Abstract:
Techniques for creating a High Dynamic Range (HDR) image within a consumer grade digital camera from a series of images of a scene captured at different exposure levels, and displaying the HDR image on the camera&#39;s built-in display, are provided. The approach employs mixing images of the series to incorporate both scene shadow and highlight details, and the removing of “ghost” image artifacts appearing in the mixed HDR image resulting from movement in the scene over the time the series images are captured. The low computational resource utilization of the present invention&#39;s image mixing and ghost removal processing operations, along with the present invention&#39;s ability to commence image mixing and ghost removal prior to the acquisition of all series images, can significantly reduce the time required to generate and display a tone mapped HDR image.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims priority under 35 U.S.C. §119(e) to U.S. Provisional Application Ser. No. 61/171,936, entitled “HDR from Multiple Exposures” filed on Apr. 23, 2009 which is expressly incorporated herein in its entirety for all purposes by this reference. 
    
    
     BACKGROUND OF INVENTION 
     1. Field of Invention 
     This invention relates to the acquisition and processing of images that display the full range of gray shades that appear in a physical scene, often referred to as a “High Dynamic Range” or “HDR” image. More particularly it relates to a system and method for the image capture and processing of a HDR image in a digital image capture device such as a consumer grade digital camera. 
     2. Discussion of Related Art 
     Images captured by digital cameras are most commonly Low Dynamic Range (LDR) images, in which each image pixel is comprised of a limited number of digital bits per color. The number of digital bits per pixel is called the digital pixel bit width value. This number is commonly 8 bits. Such 8 bit pixels can be used to form an image with 256 different gray levels for each color at each pixel location. In a LDR image of a scene, shadow areas of the scene are depicted as being completely black (black saturation), bright sunlit areas of the scene are depicted as being completely white (white saturation), and scene areas in between are shown in a range of gray shades. A High Dynamic Range (HDR) image is one that has digital pixel bit width values of greater than 8 bits, 16 bits per pixels is a possible value. In such an image the full range of gray shades that appear in a physical scene can be displayed. These gray shades provide image details that are present in the scene&#39;s shadow regions, highlight regions and mid tone regions that are missing from the LDR image. Thus, in an HDR image, scene details are present in image dark areas that are in shadow due to their proximity next to tall buildings and beneath trees, in light areas directly illuminated by bright sunlight, as well as in mid-illumination areas that are lighted between these 2 extremes. 
     An HDR image can be captured by acquiring multiple LDR images of a scene that are captured at different exposure levels. These multiple LDR images are called a bracketed exposed image series. A low exposure level will properly capture the gray shades in scene areas fully illuminated by bright sunlight and a high exposure level will properly capture the gray shades in scene areas completely shielded from the sun and sky by buildings and trees. However, at the low exposure level the areas of the scene in shadow will be completely black, in black saturation, and show no detail, and the mid-tone areas will lose detail. Further, at the high exposure level, the highlights of the scene will be completely white, in white saturation, and show no detail, and the mid-tone areas will again lose detail. Thus, a third, mid exposure level image, which properly captures mid level gray shades, is often acquired as well. By mixing these three LDR images, an HDR image can be generated that depicts the full gray scale range of the scene. 
     Deriving a HDR image from a bracketed exposed image series currently requires a complex implementation that employs an expensive computational engine. This is due to the need to perform 3 separate processing operations to properly mix the bracketed exposed image series into a single HDR image, and a fourth to convert the resulting image, which is now composed of pixels with digital pixel bit width values of greater than 8 bits per color, into one that can be displayed on commonly available 8 bit per pixel per color displays. These four processing operations are:
         “Image Registration” for accurately aligning the multiple images one to another;   “Image Mixing” for blending the multiple images together with the proper weighting;   “Ghost Removal” for removing location shifted replications of scene objects, or ghosts, that would appear in the mixed HDR image, due to the movement of these objects over the time the multiple images were acquired; and   “Tone Mapping” for preparing the final HDR image for presentation on a conventional displays that are limited to displaying 8 bit per pixel per color image pixels.       

     To execute these four processing operations requires the performance of a large number of floating point operations over a short period of time, as can be seen from a review of “High Dynamic Range Imaging Acquisition, Display, and Image-Based Lighting, authors Erik Reinhard, Sumanta Pattanaik, Greg Ward and Paul Debevec, published by Morgan Kaufmann Publishers, copyright 2005 by Elsevier, Inc. This is especially the case for the image mixing and ghost removal processing operations. Thus, powerful and expensive computational engines (Central Processing Units or CPUs) need to be used. Their expense can possibly be tolerated for professional digital cameras use, but for inexpensive “Point and Shoot” digital cameras, which incorporate limited processing power CPUs, they represent an impractical solution. 
     An HDR image can be created from a bracketed exposed image series captured by an inexpensive digital camera by uploading the image series from the camera to a general purpose computer, such as Personal Computer (PC). An image processing application, such as Adobe Photoshop, can be used to perform the required complex HDR image combining process on a desktop. This approach is not efficient or convenient and does not meet demands to reconstruct an HDR image on the camera&#39;s built-in display shortly after its capture. 
     Thus there exists a need for an in-camera method and apparatus that can rapidly create a HDR image from a bracketed exposed image series, and display it on the camera&#39;s built-in display shortly after capture, using a limited processing power CPU. 
     SUMMARY OF INVENTION 
     It is therefore desirable to:
         (a) effect a mixing operation on a series of two or more images of a scene, such series images having been registered one to another, each image composed of pixels containing digital bits, to generate a composite image in which each pixel contains a number of digital bits, the number being greater than the number of digital bits contained in any series image pixel, in a processing operation resource efficient manner; and   (b) effect a ghost removal operation that removes location shifted replications of scene objects appearing in mixed image data, the mixed image data generated by a digital image mixing process applied to scene images acquired at different exposure levels and times, in a processing operation resource efficient manner.       

     According to a first aspect of the present invention, a registered, captured bracketed exposed image series composed of two or more LDR images is mixed to generate a HDR image with digital pixel bit width values greater than that contained in any of the initial LDR image pixels. Series images at different exposure levels are captured, where a series image exposed at a first exposure level is exposed less than a series image exposed at a second exposure level, which is exposed less than a series image exposed at a third exposure level, which is exposed less than a series image exposed at a n th  exposure level. A normalized image exposure level for each image in the series is derived by using the exposure level of the least exposed image of the series as the reference exposure level, and is employed in an image mixing process, wherein series images are blended together, two at a time. The image captured at the lowest exposure level of the series is first blended together with the image captured at the next highest exposure level in the series, to generate a first intermediate image. The generated first intermediate image is then blended together with the image captured at the next highest exposure level of the series to generate a second intermediate image. If the bracketed exposed image series is composed of two images, the mixing process stops at the generation of the first intermediate image, and the generated HDR image output is the first intermediate image. If there are three images in the series, the generated HDR image output is the second intermediate image. If there are more images in the bracketed exposed image series than three, each generated intermediate image is blended with the image captured at the next highest exposure level of the series to generate a next intermediate image, until there are no more series images left to blend. In this case, the HDR image output is the image generated by the blending of the last remaining image in the series with the previously generated intermediate image. 
     This mixing process operation greatly reduces the processing power required to mix a bracketed exposed image series to generate a HDR image, while minimizing processing latency. The act of normalizing image exposure level for each captured image to the exposure level of the lowest exposed (darkest) image of the bracketed exposed image series allows the mathematical operations employed by the blending processes to be mostly restricted to summations and multiplications, thus avoiding the use of division operations that have high computational resource requirements. 
     According to a second aspect of the present invention, a two stage computational resource efficient process is used to remove location shifted replications of scene objects, or ghosts, appearing in the mixed HDR image data generated by a digital image mixing process applied to a series of scene images acquired at different exposure levels and times. The second stage process removes ghosts that remain after the execution of the first stage of the ghost removal process. In the first stage the variance of the luma of every pixel of the HDR image as compared to the pixels of a reference image is calculated. Pixels in HDR image regions with variances exceeding a first threshold are replaced with pixels from the corresponding image regions of one the image series images, which is used as the reference image. This procedure removes a major part of the ghosts, but some ghosts may still remain. In the second processing stage, the luma of pixels of the ghost reduced HDR image, the first processed HDR image, resulting from the first stage ghost removal processing operation, are compared with the luma of pixels from the corresponding image regions of same image series reference image used for the first ghost removal stage. Ghost residuals are detected by calculating the differences between the luma of the first processed HDR image pixels and the luma of the pixels of the reference image. A second threshold based on the peak of these differences is generated. Pixels in HDR image regions exceeding this second threshold are replaced with pixels from the corresponding image regions of the reference image to produce a second processed HDR image. 
     In accordance with a third aspect of the present invention, the invention is incorporated within a digital camera that captures a series of two or more digital images of a scene at different exposure levels, and at different times, to generate a tone mapped HDR image, that can be displayed shortly after the images of the series are captured, on the camera&#39;s built-in display. Such a digital camera includes the image mixing and ghost removal processing operations of the present invention, with image registration, tone mapping, general digital camera image processing, control, image sensing and display technologies that are well known in the art. 
     By mixing two images of the series at a time, and removing ghosts from the mixed HDR image with respect to an image of the image series, the mixing and ghost removal processing operations of the present invention can commence prior to the capture of all the images that comprise the image series. In some cases image mixing can commence immediately after the capture of the second image of the series. The low computational resource utilization of the present invention&#39;s image mixing and ghost removal processing operations, along with the present invention&#39;s ability to commence image mixing and ghost removal prior to the acquisition of all series images, can significantly reduce the time required for a digital camera with low processing power to generate a tone mapped HDR image and display the HDR image on its built-in display. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The accompanying drawings are not intended to be drawn to scale. In the drawings, each identical or nearly identical component that is illustrated in various figures is represented by a like numeral. For purposes of clarity, not every component may be labeled in every drawing. In the drawings: 
         FIG. 1  is a block diagram of a digital camera or other image capture apparatus that captures a plurality of digital images of a scene, at different exposure levels and at different times, and displays these images on the camera&#39;s built-in image display; 
         FIG. 2  is a high level block diagram of an embodiment of the present invention illustrating processing modules as implemented in a digital camera; 
         FIG. 3  is a block diagram of an embodiment of the 2 Image Blending Engine of the present invention; 
         FIG. 4  is a flow chart illustrating the complete image mixing process sequence of an Image Mixer processing method of the present invention; 
         FIG. 4A  details the process used by the 2 Image Blending Engine of  FIG. 3 ; 
         FIG. 5  is a block diagram of an embodiment of the Ghost Remover processing module of the present invention; 
         FIG. 6  is a flow chart illustrating the processing sequence of a Ghost Remover processing method of the present invention. 
     
    
    
     DESCRIPTION OF THE PREFERRED EMBODIMENT 
     The present invention will now be described more fully hereinafter with reference to the accompanying drawings, which form a part thereof, and which show, by way of illustration, a specific embodiment by which the invention may be practiced. The invention may, however, be embodied in many different forms and should not be construed as limited to the embodiment set forth herein; rather, this embodiment is provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. Among other things, the present invention may be embodied as methods or devices. Accordingly, the present invention may take the form of an entirely hardware embodiment in the form of modules or circuits, and entirely software embodiment in the form of software executed on a general purpose microprocessor, an application specific microprocessor processor, a general purpose digital signal processor or an application specific digital signal processor, or an embodiment combining software and hardware aspects. Thus, in the following description, the terms “circuit” and “module” will be used interchangeably to indicate a processing element that executes an operation on a input signal and provides an output signal therefrom regardless of the hardware or software form of its implementation. Likewise, the terms “register”, “registration”, “align” and “alignment” will be used interchangeably to indicate the process of causing like objects to correspond one to another, and be in correct adjustment, regardless if the mechanism employed to bring about such correspondence is implemented in the form of hardware or software. The following detailed description is, therefore, not to be taken in a limiting sense. 
     Throughout the specification and claims, the following terms take the meanings explicitly associated herein, unless the context clearly dictates otherwise. The phrase “in one embodiment” as used herein does not necessarily refer to the same embodiment, though it may. As used herein, the term “or” is an inclusive “or” operator, and is equivalent to the term “and/or”, unless the context clearly dictates otherwise. The term “based on” is not exclusive and allows for being based on additional factors not described, unless the context clearly dictates otherwise. In addition, throughout the specification, the meaning of “a”, “an”, “and” and “the” include plural references. The meaning of “in” includes “in” and “on”. Also, the use of “including”, “comprising”, “having”, “containing”, “involving”, and variations thereof herein, is meant to encompass the items listed thereafter and equivalents thereof as well as additional items. 
       FIG. 1  shows a digital camera or other image capture apparatus which includes an Imaging Optical System  105 , an Electronically Controlled Shutter  180 , an Electronically Controlled Lens iris  185 , an Optical Image Sensor  110 , an Analog Amplifier  115 , an Analog to Digital converter  120 , an Image Data Signal Processor  125 , an Image Data Storage Unit  130 , an Image Display  106 , and Camera Controller  165 . The Image Data Storage unit could be a memory card or an internal nonvolatile memory. Data of images captured by the camera may be stored on the Image Data Storage Unit  130 . In this exemplary embodiment, it may also include internal volatile memory for temporary image data storage and intermediate image processing results. This volatile memory can be distributed among the individual image data processing circuits and need not be architecturally located in a single image data storage unit such as Image Data Storage Unit  130 . The Optical System  105  can be a single lens, as shown, but will normally be a set of lenses. An Image  190  of a Scene  100  is formed in visible optical radiation onto a two-dimensional surface of an image sensor  110 . An electrical output  195  of the sensor carries an analog signal resulting from scanning individual photo-detectors of the surface of the Sensor  110  onto which the Image  190  is projected. Signals proportional to the intensity of light striking the individual photo-detectors are obtained in the output  195 . The analog signal  195  is applied through an Amplifier  115  to an Analog to Digital Converter  120  by means of amplifier output  102 . Analog to Digital converter  120  generates an image data signal from the analog signal at its input and, through output  155 , applies it to Image Data Signal Processor  125 . The photo-detectors of the Sensor  110  typically detect the intensity of the light striking each photo detector element in one of two or more individual color components. Early detectors detected only two separate color of the image. Detection of three primary colors, such as red, green and blue (RGB) components, is now common. Currently image sensors that detect more than three color components are becoming available. 
     Multiple processing operations are performed on the image data signal from Analog to Digital Converter  120  by Image Data Signal Processor  125 . Processing of the image data signal, in this embodiment, is shown in  FIG. 1  as being effected by multiple image data signal processing circuits within Image Data Signal Processor  125 . However, these circuits can be implemented by a single integrated circuit image data signal processor chip that may include a general purpose processor that executes algorithmic operations defined by stored firmware, multiple general purposed processors that execute algorithmic operations defined by stored firmware, or dedicated processing logic circuits as shown. Additionally, these operations may be implemented by several integrated circuit chips connected together, but a single chip is preferred.  FIG. 1  depicts the use of image data signal processing circuits  135 ,  140 ,  145 , and  150  connected in series to effect multiple algorithmic processing operations on the image data signal from Analog to Digital Converter  120 . The result of these operations is a stored nonvolatile digital image data that can be viewed either on the digital camera&#39;s internal Image Display  106  of  FIG. 1 , or an external display device. This viewing can be effected either by the physical removal of a memory card from the digital camera and the reinsertion of this card into an external display device, or the electronic communication of the digital camera with an external display device by the use of a Universal Serial Bus (USB) connection, or a Wi-Fi or Bluetooth wireless local area network. 
     Additional processing circuits as indicated by the dots  175  between circuit  145  and  150 , can be included in the digital camera&#39;s image data signal processor. The series structure of the image data signal processor of the present embodiment is known as a “pipe line” architecture. This architectural configuration is employed as the exemplary embodiment of the present invention, however other architectures can be used. For example, an image data signal processor with a “parallel architecture”, in which one or more image data signal processing circuits are arranged to receive processed image data signals from a plurality of image data signal processing circuits, rather than after they have been processed serially by all preceding image data signal processing circuits, can be employed. A combination of a partial parallel and a partial pipeline architectures is also a possibility. 
     The series of image data signal processing circuits of Image Data Processor  125  is called an “image processing pipe”. The present invention adds image data signal processing circuits shown in  FIG. 2  to those routinely included in the image processing pipe of a digital camera. Image data signal processing circuits routinely included in the image processing pipe of a digital camera include circuits for White Balance Correction (WBC), Lens Shading Correction (LSC), Gamma Correction (GC), Color Transformations (CTM), Dynamic Range Compression (DRC), Demosaicing, Noise Reduction (NR), Edge Enhancement (EE), Scaling, and Lens Distortion Correction (LDC). As depicted in  FIG. 2 , the present invention adds an Image Registration Processor (IRP) circuit  210 , an Image Mixer (IM) circuit  220 , Ghost Remover (GR) circuit  230 , and Tone Mapping Processor (TMP) circuit  235  to the complement of image data signal processing circuits discussed above. Image Storage  200  of  FIG. 2  stores the digital data of a series of two or more images of a scene, each series image composed of pixels containing digital bits, these digital bits having been processed by Image data signal processing circuits in the image processing pipe. Image Storage  200  could share memory with Image Storage  130  of  FIG. 1 , however, memory resources used for temporary image data storage and intermediate image processing results, or totally separate volatile or nonvolatile memory resources, could provide the memory resources used by Image Storage  200 . 
     Referring to  FIG. 1 , Camera Controller  165 , through line  145  and Control/Status lines  160 , causes Electronic Shutter  180 , Electronic Iris  185 , Image Sensor  110 , Analog Amplifier  115 , and Analog to Digital converter  120  to capture and convert to digital image data a series of images of a scene. These images are captured at different exposure levels, processed by Image data signal processing circuits, and stored in Image Storage  200  of  FIG. 2 . Image Registration Processor  210  reads the image series digital image data stored in Image Storage  200  and registers counterpart pixels of each image of the image series one to to the other. Image registration is executed before image mixing in order to pixel to pixel align all series images. Due to both camera and object movement occurring during image series capture, such alignment is necessary for image mixer  220  of  FIG. 2  to be able to properly combine series image pixels and form an image with the captured scene&#39;s full range of gray shades. Such an image is often referred to as a “High Dynamic Range” or “HDR” image. In Image Mixer  220 , each series image pixel of each captured series image is combined with its pixel counterpart in each captured series image. Thus, an image pixel representing a particular position on the edge or within the body of an object appearing in a first series image is mixed with its counterpart located at the same position on the edge or within the body of the same object appearing in a second series image. In this regard, the location of a pixel in an image is with respect to the object in which it is part of, not to the fixed coordinate system of the defined by the vertical and horizontal outer edges of the image. 
     Image Registration Processor  210 , in general, employs a first image captured at a nominal exposure setting of the camera as a reference image to which all the other images of the series are aligned. A number of techniques are in current use for image alignment and registration. A good example is described in “High Dynamic Range Video”, S. B. Kang, M. Uyttendaele, S. Winder, and R. Szeliski, Interactive Visual Media Group, Microsoft Research, Redmond, Wash., 2003. The approach described handles both camera movement and object movement in a scene. For each pixel a motion vector is computed between successive series images. This motion vector is then refined with additional techniques, such as hierarchical homography, to handle degenerate cases. Once the motion of each each pixel is determined, frames can be warped and registered with the chosen reference image. The images can then be mixed by Image Mixer  220  into an HDR image. 
     The Image Mixing Process 
     Image Mixer  220  has the ability to mix an unlimited number of images, but employs an image blending engine that mixes the pixels of 2 images at a time. The 2 Image Blending Engine of Mixer  220  is shown in  FIG. 3 . In the preferred embodiment of the present invention, the blending engine blends the pixels of a first 8 bit image whose digital image data appears on input line  300  and the pixels of a second 8 bit image whose digital image data appears on input line  305 . Images with bit widths that are wider than 8 bits, for example 10 bits, or narrower, for example 7 bits can be used. The Flow Chart of  FIG. 4  illustrates the complete image mixing process of Image Mixer  220 , and  FIG. 4A  details Block  420  of  FIG. 4 , the process used by the 2 Image Blending Engine of  FIG. 3 . 
     Referring to  FIG. 3 , the image mixing process of the present invention blends 2 images during each image mixing operation. The first 2 images to be blended are both taken from the captured image series, wherein each image of the captured image series has been previously registered with a reference image captured at a nominal exposure setting of the camera. For the initial image mixing operation the series image with a lower exposure level provides the First Image digital image data input appearing on line  300  of  FIG. 3 , and the series image with a higher exposure level provides the Second Image digital image data input appearing on line  305 . For a second and all subsequent image mixing operations, a subsequent image of the series is blended with the result obtained from a previous image mixing operation. For these follow-on mixing operations, the digital image data of a subsequent image of the series serves as the Second Image digital image data input appearing on  305 , and the mixed image digital image data result serves as the First Image digital image data input appearing on line  300  of  FIG. 3 . In all cases, the subsequent image of the series has been exposed at a higher exposure level than its immediate image series predecessor. 
     The Second Image digital image data on line  305  is initially processed in 2 ways. (1) Luma Conversion Circuit  320  extracts the Luma, the black and white component, of the combined Red, Green and Blue (RGB) component data comprising Second Image digital image data  305 , and outputs the Luma component of each image data pixel on line  325 . (2) Image Normalizer  310  normalizes the exposure level of each RGB component of the Second Image image data on line  305  to the exposure level of a reference image, and outputs on line  302  each image data pixel, for each color component, normalized to the reference image exposure level. Note that the reference image used is not necessarily the same reference image used for the registration process previously described. For the preferred embodiment of the present invention the exposure level of the darkest image of the series, that is the image which is least exposed, serves as the reference exposure level and all other images of the series are normalized to it. For example, if the captured image series is composed of 3 images, a dark image exposed for 1/64 sec, a medium image exposure at 1/16 sec and a bright image exposed at ½ sec, the normalized value of each pixel of the medium image appearing on line  302  would be:
 
Medium Pixel Value Normalized =Medium Pixel Value Input /(( 1/16)/( 1/64))=Medium Pixel Value Input /4;  (1)
 
and
 
the normalized value of each pixel of the bright image appearing on line  302  would be:
 
Bright Pixel Value Normalized =Bright Pixel Value Input /((½)/( 1/64))=Bright Pixel Value Input /32  (2)
 
     Therefore, for the preferred embodiment of the present invention:
 
Exposure Level Normalized =Exposure Level Series Image /Exposure Level Least Exposed Series Image  and;  (3)
 
the normalized value of each pixel of the Second Image Data input on line  305  and output on line  302  is:
 
2nd Image Pixel Value Normalized =2nd Image Pixel Value Input /2nd Image Exposure Level Normalized   (4)
 
     The luma component of each Second Image digital image data pixel appearing on line  325  of  FIG. 3  is input to Look-up Table (LUT)  315  to obtain a per pixel weighting parameter, W i , on lines  330  and  335 . The luma component of each Second Image pixel serves as an index into LUT  315 , and causes a weighting parameter value, W i  between the numbers zero and Unity to be output to lines  330  and  335  for each input luma value. This value is output in the form of a two dimensional matrix where:
 
 W   (m,n) =255−Luma (m,n) ;  (6)
 
Luma (m,n)  is the Luma component of each Second Image digital data pixel at image coordinates (m,n), which, for the preferred embodiment of the present invention, can attain a maximum value of 255, since the embodiment blends the pixels of 8 bit series images, and 255=Unity, which, represents Table  315 &#39;s 100% output value, because the luma component Second Image pixel values serving as indexes into LUT  315 , are 8-bit digital values with a number range from 0 to 255. Therefore, defining 255 as Unity allows for a direct mapping from input index value to output weighting parameter value and reduces weighting parameter application computational work load. Other values of Unity can be chosen. For example, if the luma component Second Image pixel values serving as indexes into LUT  315  are 10-bit digital values, with a number range from 0 to 1024, it would be appropriate and beneficial to assign Unity the value of 1024.
 
     For the preferred embodiment, the weighting parameter value output from LUT  315  linearly decreases as the luma component Second Image pixel value, serving as the index into LUT  315 , increases. Other LUT functions, for example, trapezoidal shaped functions in which the weighting parameter value obtained from LUT  315  remains at a predetermined value and starts to linearly increase when the luma component Second Image pixel value index decreases below a threshold, may be used as well. The choice of LUT  315  functions is based on the observation that when two images are mixed, one which is highly saturated, due to being exposed at a high exposure level, perhaps at a long exposure time, and the other dark, due to being exposed at a lower exposure level, perhaps at a short exposure time, it is desirable to apply a low weight to the highly saturated pixels of the image with the high exposure level, while applying a high weight to the counterpart pixels of image with the low exposure level. This will result in a mixed image with fewer highly saturated pixels, since many have been supplanted by counterpart, properly exposed pixels. The result is a mixed image with greater detail in its highlight areas. 
     The present invention is not restricted to the use of a single LUT  315 . A plurality of LUTs can be used. In this case a different LUT can be associated with each Series Image for obtaining the weighting value, or two or more Series Images can be associated with the same LUT from a plurality of provided LUTs. These LUTs can, for example, be populated with weighting parameter values responsive to Series Image exposure levels. 
     During the blending operation of the present invention, the weighting parameter is applied, on a pixel by pixel basis, to each color component of the normalized Second Image digital image data appearing on line  302 , and 1 minus the weighting parameter (1−W i ) is applied to each color component of the First Image digital image data appearing on line  300 . The pixel by pixel blending operation of the present invention is defined by the following equation:
 
Blended Image Data Pixel=(1− W   i )×(1st Image Data Pixel)+ W   i ×(Normalized 2nd Image Data Pixel)  (5)
 
The processing blocks of  FIG. 3  execute equation (5) as follows: The Luma of the Second Image Data on Line  305  is derived by Luma Conversion circuit  320  and used by LUT  315  to generate weighting parameter W i  on Lines  330  and  335 . Multiplier  307  multiplies normalized Second Image Digital Image Data, normalized by Image Normalizer  310 , by W i  on Line  330  and outputs the result on Line  355 . W i  is also applied to Data Subtractor  340  through Line  335 , which outputs (1−W i ) on Line  345 . First Image Digital Image Data on Line  300  is multiplied by (1−W i ) on Line  345  by Multiplier  350  and outputs the result on Line  365 . The Normalized and weighted Second Image Digital Image Data on Line  355  is added to weighted First Image Digital Image Data on line  365  by adder  360 . Adder  360  outputs Blended Image Pixels on Line  370 . These pixels are stored in Image Storage  375  and output as 2 Image Blended Image Data on Line  380 .
 
     The pixel blending process used by the 2 Image Blending Engine of  FIG. 3  is depicted in processing Block  420  of  FIG. 4A . The data of the First Image to be blended enters the process at  423  and the data of the Second Image to blended enters the process at  413 . The pixel blending process begins at  445 . At  427  a pixel from the Second Image is selected. The selected Second Image pixel is normalized at  429  and its luma is derived at  450 . The Second Image pixel&#39;s luma is used to obtain weighting parameter W i ,  485 , from a LUT at  465 . The normalized Second Image pixel is multiplied by weighting parameter W i ,  485 , at  455 . The normalized and weighted Second Image pixel enters an adding process at  475 . The First Image Pixel counterpart of the selected Second Image Pixel is selected at  460  and multiplied by (1−W i ) at  470 . The weighted First Image pixel enters the adding process at  475  and is blended with the normalized and weighted Second Image pixel. If there are more image pixels left to blend, as determined at decision point  480  and indicated by “No” on  431 , then the next pixel blend cycle is initiated at  445 , causing a next First Image pixel and a next Second Image pixel to be selected at  460  and  427 , respectively, and blended as described. 
     If there are no more image pixels left to blend, as determined at decision point  480  and indicated by “Yes” on  433 , but there are more images in the captured image series to mix, as determined at decision point  495  and indicated by “No” on  487 , then another Second Image is selected from the remaining, unmixed series images, whose data will serve as the next Second Image data to be blended. The Second Image selected will have a higher exposure level than the previous Second Image selection. Such selection is made by Image Selection Process  425  in response to the “No” on  487 . Additionally, the 2 Image Mixed Image Output Data at  493  is selected by First Image data selection process  435  as the next First Image data to be blended, due to decision point  430  signaling “Yes” to process  435  at  441 , in response to 2 Image Mixed Image data begin available at  493 . If 2 Image Mixed Image data is not available at  493 , as would be the case at the beginning of an image mixing process, decision point  430  would signal processing Block  440 , by placing a “No” at  437 , to select a First Image from the captured image series to be blended with a lower exposure level than the Second Image to be blended selected from the captured image series by processing Block  425 . In this case, Selected Second Image exposure level information is communicated to selection Block  440  at  407 , least exposed Series Image exposure level information is communicated to selection Block  440  at  443 , and selected Series Image data is communicated to processing Block  440  at  439 . As depicted in the Flow Chart of  FIG. 4 , and used in the preferred embodiment of the invention, the Series Image with the least exposure level may be selected as First Image. 
     If there are no more images in the captured image series to mix, the process concludes with the Mixed HDR Image Output appearing at  497 . 
     The Flow Chart of  FIG. 4  illustrates the complete image mixing process of Image Mixer  220 . Block  420 , depicting the 2 Image Image Blending process used by the 2 Image Blending Engine of  FIG. 3 , is highlighted in  FIG. 4  and illustrated in detail in  FIG. 4A .  FIG. 4  also includes the processing that precede 2 Image Blending processing Block  420 . This processing is comprised of the capture of a series of images, each image being exposed at a different exposure level, at processing Block  400 , the registration of counterpart series image pixels one to another, at processing Block  405 , the determination of the series image that is least exposed, at Processing Block  410 , and the calculation of a normalized exposure level according to Equation (3) previously described, referenced to the least exposed image of the series, for each image in the series, at processing Block  415 . This calculated normalized exposure level is used by  429  of processing Block  420  to normalize each Second Image pixel, as previously described by Equation (4), before being multiplied by a weighting parameter and blended with a weighted First Image pixel according to Equation (5) previously described. 
     The image mixing process of Image Mixer  220  uses only summation and multiplications, thereby avoiding computationally intensive division operations, and allowing it to be implemented by a fixed point computational engine. In addition, since the mixing approach employed is based on successive 2 image mixing operations, there is no need to wait until all series images have been captured before initiating a mixing operation. The mixing process can begin immediately after just 2 images of the series have been captured. These characteristics allow the mixing process of the present invention to rapidly create a HDR image from a bracketed exposed image series using a limited processing power computational engine. 
     The Ghost Removal Process 
     Ghost Remover  230  removes location shifted replications of scene objects, or ghosts, that appear in the Mixed HDR Image Output data at  497  of  FIG. 4 , due to the movement of these objects over the time the images of the series are captured. Fundamentally, ghosts appear due to the mixing of Series Images in which an object visualized in a First Series Image has moved with respect to its First Series Image location coordinates, as visualized in a Second Series Image. Consequently, in the mixed image, the object may appear in multiple locations with the locations depending on the object&#39;s motion rate and motion direction. The present invention employs a unique 2 stage approach to mitigating ghost images. 
     Given a mixed image, HDR (i,j), generated from a weighted sum of images from a captured aligned image series of K images, the present invention, in a first stage of processing, first calculates the variance of the luma value of every pixel of HDR(i,j) as follows:
 
 V ( i,j )=Σ k   W ( i,j,k )× P   2 ( i,j,k )−HDR( i,j ) 2   (7)
 
Where:
     V(i,j)=The variance of the luma value of Mixed HDR image pixel, HDR(i,j), located at image coordinates (i,j) with respect to the value of Kth Series Image pixel located at image coordinates (i,j), over the K aligned images of the captured image series;   HDR(i,j)=The luma value of the Mixed HDR image pixel located at image coordinates (i,j);   W(i,j,k)=A normalizing weight applied to the level of the Kth Series Image pixel located at image coordinates (i,j) to normalize the Series Image pixel level range to the pixel level range of the Mixed HDR image; and   P(i,j,k)=The value of Kth Series Image pixel located at image coordinates (i,j);
 
and then replaces a Mixed HDR Image pixel whose luma variance exceeds a first threshold level with its counterpart pixel from an aligned reference image, HDR ref , the reference image begin chosen from the captured image series, to generate first processed mixed image data, HDR 1st processed .
   

     The first stage of ghost removing processing, described above, is based on the observation that if no local motion exists in the Series Images mixed, the variance of a pixel in the Mixed HDR Image output over the K aligned images of the captured image series, as defined by Equation (7) above, will be low. The only significant error associated with this assumption is alignment error, which is around −15 decibels relative to the variance of each of the K aligned images of the captured image series. Since alignment is inherently a global process, it cannot compensate for local image object local motion, and thus local object motion is manifested as high amplitude pixel variance regions. By analyzing the high amplitude pixel variance regions in the 2-dimensional variance data generated by Equation (7), regions of local motion in the Mixed HDR Image output data can be defined, and Mixed HDR Image output data pixels with variances above a predefined threshold can be replaced with counterpart pixels from the reference image. The reference image chosen is often the least exposed Series Image, but may be a Series Image exposed at a higher exposure level. 
     The present invention&#39;s first stage of ghost removal processing generates first processed mixed image data, HDR 1st processed , with less ghosting. However some residual ghosting still remains. A second stage of processing improves these results by comparing the content of HDR 1st processed  with HDR ref . In this second stage, ghost residuals are detected by analyzing the pixel to pixel result obtained by subtracting the luma of HDR ref  from the luma of HDR 1st processed . A second threshold level based on the maximum value of the differences between the luma of HDR 1st processed  and the luma of HDR ref  is generated. Each HDR 1st processed  data pixel exceeding the second threshold level is replaced with its counterpart HDR ref  data pixel, resulting in second processed mixed image data, HDR 2nd processed , with fewer ghosts. The procedure used by the preferred embodiment of the present invention&#39;s second stage of processing can be summarized as follows: 
                                     (a)   Generate D 0  = ABS(Luma(HDR 1st processed ) − Luma(HDR ref ));       (b)   Determine Threshold 2nd  = Max(D 0 ) = D M0 .       (c)   Replace each HDR 1st processed  data pixel exceeding Threshold 2nd  with its counterpart           HDR ref  data pixel, resulting in HDR 2nd processed  mixed image data       (d)   Compare HDR 2nd processed  with HDR ref  and generate Max(D 1 ) = D m1  where           D m1  = Max((ABS(Luma(HDR 2nd processed ) − Luma(HDR ref )))       (d)   If D m1  &gt; 60% of D M0 , Threshold 2nd  is too large and HDR 2nd processed  may look too much like           HDR ref . Then:       (e)     Segment D M0  into 2 levels, where D M00  = a value &lt; 0.5D M0 , and D M01  = a value &gt;             0.5D M0         (f)     Determine, in percent, the amount of HDR 2nd processed  image area relative to the full             image area, exceeding D M01 , SIZE_1       (g)     Determine, in percent, the amount of HDR 1st processed  image area relative to the full             image area, exceeding D M00 , SIZE_0, where SIZE_1 should be &gt;= SIZE_0.       (g)     Calculated SIZE-RATIO = SIZE_1 / SIZE _0       (h)     IF (SIZE_0 &gt; 40% || (SIZE_RATIO &gt; 2 &amp;&amp; SIZE_1 &gt; 8 %))       (i)       Capture Series Images again       (j)     Else       (k)       Replace pixels of HDR 2nd processed  image areas exceeding D M01 , with their               counterpart HDR ref  pixels, resulting in HDR 2nd processed  mixed image data                    
(10) End
 
     The ghost removal process of the present invention can be applied to any aligned, captured bracketed exposed series of two or more images. In addition, two or more HDR ref  images can be employed by the process. When the process is applied to a series of three images, for example, the exposure level of a first series image being lower than the exposure level of a second series image and the exposure level of the second series image being lower than the exposure level of a third series image, the areas of the regions of the second series image that correspond to mixed image data with variances that exceed the first threshold can be used to select between 2 reference images, HDR ref1  and HDR ref2 . In this example, second series image region areas that are saturated are summed and a ratio of the sum of saturated area to the total area of the second series image is used to select HDR ref  for the remainder of ghost removal processing. If the ratio is less than or equal to 0.03 to 1 then HDR ref2 =the second series image is selected. If the ratio is greater than 0.03 to 1 then then HDR ref1 =the first series image is selected. Further, the above selection approach, or ones similar in nature that are responsive to other image characteristics, such as, for example, the size of image areas with object movement above a predetermined threshold, or spacial frequency details above a predetermined threshold, can be used to select a HDR ref1  for the first stage of ghost removal processing and, additionally, a different HDR ref2  for the second stage of ghost removal processing. 
       FIG. 5  is a block diagram of an embodiment of the present invention&#39;s Ghost Remover processing module,  230  of  FIG. 2 . Mixed HDR Image pixel data is input to Luma Conversion Circuit  515  and First Pixel Replacement Circuit  540  on line  505  of  FIG. 5 . Luma Conversion Circuit  515  converts Mixed HDR Image Pixel Data to Mixed Image Luma Pixel Data and, through line  525 , inputs Mixed Image Luma Pixel Data to Variance Calculation Circuit  550 . Although not shown in  FIG. 2 , aligned images of the captured bracketed exposed series of two or more images are input to the Ghost Remover module  230  on line  500  of  FIG. 5 , which is connected to Reference Image Selection Circuit  510 . In the preferred embodiment of the present invention, Reference Image Selection Circuit  510  selects the least exposed series image as the reference image, HDR ref , however a Series Image exposed at a higher exposure level could be selected. Through line  520 , HDR ref  Pixel Data is also applied to Variance Calculation Circuit  550 . Additionally, line  520  applies HDR ref  Pixel Data to 1st Pixel Replacement Circuit  540 , 2nd Pixel Replacement Circuit  585  and Luma Conversion Circuit  560 . From the HDR ref  Pixel Data on line  520  and Mixed Image Luma Pixel Data on line  525 , Variance Calculation Circuit  550  generates output Mixed Image Luma Pixel Variance Data on line  530 . This Luma Pixel Variance Data is applied to First Pixel Replacement Circuit  540  through line  530 . On line  535 , a 1st Threshold Level is also applied to 1st Pixel Replacement Circuit  540 . From these inputs, 1st Pixel Replacement Circuit  540  replaces pixels of the Mixed Image Pixel Data on line  505 , whose Luma variance exceeds the first threshold level on line  535 , with counterpart pixels from the HDR ref  Data on line  520 , to generate first processed mixed image pixel data, HDR 1st processed , on line  545 , which is the output of a first stage of processing. 
     The first stage of processing output, HDR 1st processed , on line  545 , is converted to HDR 1st processed Luma  Pixel Data by Luma Conversion Circuit  565 . The output of Circuit  565  appears on line  595  and is connected to Comparison Circuit  575 . HDR 1st processed  on line  545  is also applied to 2nd Pixel Replacement Circuit  585 . Line  520  applies HDR ref  Pixel Data to Luma Conversion Circuit  560  and 2nd Pixel Replacement Circuit  585 . Pixel Data to Luma Conversion Circuit  560  converts HDR ref  Pixel Data to HDR ref Luma  Pixel Data, and provides the HDR ref Luma  Pixel Data to Comparison Circuit  575  over Line  570 . Comparison Circuit  575  calculates the difference between the each HDR 1st processed Luma  Data Pixel and its counterpart HDR ref Luma  Data Pixel and generates a 2nd Threshold Level based on the maximum value of the differences. This 2nd Threshold Level is applied to 2nd Pixel Replacement Circuit  585  over Line  580 . 2nd Pixel Replacement Circuit  585  replaces each HDR 1st processed  data pixel on line  545  exceeding the 2nd threshold level with its counterpart HDR ref  data pixel on line  520 , the resulting 2nd processed mixed image data, HDR 2nd processed , on line  590 , being the ghost reduced output of a second stage of processing. 
     The 2 stage ghost removal process used by the Ghost Remover processing module of  FIG. 5  is depicted in the flow chart of  FIG. 6 . At Block  600 , a bracketed exposed series of two or more images, each image exposed at a different exposure level and at a different time, is captured. At Block  605  these images are registered to each other such that counterpart series image pixels correspond one another. At Block  610  a reference image is selected from the acquired scene images and its pixel data is passed onto processing Blocks  625 ,  640 ,  660  and  695  over processing path  615 . The reference image chosen is often the least exposed Series Image, but may be a Series Image exposed at a higher exposure level. It does not have to be the same reference image as used by the present invention&#39;s Image Registration Processor  210 , which, in general, employs a first image captured at a nominal exposure setting of the camera as a reference image to which all the other images of the series are aligned. The Image Mixer of the present invention previously described, whose image mixing process is depicted in the flow chart of  FIG. 4 , performs the processing at Block  620 , with the Mixed HDR Image Output Image Data Pixels of  FIG. 4  entering processing Blocks  650  and  640 . At Block  650  the Luma of Mixed Data Pixels is generated and passed onto Block  625 , where the variance of each Mixed Image Data Pixel Luma from Block  650 , as compared with its counterpart Reference Image Data Pixel from Block  610 , is calculated. This variance is passed to processing Block  640  and used by Block  640 , along with a 1st Threshold which enters processing Block  640  along path  635 , Mixed Image Data Pixels from processing Block  620 , which enters processing Block  640  along path  650 , and Reference Image Data Pixels which enters processing Block  640  along path  615 , to replace each Mixed Image Data Pixel, with a variance exceeding the 1st Threshold, with its counterpart Reference Image Data Pixel, and generate 1st Processed Mixed Image Data Pixels. 1st Processed Mixed Image Data Pixels are the result of a first stage of ghost removal processing. 
     1st Processed Mixed Image Data Pixels are passed to processing Blocks  665  and  695  along processing path  655 . Processing Block  665  generates the Luma of 1st Processed Mixed Image Data Pixels, while processing Block  660 , from Reference Image Data Pixels which enters processing Block  660  over path  615 , generates the Luma of each Reference Image Data Pixel. Processing Block  670  calculates the difference, on a pixel by pixel basis, between the Luma value of each 1st Processed Mixed Image Data Pixel and the Luma value of it&#39;s counterpart Reference Image Data Pixel and provides these differences to processing Block  675 . Processing Block  675  determines the maximum value of these differences and processing Block  680  generates a 2nd Threshold based on this maximum value. Processing Block  695  receives this 2nd Threshold over path  685 , along with Reference Image Data Pixels over Path  615  and 1st Processed Mixed Image Data Pixels over path  655  and replaces each 1st Processed Mixed Image Data Pixel that exceeds this 2nd Threshold with its corresponding Reference Image Data Pixel counterpart, and thus generates enhanced ghost removed 2nd Processed Mixed Image Data on processing Path  695 . This 2nd Processed Mixed Image Data, the result of a 2nd stage of ghost removal processing, is used as input to a Tone Mapping processor, such as  235  of  FIG. 2 . 
     The Tone Mapping Process 
     In the preferred embodiment of the present invention, enhanced ghost removed 2nd Processed Mixed Image Data on processing Path  695  of  FIG. 6  is 16 bits in bit-width and is comprised of 3 color components, a red component, a green component and blue component. This RGB 16 bit data is to be displayed on Built-In Digital Camera Display  245  of  FIG. 2 . which is an 8 bit display, so the 2nd Processed Mixed Image Data needs to be converted from 16 bit RGB data to 8 bit RGB data. The process of converting image data of one bit-width to that of a narrower bit width, such as from 16 bits to 8 bits, while maintaining the relative gray shade levels represented in the wider bit-width data in the resulting 8 bit data, is referred to as “Tone Mapping”. There are many such Tone Mapping processes that can be used by the present invention. The preferred embodiment of the present invention employs a unique tone mapping approach which was originally designed to map 12 bit wide image data to 8 bit wide image data. Therefore, the present invention approach first removes the 4 least significant bits of the 2nd Processed Mixed Image Data, leaving 12 bit RGB image data. Three Look-Up tables (LUTs) are used to map the remaining 12 bit RGB data to the needed 8 bit RGB data:
     (a) A Normal Gain LUT,   (b) A High Gain LUT; and   (c) A Very High Gain LUT   

     The proper LUT to use in the 12 bit to 8 bit tone mapping process needs to be selected in order to correctly represent the image gray shades present in the 2nd Processed Mixed Image Data in 8 bit data form. The selection criteria depends on the size of image area populated with pixels whose value, on average, is below a predefined pixel value, or “dark”, as compared to the rest of the image area. The lower the average pixel value in the image dark area, the higher the gain of the LUT selected. 
     The process of LUT selection is as follows:
     (1) Shift right the 12 bit RGB image by 4 bits. This results in an 8 bit image;   (2) Generate the Luma component of the resulting 8 bit image;   (3) Calculate the average value, Mn, of all pixels in the 8-bit image whose Luma is less than a threshold for dark area, Td. A digital value of 20 out of a maximum digital value of 255 (the maximum 8 bit value) can be used for Td;   (4) If the sum of all the pixels having Luma &lt;Td is less than an area threshold, P %, use the Normal Gain LUT, Otherwise:   (5) Given Mn and predefined Thresholds pixel value thresholds T 1  less than T 2 :   (6) If Mn&lt;T 1  use the Very High Gain LUT;   (7) If Mn between T 1  and T 2  use the High Gain LUT   (8) If Mn&gt;T 2  use the Normal Gain LUT   

     Td=20 out of 255, T 1 =5 out of 255 and T 2 =10 out of 255 are examples of the Thresholds that can be employed in the above Tone Mapping LUT selection process. 
     The tone mapping procedure employed by the present invention is designed to boost image regions of low illumination, while the 3 LUTs behave the same for image regions of high illumination. It was found that this provides a good result when applied to the 2nd Processed Mixed Image Data of the present invention. 
     Having thus described several aspects of the preferred embodiment of the present invention, it is to be appreciated that various alterations, modifications, and improvements will readily occur to those skilled in the art. Such alterations, modifications, and improvements are intended to be part of this disclosure, and are intended to be within the spirit and scope of the invention. Accordingly, the foregoing description and drawings are by way of example only.