is a flag that represents the determination result of whether or not the short-exposure image can be executed and whether or not the shake correction process can be executed. Each part in the main control unit 13 corresponds to the value of the flag FG. Operate. A flag FG having a value of 1 indicates that a short-exposure image can be captured and blur correction processing can be performed. A flag FG having a value of 0 indicates that a short-exposure image cannot be captured. This indicates that the shake correction process cannot be executed.
On the other hand, when the second inequality is satisfied, if the exposure time of the short exposure image is set to the camera shake limit exposure time (1 / f 1 ), a short exposure image having a sufficient SN ratio cannot be taken. However, in this case, even if the exposure time of the short exposure image is set to the limit exposure time t2TH , it can be expected that camera shake is relatively small. Therefore, the establishment of the second inequality means that if the exposure time of a short exposure image is set to a time (t 2TH ) at which camera shake is expected to be relatively small, a short exposure image having a sufficient S / N ratio can be taken. Show.
Therefore, when the second inequality is satisfied, the process proceeds from step S23 to step S24, and the shooting parameters of the short exposure image are set so that “f 2 = f 1 , t 2 = t 2TH and is 2 = is 2TH ”. After resetting, 1 is substituted into the flag FG in step S25. Accordingly, the short exposure shooting in step S7 in FIG. 4 is executed using the reset shooting parameters. That is, when the second inequality is satisfied, the focal length f 2 (= f 1 ) in which the focal length, the exposure time, and the ISO sensitivity at the time of photographing the short exposure image in step S7 in FIG. 4 are reset in step S24. The short exposure shooting control unit 54 controls the imaging unit 11 and the AFE 12 so that the exposure time t 2 (= t 2TH ) and the ISO sensitivity is 2 (= is 2TH ) are obtained.
When the third inequality is satisfied, if the exposure time of the short exposure image is set to the camera shake limit exposure time (1 / f 1 ), a short exposure image having a sufficient SN ratio cannot be taken. In addition, even if the exposure time of the short exposure image is set to a time (t 2TH ) at which camera shake is expected to be relatively small, a short exposure image having a sufficient SN ratio cannot be taken.
Accordingly, when the third inequality is satisfied, the process proceeds from step S23 to step S26, and it is determined that the short-exposure image cannot be captured, and 0 is substituted for the flag FG. Thereby, a short exposure image is not taken.
When the first inequality or the second inequality is satisfied, 1 is assigned to the flag FG, so that the shake correction processing by the shake correction processing unit 53 is executed, but when the third inequality is satisfied, the flag FG Since 0 is assigned to, the shake correction processing by the shake correction processing unit 53 is not executed.
Specific numerical examples will be given. When the shooting parameters for the normal exposure image are “f 1 = 100 [mm], t 1 = 1/10 [seconds] and is 1 = 100”, the shooting parameter for the short exposure image is “f 2 ” in step S21. = 100 [mm], t 2 = 1/100 [second] and is 2 = 1000 ”. Here, when the limit ISO sensitivity of the short exposure image is set as is 2TH = 800, the limit exposure time t 2TH of the short exposure image is 1/80 [second] (step S22). Then, since “t 2TH = 1/80> 1/100”, the first inequality is not satisfied, and a short-exposure image having a sufficient SN ratio is obtained by performing short-exposure shooting using temporarily set shooting parameters. Can not get.
However, for example, when the limit exposure time coefficient k t is 0.5, “1/100 ≧ t 2TH × k t ” is satisfied, so that the second inequality is satisfied. In this case, a short exposure image having a sufficient S / N ratio can be taken by resetting the exposure time t 2 and ISO sensitivity is 2 of the short exposure image to the limit exposure time t 2TH and the limit ISO sensitivity is 2TH. A sufficient blur correction effect can be obtained by performing the blur correction process using the short exposure image.
FIG. 6 shows a curve 200 representing the relationship between the focal length and the camera shake limit exposure time. Points 201 to 204 corresponding to the above numerical examples are plotted in the graph of FIG. A point 201 corresponds to the shooting parameter of the normal exposure image, a point 202 on the curve 200 corresponds to the shooting parameter of the temporarily set short exposure image, and a point 203 corresponds to the focal length and the exposure. This corresponds to a state where the time is 100 [mm] and t 2TH (= 1/80 [second]), and the point 204 has a focal length and an exposure time of 100 [mm] and t 2TH × k t (= 1). / 160 [seconds]).
As described above, in order to reduce the blur of the short exposure image to a level that can be ignored, it is common to set the exposure time of the short exposure image to be equal to or less than the camera shake limit exposure time. However, even if the former is slightly larger than the latter, it is possible to obtain a short-exposure image with less blur to the extent that there is no practical problem. Specifically, the limit exposure time t 2TH (in the above numerical example, 1/80 [second]) of the short exposure image is larger than the camera shake limit exposure time (in the above numerical example, 1/100 [second]). also, (in the above numerical example, t 2TH × k t = 1 /160 [ sec]) k t times the limit exposure time t 2TH of the short-exposure image if it is below the camera shake limit exposure time, the limit exposure time t By performing short exposure photography using 2TH , it is possible to obtain a short exposure image with as little blur as practically possible (conversely, in order to satisfy such a relationship, the limit exposure time coefficient k The value of t is set in advance through experiments or the like). Considering this, even if the first inequality is not satisfied, if the second inequality is satisfied, the resetting of step S24 is executed to enable the short exposure image to be captured.
As described above, in the first embodiment, an SN ratio sufficient to realize a sufficient blur correction effect is obtained based on the shooting parameters of the normal exposure image reflecting the actual shooting environment conditions (such as the ambient illuminance of the imaging device 1). It is determined whether the short-exposure image can be captured, and according to the determination result, whether or not the short-exposure image can be captured and whether or not the blur correction process is performed are controlled. As a result, a stable blur correction effect can be obtained, and it is possible to avoid the occurrence of a situation where the blur correction process is forcibly performed to generate an image with little blur correction effect (or an altered image).
A second embodiment will be described. Part of the operations described in the first embodiment is also used in the second embodiment. With reference to FIG. 7, shooting and correction operations of the imaging apparatus 1 according to the second embodiment will be described. FIG. 7 is a flowchart showing the flow of the operation. Also in the second embodiment, first, the processes of steps S1 to S4 are executed. The processes in steps S1 to S4 are the same as those described in the first embodiment.
That is, when the shutter button 17a is half-pressed, the shooting control unit 51 acquires shooting parameters (focal length f 1 , exposure time t 1 and ISO sensitivity is 1 ) of the normal exposure image, and then the shutter button 17a is pressed. When fully depressed, in step S4, normal exposure shooting using the shooting parameters is executed and a normal exposure image is acquired. In the second embodiment, after capturing the normal exposure image, the process proceeds to step S31.
In step S31, the short exposure shooting control unit 54 determines whether the number of shots of the short exposure image should be one or a plurality based on the shooting parameters of the normal exposure image.
Specifically, first, the short exposure photographing control unit 54 executes the same processing as steps S21 and S22 of FIG. That is, in step S21, “f 2 = f 1 , t 2 = 1 / f 1 and is 2 = is set using the focal length f 1 , the exposure time t 1 and the ISO sensitivity is 1 included in the shooting parameters of the normal exposure image. The short-exposure image shooting parameters are temporarily set so as to be “is 1 × (t 1 / t 2 )”, and in step S22, the short-exposure image according to the expression “t 2TH = t 1 × (is 1 / is 2TH )”. The limit exposure time t 2TH is obtained.
Thereafter, by comparing the exposure time t 2 of the short exposure image temporarily set in step S 21 with the limit exposure time t 2TH calculated in step S 22, the first inequality “t 2 ≧ t 2TH ”, It is determined which of the two inequalities “t 2TH > t 2 ≧ t 2TH × k t ” and the third inequality “t 2TH × k t > t 2 ” holds. Note that k t is the same as that described in the first embodiment.
If the first or second inequality is satisfied, it is determined that the number of short-exposure images to be taken is one, and the process proceeds from step S31 to step S32, and steps S32, S33, S9, and S10 are performed. These processes are executed sequentially. The determination result that the number of short-exposure images to be taken is one is transmitted to the correction control unit 52. In this case, the correction control unit 52 performs the normal exposure image obtained in step S4 and the step S32. The blur correction processing unit 53 is controlled so that the short-exposure images obtained in this way are handled as the correction target image and the reference image, respectively.
That is, in step S32, the short exposure shooting control unit 54 performs shooting control so that the short exposure shooting is performed only once. One short exposure image is acquired by this short exposure photography. This short exposure image is taken immediately after the normal exposure image is taken. In subsequent step S33, the blur correction processing unit 53 treats the normal exposure image obtained in step S4 and the short exposure image obtained in step S32 as the correction target image and the reference image, respectively, and then the correction target image. And receiving image data of the reference image. Thereafter, in step S <b> 9, the shake correction processing unit 53 performs a shake correction process for reducing the shake of the correction target image based on the correction target image and the reference image, and generates a shake correction image. The generated image data of the shake correction image is recorded on the recording medium 16 in step S10 following step S9.
As in the first embodiment, when the first inequality is satisfied, the short exposure shooting in step S32 is performed using the shooting parameter temporarily set in step S21 as it is. That is, when the first inequality is satisfied, the focal length, the exposure time, and the ISO sensitivity at the time of shooting the short exposure image in step S32 are the focal length f 2 (= f 1 ) calculated in step S21, and the exposure time t. 2 (= 1 / f 1 ) and ISO sensitivity is 2 (= is 1 × (t 1 / t 2 )), the short exposure shooting control unit 54 controls the imaging unit 11 and the AFE 12. If the second inequality is satisfied, the process of step S24 in FIG. 5 is executed to reset the short-exposure image shooting parameters, and the short-exposure shooting in step S32 is performed using the reset shooting parameters. Do. That is, when the second inequality is satisfied, the focal length, the exposure time, and the ISO sensitivity at the time of shooting the short exposure image in step S32 are the focal length f 2 (= f 1 ) and the exposure time that are reset in step S24. The short exposure shooting control unit 54 controls the imaging unit 11 and the AFE 12 so that t 2 (= t 2TH ) and ISO sensitivity is 2 (= is 2TH ).
On the other hand, if the third inequality “t 2TH × k t > t 2 ” is satisfied in step S31, it is determined that there are a plurality of short-exposure images to be photographed, and the process proceeds from step S31 to step S34. Then, after executing the processes of steps S34 to S36, the processes of S9 and S10 are executed. The determination result that there are a plurality of short-exposure images to be photographed is transmitted to the correction control unit 52. In this case, the correction control unit 52 performs the normal exposure image obtained in step S4 and the step S35. The blur correction processing unit 53 is controlled so that the synthesized images obtained in this way are handled as the correction target image and the reference image, respectively. Although details will be described later, the composite image is generated by adding and combining a plurality of short-exposure images.
The processing of steps 34 to S36 will be described in order. In step S34, n S short-exposure images are continuously photographed immediately after the normal exposure image is photographed. First, the short-exposure photographing control unit 54 counts the number of short-exposure images to be photographed (that is, n S Value) and shooting parameters. Here, n S is an integer of 2 or more. The focal length, exposure time, and ISO sensitivity at the time of shooting each short exposure image acquired in step S34 are represented by f 3 , t 3, and is 3 , respectively, and n s , f 3 , t 3, and is 3 are determined. The method will be described. In this description, reference is also made to the imaging parameters (f 2 , t 2 and is 2 ) temporarily set in step S21 of FIG.
n S , f 3 , t 3 and is 3 are set so as to satisfy all of the following first to third conditions.
The first condition is that "k t times the exposure time t 3 is less than the camera shake limit exposure time". The first condition is provided in order to suppress the blurring of each short-exposure image so as to cause no practical problem. To satisfy the first condition,
The inequality “t 2 ≧ t 3 × k t ” must be satisfied.
The second condition is a condition that “the brightness of the normal exposure image and the synthesized image to be obtained in step S35 are the same (or the same level)”. To satisfy the second condition,
The equation “t 3 × is 3 × n S = t 1 × is 1 ” must be satisfied.
The third condition is a condition that “the ISO sensitivity of the composite image to be obtained in step S35 is equal to or lower than the limit ISO sensitivity of the short-exposure image”. The third condition is provided to obtain a composite image having a sufficient S / N ratio. To satisfy the third condition,
It is necessary to satisfy the inequality “is 3 × √n S ≦ is 2TH ”.
In general, the ISO sensitivity of the image obtained images of ISO sensitivity IS 3 and n S sheets additive synthesis is expressed by is 3 × √n S. Note that √n S represents the positive square root of n S.
Specific numerical examples will be given. Consider a case where the normal exposure image shooting parameters are “f 1 = 200 [mm], t 1 = 1/10 [seconds] and is 1 = 100”. Further, the limit ISO sensitivity IS 2TH is a 800 and limit exposure time coefficient k t of the short-exposure image is assumed to be 0.5. Then, in the provisional setting of the shooting parameters for the short exposure image in step S21 in FIG. 5, “f 2 = 200 [mm], t 2 = 1/200 [seconds] and is 2 = 2000”. Further, the limit exposure time t 2TH of the short exposure image is 1/80 [second] because t 2TH = t 1 × (is 1 / is 2TH ) = 1/80 . Then, since “t 2TH × k t > t 2 ”, the process proceeds from step S31 in FIG. 7 to step S34.
In this case, in order to satisfy the first condition, it is necessary to satisfy the following formula (A-1).
1/100 ≧ t 3 (A-1)
If 1/100 is substituted for t 3 , it is necessary to satisfy the following equation (A-2) from the equation corresponding to the second condition. In addition, the following formula (A-3) corresponding to the third condition needs to be satisfied separately. Since “n S ≧ 1.5625” from the expressions (A-2) and (A-3), it is understood that n S may be set to 2 or more.
is 3 × n S = 1000 (A-2)
is 3 × √n S ≦ 800 (A-3)
If 2 is substituted for n S , the equation corresponding to the second condition is expressed by the following equation (A-4), and the inequality corresponding to the third condition is expressed by the following equation (A-5).
t 3 × is 3 = 5 (A-4)
is 3 ≦ 800 / 1.414≈566 (A-5)
From the formulas (A-4) and (A-5), “t 3 ≧ 0.0088” is derived. Considering the formula (A-1) as well, even when n S = 2, setting t 3 so as to satisfy “1/100 ≧ t 3 ≧ 0.0088” is expected to provide a sufficient blur correction effect. A synthesized image can be generated. If n S and t 3 are determined, is 3 is automatically determined. Note that f 3 is the same as f 1 . In the above example, t 3 satisfying all of the first to third conditions can be set with 2 being substituted for n S , but if it cannot be set, the value of n S is set until it can be set. You should increase sequentially.
In step S34, n S , f 3 , t 3 and is 3 are obtained according to the above-described method, and n S short-exposure photographing is executed according to them. The image data of the n S short-exposure images acquired in step S34 is sent to the blur correction processing unit 53. The blur correction processing unit 53 generates a composite image by adding and synthesizing the n S short-exposure images. A method of addition synthesis will be described.
The blur correction processing unit 53 combines the n S short-exposure images after aligning them. For the sake of concrete explanation, consider a case where n S is 3, and the first, second, and third short exposure images are sequentially captured after the normal exposure image is captured. In this case, for example, the first short-exposure image is used as a reference image and each of the second and third short-exposure images is regarded as a non-reference image, and then each non-reference image is aligned with the reference image. To synthesize. The meanings of “positioning” and “positional deviation correction” described later are the same.
A process of aligning and synthesizing one reference image and one non-reference image will be described. For example, a characteristic small region (for example, a small region of 32 × 32 pixels) is extracted from the reference image using a Harris corner detector. A characteristic small area refers to a rectangular area having a relatively large number of edge components (in other words, a relatively strong contrast) in the extraction source image, and is an area including a characteristic pattern, for example. A characteristic pattern has, for example, a luminance change in two or more directions such as a corner of an object, and the position of the pattern (position on the image) can be easily detected by image processing based on the luminance change. Meaning a simple pattern. Then, the image in the small area extracted from the reference image is used as a template, and a small area having the highest similarity with the template is searched from the non-reference image using the template matching method. Then, a deviation amount between the searched position of the small area (position on the non-reference image) and the position of the small area extracted from the reference image (position on the reference image) is calculated as a positional deviation amount Δd. The positional deviation amount Δd is a two-dimensional amount including a horizontal component and a vertical component, and is expressed as a so-called motion vector. The non-reference image can be regarded as an image in which a positional deviation has occurred by an amount corresponding to the positional deviation amount Δd with reference to the reference image. Therefore, the non-reference image is corrected for position deviation by performing coordinate transformation (affine transformation or the like) on the non-reference image so that the position deviation amount Δd is canceled out. For example, a geometrical conversion parameter for performing this coordinate conversion is obtained, and the positional deviation correction is performed by performing coordinate conversion of the non-reference image on the coordinates where the reference image is defined. A pixel located at the coordinates (x + Δdx, y + Δdy) in the non-reference image before the positional deviation correction is converted into a pixel located at the coordinates (x, y) by the positional deviation correction. Δdx and Δdy are a horizontal component and a vertical component of Δd, respectively. Then, the reference image and the non-reference image after the positional deviation correction are combined so that the corresponding pixel signals are added. The pixel signal of the pixel located at the coordinate (x, y) in the image obtained by the synthesis is located at the coordinate (x, y) with the pixel signal of the pixel in the reference image located at the coordinate (x, y). It corresponds to a signal obtained by adding the pixel signals of the pixels in the non-reference image after the positional deviation correction.
The alignment and synthesis process as described above is executed for each non-reference image. As a result, a synthesized image obtained by synthesizing the first short-exposure image and the second and third short-exposure images after positional deviation correction is obtained. This composite image is a composite image to be generated in step S35 of FIG. A plurality of characteristic small regions are extracted from the reference image, a plurality of small regions corresponding to the plurality of small regions are searched from the non-reference image using the template matching method, and the plurality of extracted regions in the reference image are extracted. The above-described displacement correction may be performed by obtaining the geometric transformation parameter from the position of the small region and the positions of the plurality of small regions searched in the non-reference image.
After generating the composite image in step S35, in step S36, the blur correction processing unit 53 treats the normal exposure image obtained in step S4 as the correction target image, and receives image data of the correction target image. On the other hand, the composite image generated in step S35 is handled as a reference image. Then, the process of step S9 and S10 is performed. That is, based on the correction target image and the reference image as the composite image, a blur correction process for reducing blur of the correction target image is executed to generate a blur correction image. The generated image data of the shake correction image is recorded on the recording medium 16 in step S10 following step S9.
As described above, in the second embodiment, the short exposure necessary for obtaining a sufficient blur correction effect based on the shooting parameters of the normal exposure image reflecting the actual shooting environment conditions (such as the ambient illuminance of the imaging device 1). The number of images is determined, and blur correction processing is executed using one or a plurality of short-exposure images obtained according to the determination result. Thereby, a stable blur correction effect can be obtained.
Next, a third embodiment will be described. If a short-exposure image that is so small that the blur is negligible can be acquired, a sufficient blur correction effect can be obtained by correcting the normal exposure image with the edge state of the short-exposure image as a target. However, even if the exposure time of the short exposure image is set so that such a short exposure image is acquired, the short exposure image actually includes a blur that cannot be ignored due to the photographing technique of the photographer. Sometimes. In such a case, even if the blur correction process based on the short exposure image is performed, it is difficult to obtain a satisfactory blur correction effect (even a deteriorated image may be obtained).
Considering this, the correction control unit 52 of FIG. 3 in the third embodiment estimates the degree of blur included in the short exposure image based on the normal exposure image and the short exposure image, and the blur degree is relatively small. Only when it is estimated, it is determined that the shake correction process based on the short exposure image can be executed.
With reference to FIG. 8, the imaging and correction operations of the imaging apparatus 1 according to the third embodiment will be described. FIG. 8 is a flowchart showing the flow of the operation. Also in the third embodiment, first, the processes of steps S1 to S4 are executed. The processes in steps S1 to S4 are the same as those described in the first embodiment.
That is, when the shutter button 17a is half-pressed, the shooting control unit 51 acquires shooting parameters (focal length f 1 , exposure time t 1 and ISO sensitivity is 1 ) of the normal exposure image, and then the shutter button 17a is pressed. When fully depressed, in step S4, normal exposure shooting using the shooting parameters is executed and a normal exposure image is acquired. In the third embodiment, after shooting the normal exposure image, the process proceeds to step S41.
In step S41, the short exposure shooting control unit 54 sets shooting parameters for the short exposure image based on the shooting parameters for the normal exposure image. Specifically, “f 2 = f 1 , t 2 = t 1 × k Q and is 2 = is obtained using the focal length f 1 , the exposure time t 1 and the ISO sensitivity is 1 included in the shooting parameters of the normal exposure image. The shooting parameters for the short-exposure image are set so that “is 1 × (t 1 / t 2 )”. Here, the coefficient k Q is a coefficient whose value is set in advance so as to satisfy the inequality “0 <k Q <1”, and has a value of about 0.1 to 0.5, for example.
In subsequent step S42, the short exposure shooting control unit 54 performs shooting control so that the short exposure shooting according to the shooting parameters of the short exposure image set in step S41 is performed. One short exposure image is acquired by this short exposure photography. This short exposure image is taken immediately after the normal exposure image is taken. Specifically, the focal length, exposure time, and ISO sensitivity at the time of taking a short exposure image are the focal length f 2 (= f 1 ) and the exposure time t 2 (= t 1 × k Q set in step S41. ) And ISO sensitivity is 2 (= is 1 × (t 1 / t 2 )), the short exposure shooting control unit 54 controls the imaging unit 11 and the AFE 12.
In subsequent step S43, the correction controller 52 determines the degree of blurring of the short exposure image (the degree of blurring included in the short exposure image) based on the image data of the normal exposure image and the short exposure image obtained in steps S4 and S42. Is estimated. This estimation method will be described later.
If the correction control unit 52 determines that the degree of blurring of the short-exposure image is relatively small, the process proceeds from step S43 to step 44 and steps S44, S9, and S10 are executed. That is, when it is determined that the degree of blur is relatively small, the correction control unit 52 determines that the blur correction process can be performed, and controls the blur correction processing unit 53 so that the blur correction process is performed. Under this control, the blur correction processing unit 53 treats the normal exposure image obtained in step S4 and the short exposure image obtained in step S42 as the correction target image and the reference image, respectively, and then the correction target image. And receiving image data of the reference image. Thereafter, in step S <b> 9, the shake correction processing unit 53 performs a shake correction process for reducing the shake of the correction target image based on the correction target image and the reference image, and generates a shake correction image. The generated image data of the shake correction image is recorded on the recording medium 16 in step S10 following step S9.
On the other hand, when the correction control unit 52 determines that the degree of blurring of the short-exposure image is relatively large, the correction control unit 52 determines that the blur correction process cannot be performed and the blur correction process is not performed. The correction processing unit 53 is controlled.
As described above, in the third embodiment, the blurring degree of the short exposure image is estimated, and the blurring correction process is executed only when it is determined that the blurring degree is relatively small. As a result, a stable blur correction effect can be obtained, and it is possible to avoid the occurrence of a situation where the blur correction process is forcibly performed to generate an image with little blur correction effect (or an altered image).
Note that the shooting parameters for the short-exposure image may be set by the method shown in the first embodiment. That is, the shooting parameters for the short-exposure image may be set by executing the processing including steps S21 to S26 in FIG. 5 in step S41. In this case, at the time of shooting the short exposure image in step S42, “f 2 = f 1 , t 2 = 1 / f 1 and is 2 = is 1 × (t 1 / t 2 )” or “ The imaging unit 11 and the AFE 12 are controlled so that “f 2 = f 1 , t 2 = t 2TH and is 2 = is 2TH ”. If the inequality “t 2TH × k t > t 2 ” holds for the exposure time t 2 temporarily set in step S21 of FIG. 5, the short exposure image is not taken in step S42. It is also possible to make it.
A method for estimating the degree of blur of a short exposure image will be described. First to third estimation methods that can be employed as this estimation method will be individually illustrated. The normal exposure image and the short exposure image in the description of the first to third estimation methods refer to the normal exposure image and the short exposure image obtained in steps S4 and S42 in FIG. 8, respectively.
First, the first estimation method will be described. In the first estimation method, the degree of blurring of the short exposure image is estimated by comparing the edge intensities of the normal exposure image and the short exposure image. This will be described more specifically.
FIG. 9 shows a flowchart of processing executed by the correction control unit 52 in FIG. 3 when the first estimation method is adopted. When the first estimation method is adopted, the correction control unit 52 sequentially executes the processes of steps S51 to S51.
First, in step S51, the correction control unit 52 extracts a characteristic small area from the normal exposure image by using a Harris corner detector or the like, and handles the image in the small area as the first evaluation image. . The significance of the characteristic small area is as described in the explanation of the second embodiment.
Subsequently, a small area corresponding to the small area extracted from the normal exposure image is extracted from the short exposure image, and an image in the small area extracted from the short exposure image is handled as the second evaluation image. The image sizes of the first and second evaluation images (the number of pixels in the horizontal direction and the vertical direction) are equal. When the positional deviation between the normal exposure image and the short exposure image can be ignored, the center coordinates of the small area extracted from the normal exposure image (the center coordinates in the normal exposure image) and the small area extracted from the short exposure image The small area is extracted so that the center coordinates of the area (center coordinates in the short exposure image) are equal. If the positional deviation cannot be ignored, a corresponding small region may be searched using a template matching method or the like. That is, for example, an image in a small area extracted from a normal exposure image is used as a template, and a small area having the highest similarity with the template is searched from a short exposure image using a known template matching method. The image in the small area is set as the second evaluation image.
Instead of generating the first and second evaluation images by extracting characteristic small areas, the small area located at the center of the normal exposure image is simply used as the first evaluation image and the short exposure image is extracted. It is also possible to extract a small region located at the center as the second evaluation image. Alternatively, the whole image of the normal exposure image can be handled as the first evaluation image, and the whole image of the short exposure image can be handled as the second evaluation image.
After the first and second evaluation images are set, in step S52, edge strengths in the horizontal direction and the vertical direction of the first evaluation image are calculated, and edges in the horizontal direction and the vertical direction of the second evaluation image are calculated. Calculate the intensity. Hereinafter, without distinguishing the first evaluation image and the second evaluation image, both may be collectively referred to, or one of them may be simply referred to as an evaluation image.
A method for calculating the edge strength in step S52 will be described. FIG. 10 shows a pixel array of one evaluation image. Assume that the number of pixels in the horizontal and vertical directions of the evaluation image is M and N, respectively. M and N are integers of 2 or more. The evaluation image is regarded as an M × N matrix with the origin O of the evaluation image as a reference, and each pixel forming the evaluation image is represented by P [i, j]. i takes each integer between 1 and M and represents the horizontal coordinate value of the pixel of interest on the evaluation image. j takes each integer between 1 and N, and represents the vertical coordinate value on the evaluation image of the pixel of interest. The luminance value at the pixel P [i, j] is represented by Y [i, j]. FIG. 11 shows a matrix representation of luminance values. As Y [i, j] increases, the luminance of the corresponding pixel P [i, j] increases.
The correction control unit 52 calculates the edge strength in the horizontal direction and the vertical direction of the first evaluation image for each pixel, and calculates the edge strength in the horizontal direction and the vertical direction of the second evaluation image for each pixel. A value representing the calculated edge strength is called an edge strength value. The edge strength value is assumed to be zero or a positive value. That is, the edge strength value represents the magnitude (absolute value) of the corresponding edge strength. The edge strength values in the horizontal direction and the vertical direction calculated for the pixel P [i, j] of the first evaluation image are represented by E H1 [i, j] and E V1 [i, j], respectively. The edge strength values in the horizontal direction and the vertical direction calculated for the image P [i, j] of the second evaluation image are represented by E H2 [i, j] and E V2 [i, j], respectively.
The edge strength value is calculated using an edge extraction filter such as a primary differential filter, a secondary differential filter, or a Sobel filter. For example, when the second-order differential filters as shown in FIGS. 12 and 13 are used when calculating the edge strength values in the horizontal direction and the vertical direction, the edge strength values E H1 [i, j] for the first evaluation image are used. And E V1 [i, j] are E H1 [i, j] = | −Y [i−1, j] + 2 · Y [i, j] −Y [i + 1, j] | and E V1 [i, j] j] = | −Y [i, j−1] + 2 · Y [i, j] −Y [i, j + 1] | Note that when calculating edge intensity values for pixels located at the top, bottom, left, and right ends of the first evaluation image (for example, pixel P [1,2]), they are arranged outside the first evaluation image. The luminance value of a pixel in the normal exposure image (for example, a pixel adjacent to the left side of the pixel P [1,2]) may be used. The edge intensity values E H2 [i, j] and E V2 [i, j] for the second evaluation image are calculated in the same manner.
After calculating the edge intensity value for each pixel, in step S53, the correction control unit 52 corrects each edge intensity value by subtracting a preset offset value from each edge intensity value. Specifically, the corrected edge intensity values E H1 '[i, j], E V1 ' [i, j], E H2 '[i are calculated according to the following formulas (B-1) to (B-4). , J] and E V2 ′ [i, j]. However, if the edge strength value becomes negative by subtracting the offset value OF 1 or OF 2 from a certain edge strength value, the edge strength value is set to zero. For example, when “E H1 [1,1] −OF 1 <0”, E H1 ′ [1,1] is set to zero.
In subsequent step S54, the correction control unit 52 integrates the corrected edge intensity values according to the following formulas (B-5) to (B-8), thereby integrating the edge intensity integrated values D H1 , D V1 , D H2, and D. Calculate V2 . The edge intensity integrated value D H1 is an integrated value of (M × N) corrected edge intensity values E H1 '[i, j] (that is, 1 ≦ i ≦ M and 1 ≦ j ≦ N). Integrated value of all edge intensity values E H1 '[i, j] within the range). The same applies to the edge intensity integrated values D V1 , D H2 and D V2 .
In step S55, the correction control unit 52 compares the edge intensity integrated value calculated for the first evaluation image with the edge intensity integrated value calculated for the second evaluation image, and the comparison is made. Based on the result, the degree of blurring of the short exposure image is estimated. When blurring is large, the edge strength integrated value becomes small. For this reason, the blurring degree of the short exposure image is compared when at least one of the horizontal and vertical edge intensity integrated values calculated for the second evaluation image is smaller than those for the first evaluation image. Judged to be large.
Specifically, the following inequalities (B-9) and (B-10) are evaluated for success / failure, and if at least one of the inequalities (B-9) and (B-10) is true, It is determined that the degree of blurring of the exposure image is relatively large. In this case, it is determined that the shake correction process cannot be executed. On the other hand, when both inequalities (B-9) and (B-10) are not established, it is determined that the degree of blurring of the short exposure image is relatively small. In this case, it is determined that the shake correction process can be executed.
As can be understood from the calculation method of the edge intensity integrated value, the edge intensity integrated values D H1 and D V1 take values corresponding to the magnitudes of blurring in the horizontal direction and the vertical direction of the first evaluation image, respectively. The edge intensity integrated values D H2 and D V2 take values corresponding to the magnitude of blurring in the horizontal direction and vertical direction of the second evaluation image, respectively. The correction control unit 52 has a relatively small degree of blurring of the short-exposure image only when the magnitude of the blur of the second evaluation image is smaller than that of the first evaluation image in both the horizontal direction and the vertical direction. It is determined that the blur correction process is possible.
The correction using the offset value for the edge intensity value is the edge intensity between the first and second evaluation images caused by the difference between the ISO sensitivity at the time of shooting the normal exposure image and the ISO sensitivity at the time of shooting the short exposure image. This works in the direction of suppressing the difference. In other words, the correction acts in a direction to suppress the influence of the former difference (ISO sensitivity difference) on the blur degree estimation. The reason for this will be described with reference to FIGS. 14 (a) and 14 (b).
14A and 14B, solid lines 211 and 221 respectively represent the luminance value distribution and edge intensity value distribution of an image that is not affected by noise, and broken lines 212 and 222 are affected by noise, respectively. It represents the luminance value distribution and edge intensity value distribution of the image. In FIGS. 14A and 14B, attention is paid only to the one-dimensional direction, and the horizontal axis in each graph of FIGS. 14A and 14B represents the pixel position. When there is no influence of noise, the edge intensity value in the flat part of luminance is zero, but when there is an influence of noise, an edge intensity value that is not zero is generated even in the flat part of luminance. An alternate long and short dash line 223 in FIG. 14B represents the offset value OF 1 or OF 2 .
Since the ISO sensitivity of the normal exposure image is relatively low, the influence of noise on the normal exposure image is relatively small. On the other hand, the influence of noise on the short exposure image is relatively large because the ISO sensitivity of the short exposure image is relatively high. Therefore, the normal exposure image generally corresponds to the solid lines 211 and 221, and the short exposure image generally corresponds to the broken lines 212 and 222. If the edge intensity integrated value is obtained without performing subtraction correction using the offset value, the edge intensity integrated value for the short-exposure image increases by the edge intensity caused by noise, and there is a difference in ISO sensitivity. The influence appears on the edge strength integrated value. Considering this, subtraction correction using the above-described offset value is performed. By this subtraction correction, an edge intensity component having a relatively small value due to noise is excluded, and the influence of the difference in ISO sensitivity on the blur degree estimation can be suppressed. As a result, the accuracy of blur estimation is improved.
The offset values OF 1 and OF 2 can be set in advance at the manufacturing stage or design stage of the imaging device 1. For example, normal exposure photography and short exposure photography are performed in a state where no or little light is incident on the image sensor 33 to obtain two black images, and offsets based on edge intensity integrated values for the two black images are obtained. The values OF 1 and OF 2 can be determined. Further, the offset values OF 1 and OF 2 may be the same value or different values.
FIG. 15A shows an example of a normal exposure image. The normal exposure image in FIG. 15A has a relatively large blur in the horizontal direction. FIGS. 15B and 15C show a first example and a second example of a short exposure image. The short-exposure image in FIG. 15B has almost no blur in the horizontal direction and the vertical direction. For this reason, when the above-described blur degree estimation is performed on the normal exposure image in FIG. 15A and the short exposure image in FIG. 15B, both the inequalities (B-9) and (B-10) are obtained. It is determined that the degree of blurring of the short-exposure image is relatively small. On the other hand, the short exposure image of FIG. 15C has a relatively large blur in the vertical direction. For this reason, when the above-described blur degree estimation is performed on the normal exposure image in FIG. 15A and the short exposure image in FIG. 15C, the above inequality (B-10) is established, and the blur of the short exposure image is established. It is determined that the degree is relatively large.
Next, the second estimation method will be described. In the second estimation method, the degree of blurring of the short exposure image is estimated based on the amount of positional deviation between the normal exposure image and the short exposure image. This will be described more specifically.
As is well known, when two images are taken at different times, misalignment due to camera shake or the like may occur between the two images. When the second estimation method is adopted, the correction control unit 52 calculates the amount of misalignment between the two images based on the image data of the normal exposure image and the short exposure image, and the size of the misalignment amount is set in advance. Compared with the misregistration threshold. And when the former is larger than the latter, it is estimated that the blurring degree of a short exposure image is comparatively large. In this case, the shake correction process cannot be executed. On the other hand, when the former is smaller than the latter, it is estimated that the blurring degree of the short exposure image is relatively small. In this case, the shake correction process can be executed.
The positional deviation amount is a two-dimensional amount including a horizontal component and a vertical component, and is expressed as a so-called motion vector. Needless to say, the magnitude of the positional deviation amount compared with the positional deviation threshold (in other words, the magnitude of the motion vector) is a one-dimensional quantity. The displacement amount can be calculated using a representative point matching method, a block matching method, or the like.
Focusing on the amount of camera shake that can act on the imaging device 1, the significance of the second estimation method will be supplementarily described. FIG. 16A shows the amount of camera shake when the amount of positional deviation between the normal exposure image and the short exposure image is relatively small. The integrated value of the amount of instantaneous camera shake applied during the exposure period of the normal exposure image is the total amount of camera shake for the normal exposure image, and the integrated value of instantaneous camera shake applied during the exposure period of the short exposure image is The amount of camera shake. If the overall amount of camera shake with respect to the short exposure image increases, the degree of blurring of the short exposure image also increases.
Since it takes a short time to complete the shooting of both images (for example, about 0.1 second), it can be assumed that the amount of camera shake acting from the start of shooting of both images to the completion of shooting is almost constant. Then, the amount of misalignment between the normal exposure image and the short exposure image is an integrated value of the amount of instantaneous camera shake that is applied between the intermediate point of the exposure period of the normal exposure image and the intermediate point of the exposure period of the short exposure image. Approximated. Therefore, as shown in FIG. 16B, when the obtained positional deviation amount is large, the integrated value of the instantaneous camera shake amount that is applied during the exposure period of the short exposure image is also large (that is, the total camera shake amount for the short exposure image is also large). As shown in FIG. 16A, when the obtained positional deviation amount is small, the integrated value of the amount of instantaneous camera shake that has been applied during the exposure period of the short exposure image is also small (that is, the entire short exposure image). The amount of camera shake is small.
[Third estimation method]
Next, a third estimation method will be described. In the third estimation method, the blurring degree of the short exposure image is estimated based on the image degradation function of the normal exposure image estimated using the image data of the normal exposure image and the short exposure image.
The principle of the third estimation method will be described. The observation model of the normal exposure image and the short exposure image can be expressed by the following formulas (C-1) and (C-2).
Here, g 1 and g 2 represent a normal exposure image and a short exposure image obtained by actual shooting, respectively, and h 1 and h 2 respectively represent a normal exposure image and a short exposure image obtained by actual shooting. An image degradation function of the exposure image is represented, and n 1 and n 2 represent observation noise components included in the normal exposure image and the short exposure image obtained by actual photographing, respectively. f I represents an ideal image that is not deteriorated by blur and is not affected by noise. If the normal exposure image and the short exposure image are not blurred and are not affected by noise, g 1 and g 2 are equivalent to f I. Specifically, the image deterioration function is, for example, a point spread function. Moreover, * in Formula (C-1) etc. represents the convolution integral. For example, h 1 * f I represents the convolution integral of h 1 and f I.
Since the image is represented by a two-dimensional matrix, the image degradation function is also represented by a two-dimensional matrix. Due to the characteristics of the image degradation function, in principle, each element when the image degradation function is expressed in a matrix takes a value of 0 or more and 1 or less, and the total value of all the elements is 1.
When it is assumed that there is no deterioration due to blurring of the short-exposure image, h 1 ′ that minimizes the evaluation value J expressed by the following formula (C-3) can be estimated as an image deterioration function of the normal exposure image. h 1 ′ is called an estimated image deterioration function. The evaluation value J is obtained by squaring the norm of (g 1 −h 1 ′ * g 2 ).
At this time, if the short-exposure image does not include blurring, there is an element having a negative value in the estimated image degradation function h 1 ′ due to the influence of the observation noise. The value is small. FIG. 17 shows the pixel value distribution of the normal exposure image as a graph 241, and the pixel value distribution of the short exposure image when the short exposure image does not include blur is shown as a graph 242. A distribution of element values of the estimated image deterioration function h 1 ′ obtained from both images corresponding to the graphs 241 and 242 is shown in a graph 243. In the graphs 241 to 243 and the graphs 244 and 245 described later, the horizontal axis corresponds to the spatial direction. When considering the graphs 241 to 245, it is considered that each image is a one-dimensional image for convenience. Also from the graph 243, it can be seen that the sum of negative values in the estimated image degradation function h 1 ′ is small.
On the other hand, when blurring is included in the short exposure image, the estimated image degradation function h 1 ′ is affected by the image degradation function of the short exposure image, and as shown in the following equation (C-4), the normal exposure image This is close to the convolution integral of the true image degradation function h 1 of the image and the inverse function h 2 −1 of the image degradation function of the short exposure image. When the short exposure image includes blurring, an element having a negative value exists in the inverse function h 2 −1. Therefore, the estimated image is compared with the case where the short exposure image does not include blurring. There will be a relatively large number of elements having a negative value in the deterioration function h 1 ′, and the absolute value of the value will be relatively large. Then, the magnitude of the negative value in the estimated image degradation function h 1 ′ is larger when the short-exposure image includes blurring than when the short-exposure image does not include blurring. .
A graph 244 in FIG. 17 represents a pixel value distribution of the short exposure image when blurring is included in the short exposure image, and a graph 245 is obtained from the normal exposure image and the short exposure image corresponding to the graphs 241 and 244. The distribution of the element values of the estimated image degradation function h 1 ′ is represented.
Based on the above principle, the actual processing is as follows. First, the correction control unit 52 derives an estimated image deterioration function h 1 ′ that minimizes the evaluation value J based on the image data of the normal exposure image and the short exposure image. Any known method can be used as this derivation method. Actually, the first and second evaluation images are extracted from the normal exposure image and the short exposure image using the method described in the description of the first estimation method (see step S51 in FIG. 9), and extracted. The estimated image deterioration function h 1 ′ for minimizing the evaluation value J of the above equation (C-3) is obtained by regarding the first evaluation image and the second evaluation image as g 1 and g 2 , respectively. To derive. As described above, the estimated image degradation function h 1 ′ is expressed as a two-dimensional matrix.
The correction control unit 52 refers to the values of all elements (values of all elements) of the estimated image degradation function h 1 ′ when the matrix is expressed, and extracts a value that deviates from the specified numerical value range among the referenced values. . In this case, the upper limit value in this numerical range is sufficiently larger than 1, and the lower limit value is 0. That is, only values having negative values are extracted from the referenced values. The correction control unit 52 adds up all the negative values extracted here to obtain a combined value, and compares the absolute value of the combined value with a preset threshold value R TH . When the former is larger than the latter (R TH ), it is determined that the degree of blurring of the short exposure image is relatively large. In this case, the shake correction process cannot be executed. On the other hand, if the former is smaller than the latter (R TH ), it is determined that the degree of blurring of the short exposure image is relatively small. In this case, the shake correction process can be executed. Note that the threshold value R TH may be set to, for example, about 0.1 in consideration of the influence of noise.
Next, a fourth embodiment will be described. In the fourth embodiment, a shake correction processing method based on a correction target image and a reference image that can be applied to the first to third embodiments will be described. That is, this method can be used for the blur correction process in step S9 shown in FIGS. It is assumed that the image size of the correction target image and the reference image are the same. In the fourth embodiment, Lw, Rw, and Qw are respectively introduced as symbols representing the entire image of the correction target image, the entire image of the reference image, and the entire image of the shake correction image.
As the blur correction method, first to fourth correction methods will be exemplified below. The first, second, and third correction methods are correction methods using image restoration processing, image synthesis processing, and image sharpening processing, respectively. The fourth correction method is also a correction method using image composition processing, but the content is different from that of the second correction method (details will be apparent from the description given later). In the following description, the term “memory” simply means the internal memory 14 (see FIG. 1).
The first correction method will be described with reference to FIG. FIG. 18 is a flowchart showing the flow of blur correction processing based on the first correction method.
First, in step S71, a characteristic small area is extracted from the correction target image Lw, and an image in the extracted small area is stored in the memory as a small image Ls. For example, a small area of 128 × 128 pixels is extracted as a characteristic small area by using a Harris corner detector. The significance of the characteristic small region is as described above in the second embodiment.
Next, in step S72, a small region corresponding to the small region extracted from the correction target image Lw is extracted from the reference image Rw, and an image in the small region extracted from the reference image Rw is stored in the memory as a small image Rs. Remember. The image sizes of the small image Ls and the small image Rs are equal. When the positional deviation between the correction target image Lw and the reference image Rw can be ignored, the center coordinates (center coordinates in the correction target image Lw) of the small image Ls extracted from the correction target image Lw and the reference image Rw are used. The small area is extracted so that the center coordinates (center coordinates in the reference image Rw) of the extracted small image Rs are equal. If the positional deviation cannot be ignored, a corresponding small region may be searched using a template matching method or the like. That is, for example, the small image Ls is used as a template, and a small region having the highest similarity to the template is searched from the reference image Rw using a known template matching method, and the image in the searched small region is the small image. Let Rs.
Due to the relatively short exposure time of the reference image Rw and the relatively high ISO sensitivity, the SN ratio of the small image Rs is relatively low. Therefore, in step S73, noise removal processing using a median filter or the like is performed on the small image Rs. The small image Rs after the noise removal processing is stored on the memory as a small image Rs ′. Note that this noise removal processing can be omitted.
The small image Ls obtained as described above is treated as a deteriorated image and the small image Rs ′ is treated as an initial restored image (step S74), and then the Fourier iteration method is performed in step S75, whereby the small image Ls is obtained. An image deterioration function representing a state of deterioration due to blurring is obtained.
When performing the Fourier iteration method, it is necessary to give an initial restored image (initial value of the restored image). This initial restored image is called an initial restored image.
A point spread function (hereinafter referred to as PSF) is obtained as an image degradation function. Since camera shake uniformly degrades the entire image, the PSF obtained for the small image Ls can be used as the PSF for the entire correction target image Lw.
The Fourier iteration method is a method for obtaining a restored image from which deterioration is removed or reduced from a deteriorated image including deterioration (see Non-Patent Document 1 above). This Fourier iteration method will be described in detail with reference to FIGS. FIG. 19 is a detailed flowchart of the process in step S75 of FIG. FIG. 20 is a block diagram of a part that implements the Fourier iteration method inherent in the shake correction processing unit 53 of FIG.
First, in step S101, a restored image is set as f ', and an initial restored image is set in the restored image f'. That is, the small image Rs ′ is used as the initial restored image f ′. Next, in step S102, the deteriorated image (that is, the small image Ls) is set to g. Then, the result of Fourier transform of the deteriorated image g is stored in the memory as G (step S103). For example, when the image sizes of the initial restored image and the degraded image are 128 × 128 pixels, f ′ and g can be expressed as a matrix having a matrix size of 128 × 128.
Next, in step S110, F ′ obtained by Fourier transform of the restored image f ′ is obtained, and in step S111, H is calculated by the following equation (D-1). H corresponds to the result of Fourier transform of PSF. In Formula (D-1), F ′ * is a conjugate complex matrix of F ′, and α is a constant.
Next, in step S112, PSF is obtained by performing inverse Fourier transform on H. The PSF obtained here is assumed to be h. Next, in step S113, PSF h is corrected under the constraint condition of the following formula (D-2a), and further corrected under the constraint condition of the formula (D-2b).
Since PSF h is expressed as a two-dimensional matrix, each element of this matrix is represented by h (x, y). Each element of the PSF should originally take a value of 0 or more and 1 or less. Therefore, in step S113, it is determined whether each element of the PSF is 0 or more and 1 or less, and the value of the element that is 0 or more and 1 or less is left as it is. The value of the element is corrected to 1, and if there is an element smaller than 0, the value of the element is corrected to 0. This is correction by the constraint condition of Formula (D-2a). Then, the PSF is normalized so that the sum of the elements of the PSF after correction is 1. This normalization is correction according to the constraint condition of the equation (D-2b).
Let PS ′ be a PSF corrected by the constraints of equations (D-2a) and (D-2b).
Next, in step S114, H ′ obtained by Fourier transforming PSF h ′ is obtained, and in step S115, F is calculated by the following equation (D-3). F corresponds to a Fourier transform of the restored image f. In Expression (D-3), H ′ * is a conjugate complex matrix of H ′, and β is a constant.
Next, in step S116, a restored image is obtained by performing an inverse Fourier transform on F. Let f be the restored image obtained here. Next, in step S117, the restored image f is corrected under the constraint condition of the following equation (D-4), and the corrected restored image is newly set as f '.
Since the restored image f is represented as a two-dimensional matrix, each element of the matrix is represented by f (x, y). Now, it is assumed that the pixel value of each pixel of the degraded image and the restored image is represented by a digital value from 0 to 255. Then, each element (that is, each pixel value) of the matrix representing the restored image f should originally have a value of 0 or more and 255 or less. Accordingly, in step S117, it is determined whether each element of the matrix representing the restored image f is 0 or more and 255 or less, and the value of the element that is 0 or more and 255 or less is left as it is. If there is an element, the value of the element is corrected to 255, and if there is an element smaller than 0, the value of the element is corrected to 0. This is correction by the constraint condition of Formula (D-4).
Next, in step S118, it is determined whether or not the convergence condition is satisfied, thereby determining whether or not the iterative process is converged.
For example, the absolute value of the difference between the latest F ′ and the previous F ′ is used as an index for convergence determination. If this index is less than or equal to a predetermined threshold value, it is determined that the convergence condition is satisfied. Otherwise, it is determined that the convergence condition is not satisfied.
When the convergence condition is satisfied, a final PSF is obtained by performing inverse Fourier transform on the latest H ′. That is, the latest H ′ obtained by inverse Fourier transform is the PSF to be obtained in step S75 of FIG. When the convergence condition is not satisfied, the process returns to step S110, and the processes of steps S110 to S118 are repeated. In the repetition of each process of steps S110 to S118, f ′, F ′, H, h, h ′, H ′, F, and f (see FIG. 20) are sequentially updated to the latest ones.
Another index can be used as an index for determining convergence. For example, the absolute value of the difference between the latest H ′ and the previous H ′ obtained may be used as the convergence determination index to determine whether or not the convergence condition is satisfied. Further, for example, the correction amount in step S113 using the above formulas (D-2a) and (D-2b) or the correction amount in step S117 using formula (D-4) is used as an index for convergence determination. Whether or not the convergence condition is satisfied may be determined. This is because if the iterative process is toward convergence, the amount of correction becomes small.
Further, when the number of repetitions of the loop process consisting of steps S110 to S118 reaches a predetermined number, it may be determined that convergence is impossible and the process may be terminated without calculating a final PSF. In this case, the correction target image Lw is not corrected.
Returning to the description of each step in FIG. After the PSF is calculated in step S75, the process proceeds to step S76. In step S76, each element of the inverse matrix of the PSF obtained in step S75 is obtained as each filter coefficient of the image restoration filter. This image restoration filter is a filter for obtaining a restored image from a deteriorated image. Actually, since each element of the matrix represented by the following equation (D-5) corresponding to a part of the right side of the above equation (D-3) corresponds to each filter coefficient of the image restoration filter, step The intermediate calculation result of the Fourier iteration method in S75 can be used as it is. However, the formula (D-5) H 'in * and H' is, H obtained satisfied immediately before the convergence condition in step S118 '* and H' (i.e., the finally obtained H '* and H' ).
After each filter coefficient of the image restoration filter is obtained in step S76, the process proceeds to step S77, and the entire correction target image Lw is filtered (spatial filtering) using the image restoration filter. In other words, the correction target image Lw is filtered by applying an image restoration filter having the obtained filter coefficients to each pixel of the correction target image Lw. Thereby, a filtered image with reduced blur included in the correction target image Lw is generated. Although the size of the image restoration filter is smaller than the image size of the correction target image Lw, it is considered that camera shake causes uniform degradation on the entire image. Therefore, this image restoration filter is applied to the entire correction target image Lw. Thus, the overall blurring of the correction target image Lw is reduced.
The filtering image may include ringing accompanying filtering. For this reason, in step S78, a final blurring correction image Qw is generated by applying a ringing removal process for removing this to the filtered image. Since the method for removing ringing is known, a detailed description is omitted. For example, a technique described in JP 2006-129236 A may be used.
The blur correction image Qw is an image in which blur included in the correction target image Lw is reduced and ringing due to filtering is reduced. However, since the filtered image is also an image with reduced blur, the filtered image can also be regarded as the shake-corrected image Qw.
Since the amount of blur included in the reference image Rw is small, its edge component is close to the edge component of an ideal image without camera shake. Therefore, as described above, an image obtained from this reference image Rw is used as an initial restored image in the Fourier iteration method.
By repetitive loop processing by the Fourier iteration method, the restored image (f ′) gradually approaches an image from which camera shake has been removed as much as possible. However, since the initial restored image itself is already close to an image without camera shake, Convergence is faster than a random image or a deteriorated image as an initial restored image (in the shortest, convergence is achieved by a single loop process). As a result, the processing time for creating each filter coefficient of the PSF and the image restoration filter necessary for the blur correction process is shortened. If the initial restored image is far from the image to be converged, the probability of convergence to a local solution (an image different from the image to be truly converged) increases. However, by setting the initial restored image as described above, , The probability of convergence to a local solution is low (that is, the probability that camera shake correction fails) is low.
In addition, since camera shake is considered to cause uniform degradation on the entire image, a small area is extracted from each image, and PSF and filter coefficients of an image restoration filter are created from the image data of each small area. Applies to As a result, the amount of calculation required is reduced, and the processing time for creating the filter coefficients of the PSF and the image restoration filter and the processing time for camera shake correction are shortened. Of course, the required circuit scale can be reduced and the cost reduction effect can be expected.
At this time, as described above, a characteristic small region containing a lot of edge components is automatically extracted. An increase in the edge component in the PSF calculation source image means an increase in the ratio of the signal component to the noise component. Therefore, the influence of noise is reduced by extracting a characteristic small region, and the PSF can be detected more accurately. become able to.
In the process of FIG. 19, the degraded image g and the restored image f ′ on the spatial domain are transformed onto the frequency domain by Fourier transform, so that the function G representing the degraded image g on the frequency domain and on the frequency domain are converted. A function F ′ representing the restored image f ′ is obtained (note that the frequency domain is, of course, a two-dimensional frequency domain). A function H representing PSF in the frequency domain is obtained from the obtained functions G and F ′, and this function H is converted into a function in the spatial domain, that is, PSF h by inverse Fourier transform. The PSF h is corrected using a predetermined constraint condition, and a corrected PSF h ′ is obtained. The process of correcting the PSF is hereinafter referred to as “first correction process”.
PSF h ′ is again transformed onto the frequency domain by Fourier transform to obtain a function H ′, and a function F representing a restored image on the frequency domain is obtained from the functions H ′ and G. By performing inverse Fourier transform on this function F, a restored image f in the spatial domain is obtained, and this restored image f is corrected using a predetermined constraint condition, and a corrected restored image f ′ is obtained. The processing for correcting the restored image is hereinafter referred to as “second correction processing”.
In the above-described example, it has been described that, after that, the above-described processing is repeated using the corrected restored image f ′ until the convergence condition is satisfied in step S <b> 118 of FIG. 19. Further, considering the characteristic that the correction amount becomes smaller when the iterative process is toward convergence, the establishment / non-establishment of the convergence condition is determined by the correction amount in step S113 corresponding to the first correction process or the second correction. It has also been stated that the determination may be made based on the correction amount in step S117 corresponding to the processing. When this determination is made based on the correction amount, a reference correction amount is set in advance, and the correction amount in step S113 or the correction amount in step S117 is compared with the reference correction amount, and the former is smaller than the latter. In this case, it is determined that the convergence condition is satisfied. However, if the reference correction amount is set sufficiently large, the processes in steps S110 to S117 are not repeatedly executed. That is, in this case, PSF h ′ obtained by performing the first correction process only once is the final PSF to be derived in step S75 in FIG. As described above, even if the process of FIG. 19 is used, the first and second correction processes are not necessarily repeatedly executed.
The increase in the number of repeated executions of the first and second correction processes contributes to improving the accuracy of the finally obtained PSF. However, in this example, since the initial restoration image itself is close to an image without camera shake, the first correction is performed. The accuracy of PSF h ′ obtained by performing the treatment only once is also high enough to cause no problem. In consideration of this, the determination process itself in step S118 can be omitted. In this case, the PSF h ′ obtained by executing the process of step S113 only once becomes the final PSF to be derived in step S75 of FIG. 18, and the function obtained by executing the process of step S114 only once. From H ′, each filter coefficient of the image restoration filter to be derived in step S76 in FIG. 18 is obtained. Therefore, when the process of step S118 is omitted, the processes of steps S115 to S117 are also omitted.
Next, the second correction method will be described with reference to FIGS. FIG. 21 is a flowchart showing the flow of blur correction processing based on the second correction method. FIG. 22 is a conceptual diagram showing the flow of this blur correction process.
An image obtained by photographing by the imaging unit 11 is a color image including information on luminance and information on color. Accordingly, the pixel signal of each pixel that forms the correction target image Lw is formed from a luminance signal that represents the luminance of the pixel and a color signal that represents the color of the pixel. Now, it is assumed that the pixel signal of each pixel is expressed in YUV format. In this case, the color signal is formed from the two color difference signals U and V. The pixel signal of each pixel forming the correction target image Lw is formed from a luminance signal Y that represents the luminance of the pixel and two color difference signals U and V that represent the color of the pixel.
Then, as shown in FIG. 22, the correction target image Lw includes an image Lw Y including only the luminance signal Y as the pixel signal, an image Lw U including only the color difference signal U as the pixel signal, and only the color difference signal V as the pixel signal. an image Lw V containing, can be decomposed into. Similarly, the reference image Rw includes an image Rw Y including only the luminance signal Y as a pixel signal, an image Rw U including only the color difference signal U as a pixel signal, an image Rw V including only the color difference signal V as a pixel signal, (In FIG. 22, only the image Rw Y is shown).
In step S201 in FIG. 21, first, the images Lw Y , Lw U and Lw V are generated by extracting the luminance signal and the color difference signal of the correction target image Lw. In step S202, by extracting a brightness signal of the reference image Rw, and generates an image Rw Y.
Due to the relatively short exposure time of the reference image Rw and the relatively high ISO sensitivity, the SN ratio of the image Rw Y is relatively low. Therefore, in step S203, noise removal processing using a median filter or the like is performed on the image Rw Y. The image Rw Y after the noise removal processing is stored on the memory as an image Rw Y '. Note that this noise removal processing can be omitted.
After that, in step S204, a positional deviation amount ΔD between the image Lw Y and the image Rw Y ′ is calculated by comparing the pixel signal of the image Lw Y and the pixel signal of the image Rw Y ′. The positional deviation amount ΔD is a two-dimensional amount including a horizontal component and a vertical component, and is expressed as a so-called motion vector. The positional deviation amount ΔD can be calculated using a well-known representative point matching method or template matching method. For example, an image in a small area extracted from the image Lw Y is used as a template, and a small area having the highest similarity with the template is searched from the image Rw Y ′ using a template matching method. Then, to calculate the amount of deviation between the position of the searched small regions (image Rw Y 'on the position) and the position of the small area extracted from the image Lw Y (position on the image Lw Y) as the positional deviation amount [Delta] D. Note that the small area to be extracted from the image Lw Y is desirably a characteristic small area as described above.
Considering the image Lw Y as a reference, it is assumed that the positional deviation amount ΔD is the positional deviation amount of the image Rw Y ′ with respect to the image Lw Y. The image Rw Y ′ can be regarded as an image in which a positional deviation has occurred by an amount corresponding to the positional deviation amount ΔD with the image Lw Y as a reference. Therefore, in step S205, the positional deviation correcting 'image Rw Y by performing coordinate conversion (such as affine transformation) in the' image Rw Y as the positional deviation amount ΔD is canceled. The pixel located at the coordinates (x + ΔDx, y + ΔDy) in the image Rw Y ′ before the positional deviation correction is converted into a pixel located at the coordinates (x, y) by the positional deviation correction. ΔDx and ΔDy are a horizontal component and a vertical component of ΔD, respectively.
In step S205, the images Lw U and Lw V and the image Rw Y ′ after the positional deviation correction are combined, and the image obtained by the combination is output as a shake correction image Qw. The pixel signal of the pixel located at the coordinate (x, y) in the shake correction image Qw is located at the coordinate (x, y) with the pixel signal of the pixel in the image Lw U located at the coordinate (x, y). It is formed from the pixel signal of the pixel in the image Lw V and the pixel signal of the pixel in the image Rw Y ′ after displacement correction located at the coordinates (x, y).
In color images, visual blur is mainly caused by luminance blur, and if the edge component of luminance is close to that of an ideal image without blur, the viewer feels that there is little blur. Therefore, in this correction method, a pseudo camera shake correction effect is obtained by combining the luminance signal of the reference image Rw with a relatively small amount of blur with the color signal of the correction target image Lw. According to this method, although color misregistration occurs in the vicinity of the edge, an image with less blurring in appearance can be generated at a low calculation cost.
Next, the third correction method will be described with reference to FIGS. FIG. 23 is a flowchart showing the flow of blur correction processing based on the third correction method. FIG. 24 is a conceptual diagram showing the flow of this blur correction process.
First, in step S221, a small image Ls is generated by extracting a characteristic small region from the correction target image Lw, and in step S222, a small region corresponding to the small image Ls is extracted from the reference image Rw. An image Rs is generated. The processes in steps S221 and S222 are the same as the processes in steps S71 and S72 in FIG. In step S223, the small image Rs is subjected to noise removal processing using a median filter or the like. The small image Rs subjected to noise removal processing is stored on the memory as a small image Rs ′. Note that this noise removal processing can be omitted.
Next, in step S224, by filtering the small image Rs ′ using eight different smoothing filters, eight smoothed small images Rs G1 , Rs G2 ,. Generate G8 . Now, eight different Gaussian filters are used as the eight smoothing filters, and the variance of the Gaussian distribution represented by each Gaussian filter is represented by σ 2 .
When attention is paid to a one-dimensional image and the pixel position in the one-dimensional image is represented by x, a Gaussian distribution with an average of 0 and a variance of σ 2 is generally represented by the following equation (E-1). (See FIG. 25). When this Gaussian distribution is applied to a Gaussian filter, each filter coefficient of the Gaussian filter is represented by h g (x). That is, when the Gaussian filter is applied to the pixel at position 0, the filter coefficient at position x is represented by h g (x). In other words, the contribution ratio of the pixel value at the position x before filtering to the pixel value at the position 0 after filtering by the Gaussian filter is represented by h g (x).
When this idea is extended to two dimensions and the pixel position in the two-dimensional image is represented by (x, y), the two-dimensional Gaussian distribution is represented by the following equation (E-2). Note that x and y represent a horizontal position and a vertical position, respectively. When this two-dimensional Gaussian distribution is applied to the Gaussian filter, each filter coefficient of the Gaussian filter is represented by h g (x, y). When the Gaussian filter is applied to the pixel at the position (0, 0), the position (x, The filter coefficient in y) is represented by h g (x, y). That is, the contribution ratio of the pixel value at the position (x, y) before filtering to the pixel value at the position (0, 0) after filtering by the Gaussian filter is represented by h g (x, y).
In step S224, Gaussian filters with σ = 1, 3, 5, 7, 9, 11, 13, 15 are used as the eight Gaussian filters. In the subsequent step S225, image matching is performed between the small image Ls and each of the smoothed small images Rs G1 to Rs G8 , and among the smoothed small images Rs G1 to Rs G8 , the smoothing small that minimizes the matching error. An image (that is, a smoothed small image having the highest correlation with the small image Ls) is specified.
Focusing on the smoothed small image Rs G1, briefly explaining the calculation method of the small image Ls and the smoothed small image Rs G1 matching error when the comparison (matching residuals). The image sizes of the small image Ls and the smoothed small image Rs G1 are the same, and the number of pixels in the horizontal direction and the number of pixels in the vertical direction are respectively M N and N N (M N and N N are 2 or more) Integer). The pixel value of the pixel at the position (x, y) in the small image Ls is represented by V Ls (x, y), and the pixel value of the pixel at the position (x, y) in the smoothed small image Rs G1 is represented by V Rs. It is represented by (x, y). (Where x and y are integers satisfying 0 ≦ x ≦ M N −1 and 0 ≦ y ≦ N N −1). Then, R SAD representing SAD (Sum of Absolute Difference) between contrast images is calculated according to the following equation (E-3), and R SSD representing SSD (Sum of Square Difference) between contrast images is represented by the following equation (E− Calculated according to 4).
Let R SAD or R SSD be a matching error between the small image Ls and the smoothed small image Rs G1 . Similarly, a matching error between the small image Ls and each of the smoothed small images Rs G2 to Rs G8 is also obtained, and the smoothed small image with the smallest matching error is specified. Now, it is assumed that the smoothed small image Rs G3 corresponding to σ = 5 is specified. In step S225, σ corresponding to the smoothed small image Rs G3 is set to σ ′. That is, the value of σ ′ is 5.
In the subsequent step S226, the Gaussian blur represented by σ ′ is treated as an image deterioration function representing the deterioration state of the correction target image Lw, thereby deteriorating the correction target image Lw.
Specifically, in step S226, blurring of the correction target image Lw is removed by applying an unsharp mask filter to the entire correction target image Lw based on σ ′. Processing contents of the unsharp mask filter will be described with an image before application of the unsharp mask filter as an input image I INPUT and an image after application of the unsharp mask filter as an output image I OUTPUT . First, a σ ′ Gaussian filter (that is, a σ = 5 Gaussian filter) is used as an unsharp filter, and the input image I INPUT is filtered using this σ ′ Gaussian filter to generate a blurred image I BLUR . To do. Then, by subtracting each pixel value of the blurred image I BLUR from each pixel value of the input image I INPUT, and generates a difference image I DELTA between the input image I INPUT and the blurred image I BLUR. Finally, an image obtained by adding each pixel value of the difference image I DELTA to each pixel value of the input image I INPUT is defined as an output image I OUTPUT . Expression (E-5) shows a relational expression between the input image I INPUT and the output image I OUTPUT . In Expression (E-5), (I INPUT · Gauss) represents the result of filtering the input image I INPUT using a Gaussian filter of σ ′.
I OUTPUT = I INPUT + I DELTA
= I INPUT + (I INPUT -I BLUR )
= I INPUT + (I INPUT- (I INPUT · Gauss)) (E-5)
In step S226, the correction target image Lw is handled as the input image I INPUT to obtain a filtered image as the output image I OUTPUT . In step S227, the ringing of the filtered image is removed to generate a shake correction image Qw (the process in step S227 is the same as the process in step S78 in FIG. 18).
By using an unsharp mask filter, the edge of the input image (I INPUT ) is enhanced, and an image sharpening effect is obtained. However, if the blurring degree at the time of generating the blurred image (I BLUR ) is significantly different from the actual blur amount included in the input image, an appropriate blur correction effect cannot be obtained. For example, if the degree of blur at the time of blur image generation is larger than the actual blur amount, the output image (I OUTPUT ) becomes an extremely sharp and unnatural image. On the other hand, if the blurring degree at the time of generating the blurred image is smaller than the actual blur amount, the sharpening effect is too weak. In this correction method, a Gaussian filter whose degree of blurring is defined by σ is used as the unsharp filter, and σ ′ corresponding to the image degradation function is used as σ of the Gaussian filter. For this reason, an optimal sharpening effect is obtained, and a blur-corrected image from which blur is well removed is obtained. That is, it is possible to generate an image with less blur in appearance at a low calculation cost.
In FIG. 26, together with the hand shake image 300 as the input image I INPUT , an image (that is, an original blur correction image) 302 obtained when an optimal σ Gaussian filter is used, and a σ Gaussian filter that is too small are used. An image 301 obtained in this case and an image 303 obtained when a Gaussian filter with too large σ is used are shown. It can be seen that if σ is too small, the sharpening effect is weak, and if σ is too large, an extremely sharp unnatural image is generated.
[Fourth Correction Method]
Next, a fourth correction method will be described. FIGS. 27A and 27B show examples of the reference image Rw and the correction target image Lw taken up in the description of the fourth correction method. Images 310 and 311 in FIGS. 27A and 27B are examples of the reference image Rw and the correction target image Lw, respectively. The reference image 310 and the correction target image 311 are obtained by photographing a state where the person SUB as the subject of interest stands in front of the mountain that is the background subject.
Since the reference image is an image based on the short-exposure image, it contains a relatively large amount of noise. Therefore, compared to the correction target image 311, the reference image 310 has a relatively large noise (corresponding to black spots in FIG. 27A) although the edge is clearly depicted. On the other hand, compared to the reference image 310, the correction target image 311 contains less noise, but the person SUB is greatly blurred on the correction target image 311. In FIGS. 27A and 27B, it is assumed that the person SUB is moving during the photographing of the reference image 310 and the correction target image 311, thereby comparing with the position of the person SUB on the reference image 310. Thus, the person SUB on the correction target image 311 is located on the right side, and the person SUB on the correction target image 311 has subject blurring.
Also, as shown in FIG. 28, a two-dimensional coordinate system XY of a spatial domain in which an arbitrary two-dimensional image 320 is arranged is defined. The image 320 is, for example, a correction target image, a reference image, a shake correction image, or first to third intermediate generation images described later. The X axis and the Y axis are axes along the horizontal direction and the vertical direction of the two-dimensional image 320. The two-dimensional image 320 is formed by arranging a plurality of pixels in a matrix in each of the horizontal direction and the vertical direction, and the position of a pixel 321 that is any pixel on the two-dimensional image 320 is (x, y). ). X and y in (x, y) are coordinate values of the pixel 321 in the X-axis and Y-axis directions, respectively. In the two-dimensional coordinate system XY, when the position of a certain pixel is shifted to the right by one pixel, the coordinate value of the pixel in the X-axis direction is increased by 1, and when the position of a certain pixel is shifted upward by one pixel, the pixel The coordinate value in the Y-axis direction increases by 1. Therefore, when the position of the pixel 321 is (x, y), the positions of the pixels adjacent to the right side, the left side, the upper side, and the lower side of the pixel 321 are (x + 1, y) and (x-1, y), respectively. , (X, y + 1) and (x, y-1).
FIG. 29 is an internal block diagram of the image composition unit 150 included in the shake correction processing unit 53 of FIG. 3 when the fourth correction method is employed. Image data of the reference image Rw and the correction target image Lw is input to the image composition unit 150. The image data represents the color and brightness of the image.
The image synthesizing unit 150 detects a misalignment between the reference image and the correction target image and aligns the images, and a noise reducing unit 152 that reduces noise included in the reference image. The difference value calculation unit 153 that calculates a difference value at each pixel position by calculating the difference between the correction target image after the alignment and the reference image after the noise reduction, and the alignment with the combination ratio based on the difference value A first synthesizing unit 154 that synthesizes a later correction target image and a noise-reduced reference image, an edge strength value calculating unit 155 that extracts an edge from the noise-reduced reference image and calculates an edge strength value, and edge strength A second synthesizing unit 156 that generates a shake correction image by synthesizing the reference image and the synthesized image by the first synthesizing unit 154 at a synthesis ratio based on the value.
The operation of each part in the image composition unit 150 will be described in detail. Note that when simply referred to as a reference image, it refers to a reference image Rw that has not been subjected to noise reduction processing by the noise reduction unit 152. The reference image 310 illustrated in FIG. 27A is a reference image Rw that has not been subjected to noise reduction by the noise reduction unit 152.
The alignment unit 151 detects a misalignment between the reference image and the correction target image based on the image data of the reference image and the correction target image, and makes reference so that the misalignment between the reference image and the correction target image is canceled out. Alignment between the image and the correction target image is performed. The misalignment detection method and the alignment method by the alignment unit 151 can be realized using a representative point matching method, a block matching method, a gradient method, or the like. Typically, for example, the alignment method described in the second embodiment can be used. At this time, the reference image is used as a standard image and the correction target image is handled as a non-standard image for alignment. Therefore, the correction target image is subjected to processing for correcting the positional deviation of the correction target image viewed from the reference image. The correction target image after the misalignment correction (in other words, the correction target image after alignment) is referred to as a first intermediate generation image.
The noise reduction unit 152 reduces noise included in the reference image by performing noise reduction processing on the reference image. The noise reduction processing in the noise reduction unit 152 can be realized by arbitrary spatial filtering suitable for noise reduction. In the spatial filtering in the noise reduction unit 152, it is desirable to use a spatial filter that preserves as many edges as possible. For example, spatial filtering using a median filter is employed.
Moreover, the noise reduction process in the noise reduction part 152 can also be implement | achieved by the arbitrary frequency filtering suitable for noise reduction. When frequency filtering is used in the noise reduction unit 152, a low pass that allows a spatial frequency component less than a predetermined cutoff frequency to pass through and reduces a spatial frequency component equal to or higher than the cutoff frequency among the spatial frequency components included in the reference image. A filter may be used. Note that spatial filtering using a median filter or the like also reduces the spatial frequency component of a relatively high frequency while the spatial frequency component of the spatial frequency component included in the reference image remains almost as it is. Is done. Therefore, it can be considered that spatial filtering using a median filter or the like is a kind of filtering using a low-pass filter.
The reference image after the noise reduction processing by the noise reduction unit 152 is referred to as a second intermediate generation image (third image). FIG. 30 shows a second intermediate generation image 312 obtained by performing noise reduction processing on the reference image 310 of FIG. As can be seen from the comparison between FIG. 27A and FIG. 30, the noise included in the reference image 310 is reduced in the second intermediate generation image 312, but the edges are slightly smaller than in the reference image 310. It is unclear.
The difference value calculation unit 153 calculates a difference value at each pixel position between the first intermediate generation image and the second intermediate generation image. The difference value at the pixel position (x, y) is represented by DIF (x, y). The difference value DIF (x, y) is the luminance and / or between the pixel at the pixel position (x, y) of the first intermediate generation image and the pixel at the pixel position (x, y) of the second intermediate generation image. It is a value representing a color difference.
The difference value calculation unit 153 calculates the difference value DIF (x, y) based on, for example, the following formula (F-1). Here, P1 Y (x, y) is the luminance value of the pixel at the pixel position (x, y) of the first intermediate generation image, and P2 Y (x, y) is the pixel position of the second intermediate generation image. This is the luminance value of the pixel at (x, y).
It is also possible to calculate the difference value DIF (x, y) based on the following formula (F-2) or formula (F-3) using signal values in RGB format instead of formula (F-1). is there. Here, P1 R (x, y), P1 G (x, y), and P1 B (x, y) are respectively R, G, and R of the pixel at the pixel position (x, y) of the first intermediate generation image. The value of the B signal, P2 R (x, y), P2 G (x, y), and P2 B (x, y) are the pixel values at the pixel position (x, y) of the second intermediate generation image, respectively. R, G and B signal values. The R, G, and B signals of a certain pixel are color signals that represent the red, green, and blue intensities of that pixel.
The calculation method of the difference value DIF (x, y) based on the above formula (F-1), formula (F-2), or formula (F-3) is merely an example, and the difference value DIF ( x, y) may be obtained. For example, the difference value DIF (x, y) may be calculated by using the YUV signal value and using the same method as that when the RGB signal value is used. In this case, R, G, and B in the formulas (F-2) and (F-3) may be replaced with Y, U, and V, respectively. The YUV format signal is composed of a luminance signal represented by Y and a color difference signal represented by U and V.
FIG. 31 shows an example of a difference image having a difference value DIF (x, y) at each pixel position as a pixel signal value. A difference image 313 in FIG. 31 is a difference image based on the reference image 310 and the correction target image 311 in FIGS. In the difference image 313, a portion having a relatively large difference value DIF (x, y) is represented in white, and a portion having a relatively small difference value DIF (x, y) is represented in black. Due to the movement of the person SUB while the reference image 310 and the correction target image 311 are captured, the difference value DIF (x, y) in the movement area of the person SUB in the difference image 313 is relatively large. Further, the difference value DIF (x, y) in the vicinity of the edge (the contour portion of the person or the mountain) is also increased due to the shake on the correction target image 311 caused by the camera shake.
The first combining unit 154 combines the first intermediate generation image and the second intermediate generation image, and outputs the obtained composite image as a third intermediate generation image (fourth image). This synthesis is realized by weighted addition of pixel signals of corresponding pixels of the first and second intermediate generation images. The pixel signals of the corresponding pixels are mixed by weighted addition, and the mixing ratio (in other words, the combination ratio) can be determined based on the difference value DIF (x, y). The mixing ratio for the pixel position (x, y) determined by the first combining unit 154 is represented by α (x, y).
An example of the relationship between the difference value DIF (x, y) and the mixing ratio α (x, y) is shown in FIG. When adopting the relationship example of FIG.
When “DIF (x, y) <Th1_L” is satisfied, “α (x, y) = 1” is set.
When “Th1_L ≦ DIF (x, y) <Th1_H” is satisfied, “α (x, y) = 1− (DIF (x, y) −Th1_L) / (Th1_H−Th1_L)”.
When “Th1_H ≦ DIF (x, y)” is satisfied, “α (x, y) = 0” is set.
Here, Th1_L and Th1_H are predetermined thresholds satisfying “0 <Th1_L <Th1_H”. 32, when the difference value DIF (x, y) increases from the threshold Th1_L toward the threshold Th1_H, the corresponding mixing ratio α (x, y) increases linearly from 1 to 0. Although it decreases, the mixing rate α (x, y) may be decreased nonlinearly.
The first combining unit 154 determines the mixing rate α (x, y) at each pixel position from the difference value DIF (x, y) at each pixel position, and then performs the first and second according to the following formula (F-4). A pixel signal of the third intermediate generation image is generated by mixing pixel signals of corresponding pixels of the intermediate generation image.
P1 (x, y), P2 (x, y), and P3 (x, y) respectively indicate the luminance and color of the pixel at the pixel position (x, y) of the first, second, and third intermediate generation images. For example, the pixel signal is expressed in RGB format or YUV format. For example, when the pixel signal P1 (x, y) is composed of R, G, and B signals, the pixel signals P1 (x, y) and P2 (x, y) are mixed individually for each R, G, and B signal. Thus, the pixel signal P3 (x, y) may be obtained. The same applies when the pixel signal P1 (x, y) or the like is composed of Y, U, and V signals.
FIG. 33 shows an example of a third intermediate generation image obtained by the first synthesis unit 154. A third intermediate generation image 314 illustrated in FIG. 32 is a third intermediate generation image based on the reference image 310 and the correction target image 311 illustrated in FIGS.
As described above, since the difference value D (x, y) is relatively large in the region where the person SUB moves, the contribution degree (1) of the second intermediate generation image 312 (see FIG. 30) to the third intermediate generation image 314 -Α (x, y)) becomes relatively large. As a result, the subject blur in the third intermediate generation image 314 is significantly suppressed as compared with that in the correction target image 311 (see FIG. 27B). Further, since the difference value D (x, y) becomes large near the edge, the contribution (1-α (x, y)) becomes large. As a result, the sharpness of the edge in the third intermediate generation image 314 is improved more than that in the correction target image 311. However, since the edge in the second intermediate generation image 312 is slightly unclear compared to that of the reference image 310, the edge in the third intermediate generation image 314 is also slightly unclear compared to that of the reference image 310.
On the other hand, the region where the difference value D (x, y) is relatively small is presumed to be a flat region with few edge components. Therefore, for the region where the difference value D (x, y) is relatively small, as described above, the contribution degree α (x, y) of the first intermediate generation image with a small amount of noise is made relatively large. . Thereby, the noise of a 3rd intermediate generation image can be suppressed low. Since the second intermediate generation image is generated through the noise reduction process, the contribution degree (1-α (x, y)) of the second intermediate generation image to the third intermediate generation image is relatively large. Even in the area, the noise is hardly noticeable.
As described above, the edge in the third intermediate generation image is slightly blurred compared with that of the reference image, but this blur is improved by the edge intensity value calculation unit 155 and the second synthesis unit 156.
The edge strength value calculation unit 155 performs edge extraction processing on the second intermediate generation image, and calculates an edge strength value at each pixel position. The edge intensity value at the pixel position (x, y) is represented by E (x, y). The edge intensity value E (x, y) is an index representing the change amount of the pixel signal in the small block centered on the pixel position (x, y) of the second intermediate generation image, and the larger the change amount, the more the edge The intensity value E (x, y) increases.
For example, the edge strength value E (x, y) is obtained according to the following formula (F-5). As described above, P2 Y (x, y) represents the luminance value of the pixel at the pixel position (x, y) of the second intermediate generation image. Fx (i, j) and Fy (i, j) represent filter coefficients of an edge extraction filter for extracting horizontal and vertical edges, respectively. Any spatial filter suitable for edge extraction can be used as the edge extraction filter. For example, a Prewitt filter, a Sobel filter, a differential filter, or a Laplacian filter is used. Can do.
For example, when a pre-wit filter is used, Fx (−1, −1) = Fx (−1,0) = Fx (−1,1) = in Fx (i, j) in Expression (F-5) = −1 ”,“ Fx (0, −1) = Fx (0,0) = Fx (0,1) = 0 ”and“ Fx (1, −1) = Fx (1,0) = Fx (1, 1) = 1 ”is substituted, and“ Fy (−1, −1) = Fy (0, −1) = Fy (1, −1 ”is substituted for Fy (i, j) in the formula (F-5). ) = − 1 ”,“ Fy (−1,0) = Fy (0,0) = Fy (1,0) = 0 ”and“ Fy (−1,1) = Fy (0,1) = Fy ( 1, 1) = 1 ”may be substituted. Of course, such a filter coefficient is an example, and the edge extraction filter for calculating the edge strength value E (x, y) can be variously modified. Further, in the expression (F-5), an edge extraction filter having a filter size of 3 × 3 is used, but the filter size of the edge extraction filter may be other than 3 × 3.
FIG. 34 shows an example of an edge image having an edge intensity value E (x, y) at each pixel position as a pixel signal value. An edge image 315 in FIG. 34 is an edge image based on the reference image 310 and the correction target image 311 in FIGS. In the edge image 315, a portion where the edge intensity value E (x, y) is relatively large is represented in white, and a portion where the edge intensity value E (x, y) is relatively small is represented in black. The edge intensity value E (x, y) is obtained by extracting the edge of the second intermediate generation image 312 obtained by suppressing the noise of the reference image 310 having a clear edge. Therefore, the noise and the edge are separated, and the position of the edge is specified after clearly distinguishing the noise from the edge of the subject by the edge intensity value E (x, y).
The second synthesizing unit 156 synthesizes the third intermediate generated image and the reference image, and outputs the obtained synthesized image as a shake correction image (Qw). This synthesis is realized by weighted addition of pixel signals of corresponding pixels of the third intermediate generation image and the reference image. The pixel signals of the corresponding pixels are mixed by weighted addition, and the mixing ratio (in other words, the combination ratio) can be determined based on the edge intensity value E (x, y). The mixing ratio for the pixel position (x, y) determined by the second combining unit 156 is represented by β (x, y).
An example of the relationship between the edge strength value E (x, y) and the mixing ratio β (x, y) is shown in FIG. When adopting the relationship example of FIG.
When “E (x, y) <Th2_L” is satisfied, “β (x, y) = 0” is set.
When “Th2_L ≦ E (x, y) <Th2_H” is satisfied, “β (x, y) = (E (x, y) −Th2_L) / (Th2_H−Th2_L)” is established.
When “Th2_H ≦ E (x, y)” is satisfied, “β (x, y) = 1” is set.
Here, Th2_L and Th2_H are predetermined thresholds satisfying “0 <Th2_L <Th2_H”. 35, when the edge intensity value E (x, y) increases from the threshold Th2_L toward the threshold Th2_H, the corresponding mixing ratio β (x, y) is linear from 0 to 1. However, the mixing ratio β (x, y) may be increased nonlinearly.
The second synthesis unit 156 determines the mixing rate β (x, y) at each pixel position from the edge intensity value E (x, y) at each pixel position, and then performs the third intermediate generation according to the following formula (F-6). By mixing the pixel signals of the corresponding pixels of the image and the reference image, the pixel signal of the blur correction image is generated.
P OUT (x, y), P IN — SH (x, y), and P3 (x, y) are the luminances of the pixels at the pixel position (x, y) of the shake correction image, the reference image, and the third intermediate generation image, respectively. And a pixel signal representing a color, for example, expressed in RGB format or YUV format. For example, when the pixel signal P3 (x, y) or the like is composed of R, G, and B signals, the pixel signals P IN — SH (x, y) and P3 (x, y) are individually set for each of the R, G, and B signals. The pixel signal P OUT (x, y) may be obtained by mixing. The same applies when the pixel signal P3 (x, y) or the like is composed of Y, U, and V signals.
FIG. 36 shows a shake correction image 316 as an example of the shake correction image Qw obtained by the second synthesis unit 156. The shake correction image 316 is a shake correction image based on the reference image 310 and the correction target image 311 in FIGS. In the edge portion, the contribution β (x, y) of the reference image 310 to the shake correction image 316 increases, and therefore, in the shake correction image 316, the edge in the third intermediate generation image 314 (see FIG. 33) is slightly blurred. And the edges are clearly depicted. On the other hand, in the part other than the edge, the contribution degree (1-β (x, y)) of the third intermediate generation image 314 with respect to the shake correction image 316 is increased, so that the blur correction image 316 is included in the reference image 310. The reflected noise is suppressed. Since noise is particularly visually noticeable in a portion other than the edge (flat portion), the adjustment of the synthesis ratio by the mixing rate β (x, y) as described above is effective.
Thus, according to the fourth correction method, the correction target image (more specifically, the correction target image after alignment (ie, the first intermediate generation image)) and the noise-reduced reference image (ie, second intermediate generation). The third intermediate generation image in which the blur of the correction target image and the noise of the reference image are suppressed can be generated. Thereafter, the third intermediate generation image and the reference image are synthesized using the edge intensity value obtained from the reference image after noise reduction (that is, the second intermediate generation image), so that a sharp edge of the reference image is added to the shake correction image. While it can be reflected, the reflection of the noise of the reference image on the blur correction image is suppressed. As a result, the blur correction image is an image with less blur and noise.
In order to clearly separate and detect the edge and noise and to avoid the mixing of the noise of the reference image into the blur-corrected image, the reference image (that is, the second intermediate generation image) after reducing the edge intensity value as described above is used. However, the edge intensity value may be derived from a reference image before noise reduction (that is, the reference image 310 in FIG. 27A, etc.). In this case, after substituting the luminance value of the pixel at the pixel position (x, y) of the reference image before noise reduction into P2 Y (x, y) in the equation (F-5), the equation (F-5) is followed. The edge intensity value E (x, y) may be calculated.
The specific numerical values shown in the above description are merely examples, and as a matter of course, they can be changed to various numerical values. As modifications or annotations of the above-described embodiment, notes 1 and 2 are described below. The contents described in each comment can be arbitrarily combined as long as there is no contradiction.
The imaging apparatus 1 in FIG. 1 can be realized by hardware or a combination of hardware and software. In particular, all or part of the functions of the respective parts shown in FIGS. 3 and 29 can be realized by hardware, software, or a combination of hardware and software. When the imaging apparatus 1 is configured using software, a block diagram of a part realized by software represents a functional block diagram of the part.
Also, all or part of the arithmetic processing executed in each part shown in FIG. 3 and FIG. 29 is described as a program, and the arithmetic is executed by executing the program on a program execution device (for example, a computer). You may make it implement | achieve all or one part of a process.
For example, it can be considered as follows. In the first or second embodiment, the part including the imaging control unit 51 and the correction control unit 52 in FIG. 3 functions as a control unit that controls whether or not the blur correction process can be performed or the number of captured short exposure images. In the third embodiment, the control means for controlling whether or not to perform the shake correction process includes a correction control unit 52 and may further include a photographing control unit 51. The correction control unit 52 in the third embodiment includes blur estimation means for estimating the blur degree of the short exposure image. When the first correction method shown in the fourth embodiment is used as a shake correction processing method, the shake correction processing unit 53 in FIG. 3 uses the image deterioration function (specifically, PSF) of the correction target image. Image deriving function deriving means to be obtained is included.
1 is an overall block diagram of an imaging apparatus according to an embodiment of the present invention. It is an internal block diagram of the imaging part of FIG. It is an internal block diagram of the main control part of FIG. 5 is an operation flowchart of photographing and correction of the imaging apparatus according to the first embodiment of the present invention. 6 is an operation flowchart for determining whether or not to shoot a short-exposure image and setting shooting parameters according to the first embodiment of the present invention. It is a graph showing the relationship between a focal distance and camera shake limit exposure time. It is an operation | movement flowchart of imaging | photography and correction | amendment of the imaging device which concerns on 2nd Embodiment of this invention. It is an operation | movement flowchart of imaging | photography and correction | amendment of the imaging device which concerns on 3rd Embodiment of this invention. It is an operation | movement flowchart of the blurring degree estimation of a short exposure image based on 3rd Embodiment of this invention. It is a figure which concerns on 3rd Embodiment of this invention and shows the pixel arrangement | sequence of the evaluation image extracted from the normal exposure image or the short exposure image. It is a figure which shows the arrangement | sequence of the luminance value in the evaluation image of FIG. It is a figure which concerns on 3rd Embodiment of this invention and shows the secondary differential filter of the horizontal direction which can be used for calculation of an edge strength value. It is a figure which concerns on 3rd Embodiment of this invention and shows the secondary differential filter of the perpendicular direction which can be used for calculation of an edge strength value. The figure (a) which shows the luminance value distribution of the image which has influence of the image which has no influence of noise and the noise concerning 3rd Embodiment of this invention, and the edge of the image which has no influence of the noise and the influence of the noise It is a figure (b) which shows intensity value distribution. According to the third embodiment of the present invention, a normal exposure image (a) having a blur in the horizontal direction, a short exposure image (b) having no blur in the horizontal and vertical directions, and a short exposure image having a blur in the vertical direction ( c). According to the third embodiment of the present invention, the amount of camera shake when the amount of misalignment between the normal exposure image and the short exposure image is small, and the amount of camera shake when the misalignment amount between the normal exposure image and the short exposure image is large. FIG. Relates to a third embodiment of the present invention, is a diagram for a pixel value distribution of the normal-exposure image and the short-exposure image, the estimated image degradation function of the normal exposure image (h 1 '), the relationship will be described. It is a flowchart showing the flow of operation | movement of the blurring correction process by a 1st correction method concerning 4th Embodiment of this invention. It is a detailed flowchart of the Fourier iteration method implemented by the blurring correction process by a 1st correction method concerning 4th Embodiment of this invention. It is a block diagram of a structure which implement | achieves the Fourier iteration method of FIG. It is a flowchart showing the flow of the operation | movement of the blurring correction process by a 2nd correction method concerning 4th Embodiment of this invention. FIG. 22 is a conceptual diagram of a shake correction process corresponding to FIG. 21. It is a flowchart showing the flow of operation | movement of the blurring correction process by a 3rd correction method concerning 4th Embodiment of this invention. FIG. 24 is a conceptual diagram of shake correction processing corresponding to FIG. 23. It is a figure which concerns on 4th Embodiment of this invention and shows a one-dimensional Gaussian distribution. It is a figure for demonstrating the effect of the blurring correction process corresponding to FIG. It is a figure which shows the example of the reference image and correction | amendment object image taken up in description of the 4th correction method concerning 4th Embodiment of this invention. It is a figure which shows the two-dimensional coordinate system and two-dimensional image of a space area. It is an internal block diagram of the image synthetic | combination part utilized in the 4th correction method which concerns on 4th Embodiment of this invention. It is a figure which shows the 2nd intermediate generation image obtained by reducing the noise of the reference image of Fig.27 (a). It is a figure which shows the difference image between the correction target image (first intermediate generation image) after alignment and the reference image (second intermediate generation image) after noise reduction processing. It is a figure which shows the relationship between the difference value obtained by the difference value calculation part of FIG. 29, and the mixing rate of the pixel signal of a 1st and 2nd intermediate production | generation image. It is a figure which shows the 3rd intermediate generation image obtained by synthesize | combining the correction target image (1st intermediate generation image) after alignment, and the reference image (2nd intermediate generation image) after a noise reduction process. It is a figure which shows the edge image obtained by performing an edge extraction process with respect to the reference image (2nd intermediate generation image) after a noise reduction process. It is a figure which shows the relationship between the edge strength value obtained by the edge strength value calculation part of FIG. 29, and the mixing rate of the pixel signal of a reference image and a 3rd intermediate generation image. It is a figure which shows the blurring correction image obtained by synthesize | combining a reference image and a 3rd intermediate generation image. It is a block diagram of the structure which implement | achieves the conventional Fourier iteration method.
DESCRIPTION OF SYMBOLS 1 Imaging device 11 Imaging part 12 AFE
DESCRIPTION OF SYMBOLS 13 Main control part 16 Recording medium 32 Aperture 33 Imaging element 35 Optical system 51 Shooting control part 52 Correction control part 53 Shake correction process part 54 Short exposure photographing control part 150 Image composition part
An imaging means for acquiring an image by shooting;
Blur correction processing for correcting blur of a first image obtained by shooting based on the first image and a second image shot with an exposure time shorter than the exposure time at the time of shooting the first image Means,
Control means for controlling whether or not to perform correction by the shake correction processing means ,
The control unit includes a blur estimation unit that estimates a degree of blur of the second image, and controls whether to perform correction by the blur correction processing unit based on the estimation result.
The blur estimation means refers to the value of each element of the estimated image degradation function obtained when the estimated image degradation function of the first image is expressed as a matrix obtained using the first image and the second image. An image pickup apparatus that extracts a value that deviates from a specified numerical value range among the referenced values and estimates a degree of blurring of the second image based on a sum of the extracted values. .
The first image obtained by shooting is corrected based on the first image and one or more second images shot with an exposure time shorter than the exposure time at the time of shooting the first image. Blur correction processing means;
Control means for controlling whether or not to perform correction by the shake correction processing means or the number of the second images used for the correction based on the shooting parameters of the first image;
Second image shooting control means for controlling the shooting means by determining whether the second image can be shot based on the shooting parameters of the first image;
Correction control means for controlling whether or not to perform correction by the shake correction processing means according to the determination result of whether or not the second image can be taken.
The photographing parameter of the first image includes a focal length, an exposure time, and a sensitivity for adjusting the brightness of the image at the time of photographing the first image. The imaging device described.
The imaging apparatus according to claim 2 , wherein the second image capturing control unit sets the capturing parameter of the second image based on the capturing parameter of the first image .
The blur correction processing unit treats an image based on the first image and an image based on the second image as a deteriorated image and an initial restored image, respectively, and corrects blur of the first image using a Fourier iteration method. The imaging apparatus according to any one of claims 1 to 4, wherein the imaging apparatus is characterized in that
The blur correction processing unit has an image degradation function deriving unit for obtaining an image degradation function representing the overall blur of the first image, corrects the blur of the first image based on the image degradation function,
The image degradation function deriving means includes a first function obtained by converting an image based on the first image onto the frequency domain, and a second function obtained by converting an image based on the second image onto the frequency domain. Through a process of tentatively obtaining the image degradation function on the frequency domain from the frequency domain and converting the obtained image degradation function on the frequency domain onto the spatial domain using a predetermined constraint condition The image pickup apparatus according to any one of claims 1 to 4 , wherein the image deterioration function is finally obtained .
The blur correction processing unit synthesizes the first image, the second image, and a third image obtained by reducing noise in the second image, thereby blurring the first image. The image pickup apparatus according to any one of claims 1 to 4, wherein a corrected shake-corrected image is generated .
The blur correction processing unit generates the blur correction image by generating the fourth image, which is a composite image of the first image and the third image, and then combining the second image and the fourth image. The image pickup apparatus according to claim 7, wherein the image pickup apparatus is provided.
The composition ratio when compositing the first image and the third image is set based on the difference between the first image and the third image,
The imaging apparatus according to claim 8 , wherein a composition ratio when the second image and the fourth image are synthesized is set based on an edge included in the third image .
Control means for controlling whether or not to perform correction by the shake correction processing means,
The blur correction processing unit synthesizes the first image, the second image, and a third image obtained by reducing noise in the second image, thereby blurring the first image. corrected blur correction image you wherein <br/> to generate imaging device.
The blur correction processing unit generates the blur correction image by generating the fourth image, which is a composite image of the first image and the third image, and then combining the second image and the fourth image. The imaging apparatus according to claim 10, wherein
The imaging apparatus according to claim 11 , wherein a composition ratio for composing the second image and the fourth image is set based on an edge included in the third image .
The first image obtained by shooting is corrected based on the first image and one or more second images shot with an exposure time shorter than the exposure time at the time of shooting the first image. Blur correction processing step;
A control step for controlling whether or not to perform correction by the shake correction processing step,
The control step includes a blur estimation step of estimating a blur degree of the second image,
Based on the estimation result, control whether to perform correction by the shake correction processing step,
The blur estimation step refers to the value of each element of the estimated image degradation function obtained by using the first image and the second image and representing the estimated image degradation function of the first image as a matrix. In the reference value, a value deviating from a prescribed numerical range is extracted, and the degree of blurring of the second image is estimated based on the sum of the extracted values.
A shake correction method characterized by the above.
A control step for controlling whether or not the correction by the shake correction processing step is executed or the number of the second images used for the correction based on the imaging parameters of the first image,
A second image shooting control step for controlling whether or not the second image can be shot based on a shooting parameter of the first image and controlling the shooting means;
A correction control step of controlling whether or not the correction by the blur correction processing step is executed according to the determination result of whether or not the second image can be taken.
Blur correction processing for correcting blur of a first image obtained by shooting based on the first image and a second image shot with an exposure time shorter than the exposure time at the time of shooting the first image Steps,
The blur correction processing step combines the first image, the second image, and a third image obtained by reducing noise in the second image, thereby blurring the first image. Generate a corrected image
JP2008306307A 2008-01-16 2008-12-01 Imaging apparatus and blur correction method Active JP5213670B2 (en)
JP2008007169 2008-01-16
JP2008023075 2008-02-01
JP2008306307A JP5213670B2 (en) 2008-01-16 2008-12-01 Imaging apparatus and blur correction method
US12/353,430 US20090179995A1 (en) 2008-01-16 2009-01-14 Image Shooting Apparatus and Blur Correction Method
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JP2008306307A Active JP5213670B2 (en) 2008-01-16 2008-12-01 Imaging apparatus and blur correction method
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EP1944732A2 (en) 2008-07-16 Apparatus and method for blur detection, and apparatus and method for blur correction
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