Abstract:
The noise reduction process is appropriately changed according to a proportion of the facial region in an angle of view, thereby minimizing deterioration of background resolution as well as removing wrinkles and blemishes in the facial region.

Description:
BACKGROUND OF THE INVENTION 
       [0001]    1. Field of the Invention 
         [0002]    The present invention relates to a noise reduction process of an image using a face detecting function. 
         [0003]    2. Description of the Related Art 
         [0004]    Japanese Patent Application Laid-Open No. 2005-196270 discloses a technique for breaking down signals from pixels in a flesh color region into luminance and color-difference signals, reducing signal strength of pixels in a specific range of the flesh color region through wavelet transform, and thereby removing wrinkles and blemishes. 
         [0005]    Japanese Patent Application Laid-Open No. 9-233423 discloses a technique for enhancing edges and changing tones according to a size of a facial region. This makes it possible to separately process a facial region and another region different from the facial region. 
       SUMMARY OF THE INVENTION 
       [0006]    However, with the technique disclosed in Japanese Patent Application Laid-Open No. 2005-196270, if a color of a skin is not in the flesh color region, no blemish removal process is performed With the technique disclosed in Japanese Patent Application Laid-Open No. 9-233423, it takes a lot of processing time to process the two regions separately, resulting in complicated control. 
         [0007]    An object of the present invention is to combine wrinkle and blemish removal with high image quality by varying skin tone improvement process (wrinkle and blemish removal process) according to a proportion of a face in an angle of view and thereby minimizing deterioration of background resolution. 
         [0008]    An image processing method according to a first aspect of the present invention, comprises the steps of: inputting an image; detecting a facial region in the image; determining a frequency band in which a luminance signal of the image will be subjected to a predetermined noise reduction process based on a size of the detected facial region; extracting the determined frequency band from the luminance signal of the image; and performing the predetermined noise reduction process on the frequency band extracted from the luminance signal of the image. 
         [0009]    An image processing method according to a second aspect of the present invention, comprises the steps of: inputting an image; detecting a facial region in the image; determining a frequency band in which a color-difference signal of the image will be subjected to a predetermined noise reduction process based on a size of the detected facial region; extracting the determined frequency band from the color-difference signal of the image; and performing the predetermined noise reduction process on the frequency band extracted from the color-difference signal of the image. 
         [0010]    An image processing method according to a third aspect of the present invention, comprises the steps of: inputting an image; separating a luminance signal of the image into a plurality of frequency components according to a plurality of predetermined frequency bands; detecting a facial region in the image; determining a weight for each of the separated frequency components to be subjected to a noise reduction process based on a size of the detected facial region; and performing a predetermined noise reduction process on each of the separated frequency components based on the determined weight for each of the separated frequency components. 
         [0011]    An image processing method according to a fourth aspect of the present invention, comprises the steps of: inputting an image; separating a color-difference signal of the image into a plurality of frequency components according to a plurality of predetermined frequency bands; detecting a facial region in the image; determining a weight for each of the separated frequency components to be subjected to a noise reduction process based on a size of the detected facial region; and performing a predetermined noise reduction process on each of the separated frequency components based on the determined weight for each of the separated frequency components. 
         [0012]    In addition, when the size of the facial region is an intermediate size between two predetermined sizes of a facial region (face size), i.e., a first size and a second size, a weight for the facial region of the intermediate size may be determined by linear interpolation from weights of the first size and the second size. 
         [0013]    An image processing apparatus according to a fifth aspect of the present invention, comprises: an image input unit which inputs an image; a face detecting unit which detects a facial region in the image; a frequency band determining unit which determines a frequency band in which a luminance signal of the image will be subjected to a predetermined noise reduction process based on a size of the detected facial region; a frequency band extracting unit which extracts the determined frequency band from the luminance signal of the image; and a noise reduction processing unit which performs the predetermined noise reduction process on the frequency band extracted from the luminance signal of the image. 
         [0014]    An image processing apparatus according to a sixth aspect of the present invention, comprises: an image input unit which inputs an image; a face detecting unit which detects a facial region in the image; a frequency band determining unit which determines a frequency band in which a color-difference signal of the image will be subjected to a predetermined noise reduction process based on a size of the detected facial region; a frequency band extracting unit which extracts the determined frequency band from the color-difference signal of the image; and a noise reduction processing unit which performs the predetermined noise reduction process on the frequency band extracted from the color-difference signal of the image. 
         [0015]    An image processing apparatus according to a seventh aspect of the present invention, comprises: an image input unit which inputs an image; a separation unit which separates a luminance signal of the image into a plurality of frequency components according to a plurality of predetermined frequency bands; a face detecting unit which detects a facial region in the image; a weight determining unit which determines a weight for each of the separated frequency components to be subjected to a noise reduction process based on a size of the detected facial region; and a noise reduction processing unit which performs a predetermined noise reduction process on each of the separated frequency components based on the determined weight for each of the separated frequency components. 
         [0016]    An image processing apparatus according to an eighth aspect of the present invention, comprises: an image input unit which inputs an image; a separation unit which separates a color-difference signal of the image into a plurality of frequency components according to a plurality of predetermined frequency bands; a face detecting unit which detects a facial region in the image; a weight determining unit which determines a weight for each of the separated frequency components to be subjected to a noise reduction process based on a size of the detected facial region; and a noise reduction processing unit which performs a predetermined noise reduction process on each of the separated frequency components based on the determined weight for each of the separated frequency components. 
         [0017]    An image processing program which makes a computer perform any of the image processing methods described above can also achieve the object of the present invention. In addition, a recording medium on which the image processing program is stored can also achieve the object of the present invention by causing a computer on which the program is installed to execute the program. 
         [0018]    Further, an image pickup apparatus which can achieve the object of the present invention can also be realized, and the image pickup apparatus comprises: any one of the image processing apparatus described above; an image pickup element which receives a subject image via a photographic optical system and outputs an analog image signal that represents the subject image; and an image output unit which converts the analog image signal into a digital image data and outputs the digital image data to the image input unit. The image pickup apparatus can also achieve the object of the present invention. 
         [0019]    According to the any one of the aspects of the present invention, by varying the level of noise reduction according to the size of the facial region, improvement of skin tones of human subjects can be achieved while maintaining background image quality. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0020]      FIG. 1  is a diagram showing an electrical configuration of a digital camera and detailed configuration of a digital signal processing unit according to a first embodiment; 
           [0021]      FIG. 2  is a flowchart of a skin tone improvement process according to the first embodiment; 
           [0022]      FIG. 3  is a diagram showing an example of low pass filters L 1  and L 2 ; 
           [0023]      FIG. 4  is a diagram showing an example of a function superimposed on an image signal in a coring process; 
           [0024]      FIG. 5A  is a diagram showing an example of an image including a large facial region; 
           [0025]      FIG. 5B  is a diagram showing an example of frequency characteristic of a luminance signal; 
           [0026]      FIG. 5C  is a diagram showing an example of a low pass filter to be used when a facial region is large; 
           [0027]      FIG. 5D  is a diagram showing, in an exemplary manner, a signal process to obtain a high-frequency differential signal; 
           [0028]      FIG. 5E  is a diagram showing an example of frequency characteristic of the high-frequency differential signal; 
           [0029]      FIG. 5F  is a diagram showing, in an exemplary manner, a signal process to obtain a final luminance signal; 
           [0030]      FIG. 5G  is a diagram showing an example of the image of  FIG. 5A  after the skin tone improvement process; 
           [0031]      FIG. 6A  is a diagram showing an example of an image including a small facial region; 
           [0032]      FIG. 6B  is a diagram showing an example of frequency characteristic of a luminance signal; 
           [0033]      FIG. 6C  is a diagram showing an example of a low pass filter to be used when a facial region is small; 
           [0034]      FIG. 6D  is a diagram showing, in an exemplary manner, a signal process to obtain a high-frequency differential signal; 
           [0035]      FIG. 6E  is a diagram showing an example of frequency characteristic of the high-frequency differential signal; 
           [0036]      FIG. 6F  is a diagram showing, in an exemplary manner, a signal process to obtain a final luminance signal; 
           [0037]      FIG. 6G  is a diagram showing an example of the image of  FIG. 6A  after the skin tone improvement process; 
           [0038]      FIG. 7  is a block diagram showing a digital signal processing unit according to a second embodiment; 
           [0039]      FIG. 8  is a flowchart of a skin tone improvement process according to the second embodiment; 
           [0040]      FIG. 9A  is a diagram showing an example of an image including a large facial region; 
           [0041]      FIG. 9B  is a diagram showing an example of frequency characteristic of a color-difference signal; 
           [0042]      FIG. 9C  is a diagram showing an example of a low pass filter to be used when a facial region is large; 
           [0043]      FIG. 9D  is a diagram showing, in an exemplary manner, a signal process to obtain a high-frequency differential signal; 
           [0044]      FIG. 9E  is a diagram showing an example of frequency characteristic of the high-frequency differential signal; 
           [0045]      FIG. 9F  is a diagram showing, in an exemplary manner, a signal process to obtain a final color-difference signal; 
           [0046]      FIG. 9G  is a diagram showing an example of the image of  FIG. 9A  after the skin tone improvement process; 
           [0047]      FIG. 10A  is a diagram showing an example of an image including a small facial region; 
           [0048]      FIG. 10B  is a diagram showing an example of frequency characteristic of a color-difference signal; 
           [0049]      FIG. 10C  is a diagram showing an example of a low pass filter to be used when a facial region is small; 
           [0050]      FIG. 10D  is a diagram showing, in an exemplary manner, a signal process to obtain a high-frequency differential signal; 
           [0051]      FIG. 10E  is a diagram showing an example of frequency characteristic of the high-frequency differential signal; 
           [0052]      FIG. 10F  is a diagram showing, in an exemplary manner, a signal process to obtain a final color-difference signal; 
           [0053]      FIG. 10G  is a diagram showing an example of the image of  FIG. 10A  after the skin tone improvement process; 
           [0054]      FIG. 11  is a block diagram showing a digital signal processing unit according to a third embodiment; 
           [0055]      FIG. 12  is a flowchart of a skin tone improvement process according to the third embodiment; 
           [0056]      FIG. 13  is a diagram showing an example of a weight table according to the third embodiment; 
           [0057]      FIG. 14A  is a diagram showing an example of an image including a large facial region; 
           [0058]      FIG. 14B  is a diagram showing an example of frequency characteristics of a luminance signal divided into three frequency bands; 
           [0059]      FIG. 14C  is a diagram showing an example of a weight table for an image including a large facial region; 
           [0060]      FIG. 15A  is a diagram showing an example of an image including a small facial region; 
           [0061]      FIG. 15B  is a diagram showing an example of frequency characteristics of a luminance signal divided into three frequency bands; 
           [0062]      FIG. 15C  is a diagram showing an example of a weight table for an image including a small facial region; 
           [0063]      FIG. 16  is a block diagram showing a digital signal processing unit according to a fourth embodiment; 
           [0064]      FIG. 17  is a flowchart of a skin tone improvement process according to the fourth embodiment; 
           [0065]      FIG. 18  is a diagram showing an example of a weight table according to the fourth embodiment; 
           [0066]      FIG. 19A  is a diagram showing an example of an image including a large facial region; 
           [0067]      FIG. 19B  is a diagram showing an example of frequency characteristics of a color-difference signal divided into three frequency bands; 
           [0068]      FIG. 19C  is a diagram showing an example of a weight table for an image including a large facial region; 
           [0069]      FIG. 20A  is a diagram showing an example of an image including a small facial region; 
           [0070]      FIG. 20B  is a diagram showing an example of frequency characteristics of a color-difference signal divided into three frequency bands; 
           [0071]      FIG. 20C  is a diagram showing an example of a weight table for an image including a small facial region; 
           [0072]      FIG. 21  is a block diagram showing a digital signal processing unit according to a fifth embodiment; 
           [0073]      FIG. 22  is a flowchart of a skin tone improvement process according to the fifth embodiment; 
           [0074]      FIG. 23  is a diagram showing an example of a face size determination table according to the fifth embodiment; 
           [0075]      FIG. 24  is a diagram showing an example of a weight table according to the fifth embodiment; 
           [0076]      FIG. 25  is a diagram showing an example of a parameterized weight table according to the fifth embodiment; 
           [0077]      FIG. 26  is a block diagram showing a digital signal processing unit according to a sixth embodiment; 
           [0078]      FIG. 27  is a flowchart of a skin tone improvement process according to the sixth embodiment; 
           [0079]      FIG. 28  is a diagram showing an example of a face size determination table according to the sixth embodiment; 
           [0080]      FIG. 29  is a diagram showing an example of a weight table according to the sixth embodiment; and 
           [0081]      FIG. 30  is a diagram showing an example of a parameterized weight table according to the sixth embodiment. 
       
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     First Embodiment 
       [0082]      FIG. 1  shows an electrical configuration of a digital camera  10 . As shown in  FIG. 1 , the digital camera  10  comprises: an optical unit  22  which includes a lens; a CCD  24  disposed behind an optical axis of the lens; an analog signal processing unit  26  which includes a correlated double sampling circuit (hereinafter referred to as a “CDS”); an analog/digital converter (hereinafter referred to as an “ADC”)  28  which converts an inputted analog signal into digital data; and an digital signal processing unit  30  which incorporates a line buffer of predetermined capacity, directly stores inputted digital image data in a predetermined area of a memory  72  described later, and performs various types of image processing on the digital image data. An output terminal of the CCD  24  is connected to an input terminal of the analog signal processing unit  26 , an output terminal of the analog signal processing unit  26  is connected to an input terminal of the ADC  28 , and an output terminal of the ADC  28  is connected to an input terminal of the digital signal processing unit  30 . 
         [0083]    A correlated double sampling process performed by the CDS involves obtaining accurate pixel data by finding the difference between the level of a feed-through component and level of a pixel signal component contained in the output signal of each pixel of a solid-state image pickup element to reduce noise (especially, thermal noise) and the like contained in an output signal of the solid-state image pickup element. 
         [0084]    Also, the digital camera  10  includes an LCD interface  42  which generates and supplies signals to an LCD  44  in order for the LCD  44  to display images from digital image data, menu screens, and the like; a CPU (central processing unit)  50  which controls operation of the digital camera  10  as a whole; a memory  72  made up of a VRAM (Video RAM) which stores digital image data mainly obtained by photography; a memory interface  70  which controls access to the memory  72 ; an external memory interface  80  which allows the digital camera  10  to access a memory card  82  made up of Smart Media(trademark), and a compression/decompression circuit  86  which compresses digital image data in a predetermined compression format and decompresses compressed digital image data according to the compression format of the given digital image data. 
         [0085]    The digital signal processing unit  30 , LCD interface  42 , CPU  50 , memory interface  70 , external memory interface  80 , and compression/decompression circuit  86  are interconnected via a system bus BUS. Thus, the CPU  50  can control operation of the digital signal processing unit  30  and compression/decompression circuit  86 , display various types of information on the LCD  44  via the LCD interface  42 , and access the memory  72  and memory card  82  via the memory interface  70  and external memory interface  80 , respectively. 
         [0086]    Also, the digital camera  10  includes a timing generator  32  which generates a timing signal mainly used to drive the CCD  24  and supplies the timing signal to the CCD  24 , which is driven by the timing generator  32  under the control of the CPU  50 . 
         [0087]    Furthermore, the digital camera  10  includes a motor drive unit  34  which drives a focus adjustment motor, zoom motor, and diaphragm drive motor of the optical unit  22  under the control of the CPU  50 . 
         [0088]    That is, a lens  21  of the optical unit  22  according to the present embodiment is a zoom lens, which, being made up of multiple lenses, is capable of changing focal length (zooming) and equipped with a lens drive mechanism (not shown). The focus adjustment motor, zoom motor, and diaphragm drive motor are included in the lens drive mechanism and driven by a drive signal supplied from the motor drive unit  34 , under the control of the CPU  50 . 
         [0089]    To change an optical zoom factor, the CPU  50  changes the focal length of the lens  21  included in the optical unit  22  by controlling driving of the zoom motor. 
         [0090]    Also, the CPU  50  performs focus control by controlling driving of the focus adjustment motor to maximize contrast value of an image obtained through image pickup by the CCD  24 . That is, the digital camera  10  according to the present embodiment uses a so-called TTL (Through The Lens) metering for focus control, where the TTL metering involves setting position of the lens so as to maximize the contrast of the captured image. 
         [0091]    Furthermore, the CPU  50  is connected with various switches and buttons including a release button, power switch, mode selector switch, cross-key pad, forced-flash button (collectively referred to as “control unit  90 ”). The CPU  50  keeps track of operating status of the control unit  90 . 
         [0092]    Also, the digital camera  10  includes a charging unit  60  which, being interposed between flash unit  62  and CPU  50 , charges the flash unit  62  for firing under the control of the CPU  50 . Furthermore, the flash unit  62  is also connected to the CPU  50 , which controls the firing of the flash unit  62 . 
         [0093]    A lens drive function of the optical unit  22 , the CCD  24 , the timing generator  32 , and the motor drive unit  34  correspond to the image pickup device according to the present invention; the analog signal processing unit  26 , ADC  28 , and digital signal processing unit  30  correspond to the signal processing device according to the present invention; the flash unit  62  corresponds to the flashing device according to the present invention; the charging unit  60  corresponds to the charging device according to the present invention; and the CPU  50  corresponds to the intermittent operating device according to the present invention. 
         [0094]    A face detecting unit  91  identifies a facial region in digital image data in the memory  72 , where the facial region contains the facial portion of a person. Available methods for detecting the facial region include, for example, a technique disclosed in Japanese Patent Application Laid-Open No. 2007-124112 filed by the present inventor. 
         [0095]    That is, the face detecting unit  91  reads image data P 0 ′ of a photographic image and detects a facial portion P 0   f′  in the image P 0 ′. Specifically, as described in Japanese Patent Application Laid-Open No. 2005-108195, it is conceivable to input first feature values which represent directions of gradient vectors which in turn represent the directions and magnitudes of edges of pixels in the image P 0 ′ into a plurality of first classifiers and thereby determine whether there is a candidate for a facial region in the image P 0 ′, extract any candidate for a facial region, normalize the magnitudes of the gradient vectors of the pixels in the extracted candidate for a facial region, input second feature values which represent the magnitudes and directions of the normalized gradient vectors into second classifiers, and thereby determine whether the extracted candidate for a facial region is an actual facial region, and extract the region as a facial portion P 0   f′  if the region is determined to be an actual facial region. The first and second classifiers are obtained by a learning process using an AdaBoost or other machine learning technique which uses, as inputs, the first and second feature values calculated on an image-by-image basis for a plurality of images known to be faces capable of serving as learning samples and a plurality of images known to be non-faces. 
         [0096]    Available methods for detecting a facial portion P 1   f  include, flesh color detection, knowledge base, characteristic extraction, template matching, graph matching, statistical (neural network, SVM, or HMM), and other known techniques in addition to a method which uses correlation scores between intrinsic facial expressions and images Application No. 2004-527863 . 
         [0097]    As shown in  FIG. 1 , the digital signal processing unit  30  includes: a luminance/color-difference signal separating unit  30   a;  face size determining unit  30   b;  and luminance signal noise reduction processing unit  30   c.  These functions will be described later. 
         [0098]    Next, overall operation of the digital camera  10  during photography will be described briefly. 
         [0099]    Signals which represent a subject image outputted from the CCD  24  as a result of image pickup via the optical unit  22  is inputted in the analog signal processing unit  26  in sequence, subjected to an analog signal processing such as a correlated double sampling process, and inputted in the ADC  28 . The ADC  28  converts R (red), G (green), and B (blue) signals received from the analog signal processing unit  26  into 12-bit R, G, and B signals (digital image data) and outputs the digital signals to the digital signal processing unit  30 . 
         [0100]    The digital signal processing unit  30  accumulates the digital image data received in sequence from the ADC  28  in the built-in line buffer and once stores the digital image data in a predetermined area of the memory  72 . 
         [0101]    The digital image data stored in the predetermined area of the memory  72  is read out by the digital signal processing unit  30  under the control of the CPU  50 . Then, the digital image data is subjected to white balance adjustment, gamma processing, and sharpness processing and thereby converted into 8-bit digital image data, where to make the white balance adjustment, digital gain is applied according to predetermined physical quantities. Subsequently, a luminance signal Y and chroma signals Cr and Cb (hereinafter referred to as “Y/C signals”) are generated through Y/C signal processing and the Y/C signals are stored in an area of the memory  72  other than the predetermined area. 
         [0102]    Incidentally, the LCD  44  can be used as a viewfinder, being capable of displaying moving images (live view) obtained by continuous image pickup by the CCD  24 . When the LCD  44  is used as a viewfinder, the generated Y/C signals are outputted in sequence to the LCD  44  via the LCD interface  42 . Consequently, the live view is displayed on the LCD  44 . 
         [0103]    When a user half-presses the release button, an AE function makes exposure settings and an AF function controls focusing. Then, if the user full-presses the release button, the Y/C signals stored in the memory  72  at this time are compressed by the compression/decompression circuit  86  in a predetermined compression format (JPEG format, according to the present embodiment) and stored on the memory card  82  via the external memory interface  80 . Consequently, a photograph is taken. 
         [0104]    If the user has selected forced-flash mode using the forced-flash button, the CPU  50  fires the flash unit  62  forcibly at the time of photography. Even if the forced-flash mode has not been selected, if image information obtained via the CCD  24  indicates that a photometric level is lower than a predetermined level, the CPU  50  fires the flash unit  62 . 
         [0105]    If the flash unit  62  has not been charged sufficiently, the CPU  50  makes the charging unit  60  charge the flash unit  62  in preparation for firing in parallel with displaying live view on the LCD  44 . 
         [0106]    Next, a flow of a skin tone improvement process performed by the digital camera  10  will be described with reference to a flowchart in  FIG. 2 . 
         [0107]    In S 1 , the luminance/color-difference signal separating unit  30 a performs Y/C processing to convert R, G, and B data of an original photographic image outputted from the ADC  28  into the luminance signal Y and color-difference signals Cr and Cb. The luminance/color-difference signal separating unit  30   a  sends luminance signal Y to the luminance signal noise reduction processing unit  30   c.    
         [0108]    In S 2 , the face detecting unit  91  tries to detect a facial region. If a facial region is detected successfully, the flow goes to S 3 . 
         [0109]    In S 3 , the face size determining unit  30   b  acquires size of the facial region (i.e., face size) detected by the face detecting unit  91 . The face size determining unit  30   b  determines whether the size of the facial region is equal to or larger than a predetermined threshold (e.g., whether horizontal width of the detected facial region is equal to or larger than ⅛ the horizontal pixel width of the entire screen). If the size of the facial region is equal to or larger than the predetermined threshold, the flow goes to S 4 . Otherwise, the flow goes to S 5 . 
         [0110]    In S 4 , the luminance signal noise reduction processing unit  30   c  cuts a high-frequency noise component of the luminance signal Y using a first low pass filter L 1  (see  FIG. 3 ) and thereby generates a low-frequency luminance signal Y 1 . 
         [0111]    In S 5 , the luminance signal noise reduction processing unit  30   c  cuts a high-frequency noise component of the luminance signal Y using a second low pass filter L 2  (see  FIG. 3 ) and thereby generates a luminance signal Y 1  The second low pass filter L 2  has a narrower high-frequency cutoff range than the first low pass filter L 1 . 
         [0112]    In S 6 , the luminance signal noise reduction processing unit  30   c  subtracts the luminance signal Y 1  from the original luminance signal Y using a subtractor (not shown) and thereby extracts a high-frequency differential signal Yd. 
         [0113]    In S 7 , the luminance signal noise reduction processing unit  30   c  reduces noise in the differential signal Yd and thereby generates a high-frequency differential signal Y 2  with reduced noise. This is done, for example, using coring. That is, as shown in FIG.  4 , coring is the process of preventing passage of small-amplitude signals out of the differential signal Yd and involves producing an output by removing or suppressing signals of a smaller amplitude than a predetermined amplitude by regarding the small-amplitude signals as noise. 
         [0114]    In S 8 , the luminance signal noise reduction processing unit  30   c  combines the signal Y 1  and signal Y 2  using an adder (not shown) and thereby generates a final luminance signal Y 3 , which is made up of large-amplitude signals (which correspond to the background) left after small-amplitude signals (which correspond to blemishes and wrinkles) have been removed from the high-frequency component Yd of the original luminance signal Y. 
         [0115]    Concrete examples of image processing using the noise reduction process are explained using  FIGS. 5A to 6G .  FIGS. 5A to 5G  explain the process when a facial region is equal to or larger than a predetermined threshold and  FIGS. 6A to 6G  explain the process when the facial region is smaller than the predetermined threshold. 
         [0116]    First, a facial region is detected in an original photographic image IM 1  as shown in  FIG. 5A  and frequency characteristic of a luminance signal Y of the original photographic image IM 1  is obtained as shown in  FIG. 5B . If the size of the facial region in the original photographic image IM 1  is equal to or larger than the predetermined threshold, the low-frequency luminance signal Y 1  is obtained using the first low pass filter L 1  exemplified in  FIG. 5C . 
         [0117]    Next, as shown in  FIG. 5D , the low-frequency luminance signal Y 1  is subtracted from the original luminance signal Y to extract the high-frequency differential signal Yd.  FIG. 5E  exemplifies frequency characteristic of the high-frequency differential signal Yd. A noise reduction process such as coring is applied to the high-frequency differential signal Yd to obtain the high-frequency differential signal Y 2  with small-amplitude signals (i.e., only noise) reduced. Then, as shown in  FIG. 5F , the low-frequency luminance signal Y 1  and the differential signal Y 2  obtained by removing small-amplitude signals (i.e., only noise) are added to generate the final luminance signal Y 3 . Image data which includes Y 3 , Cr, and Cb is final image data IM 2  (shown in  FIG. 5G ) which has gone through the skin tone improvement process. 
         [0118]    On the other hand, if the size of the facial region in the original photographic image IM 1  (shown in  FIG. 6A ) is smaller than the predetermined threshold, the low-frequency luminance signal Y 1  is obtained by cutting only a high-frequency component of the luminance signal using the second low pass filter (LPF) L 2  exemplified in  FIG. 6C .  FIG. 6C  shows the LPF L 2  which is used when the facial region is small with a solid line and the LPF L 1  which is used when the facial region is large with a broken line. 
         [0119]    Next, as shown in  FIG. 6D , the low-frequency luminance signal Y 1  is subtracted from the original luminance signal Y to extract the high-frequency differential signal Yd.  FIG. 6E  exemplifies frequency characteristic of the high-frequency differential signal Yd.  FIG. 6E  shows the high-frequency differential signal Yd when the facial region is small with a solid line and that when the facial region is large with a broken line. 
         [0120]    A noise reduction process such as coring is applied to the high-frequency differential signal Yd to obtain the high-frequency differential signal Y 2  with small-amplitude signals (i.e., only noise) reduced. Then, as shown in  FIG. 6F , the low-frequency luminance signal Y 1  and the differential signal Y 2  are added to generate the final luminance signal Y 3 . Image data which includes Y 3 , Cr, and Cb is final image data IM 2  (shown in  FIG. 6G ) which has gone through the skin tone improvement process. 
         [0121]    Even if the same subject is photographed, wrinkles and blemishes on facial surfaces look different between a close shot and a long shot. Since details of the face are omitted when the face is reduced in size, fine wrinkles and blemishes recognized on a large face become less noticeable on a small face. 
         [0122]    Thus, the low pass filter L 2  used for small facial regions is designed to have a regions to extract the high-frequency component Yd in a narrower range. Varying a high-frequency component to be extracted depending on the size of the facial region results in a difference of effect of skin tone improvement between when a size of the face region is large and when it is small, even though the same noise reduction process is applied. 
         [0123]    The high-frequency differential signal Yd has a narrower frequency range when the facial region is small than when the facial region is large. However, since details of the face are omitted when the face is reduced in size and fine wrinkles and blemishes recognized on a large face become less noticeable on a small face as described above, noise reduction in such a narrow range has a sufficient effect. 
         [0124]    Also since the high-frequency differential signal Yd has a narrower frequency range when the facial region is small than when the facial region is large, details of the background which are conventionally lost unnecessarily remain even after the noise reduction. That is, the present embodiment can both improve skin tones and maintain background image quality. 
       Second Embodiment 
       [0125]      FIG. 7  shows a detailed configuration of the digital signal processing unit  30  in the digital camera  10  according to a second embodiment. The digital signal processing unit  30  according to the second embodiment includes a color-difference signal noise reduction processing unit  30 d instead of the luminance signal noise reduction processing unit  30   c,  compared with the configuration in the first embodiment. The same components as those in the other embodiments are designated by the same reference numerals as the corresponding components in the other embodiments. 
         [0126]      FIG. 8  illustrates a flow of a skin tone improvement process performed by the digital camera  10  according to the second embodiment. 
         [0127]    In S 11 , the luminance/color-difference signal separating unit  30   a  performs Y/C processing to convert R, G, and B data of an original photographic image outputted from the ADC  28  into the luminance signal Y and color-difference signals Cr and Cb (the color-difference signals will be designated collectively by C). The luminance/color-difference signal separating unit  30   a  sends the color-difference signal C to the color-difference signal noise reduction processing unit  30   d.    
         [0128]    In S 12 , the face detecting unit  91  tries to detect a facial region. If a facial region is detected successfully, the flow goes to S 13 . 
         [0129]    In S 13 , the face size determining unit  30   b  acquires size of the facial region based on the facial region detected by the face detecting unit  91 . The face size determining unit  30   b  determines whether the size of the facial region is equal to or larger than a predetermined threshold (e.g. whether, width of the detected facial region is equal to or larger than ⅛ the horizontal pixel width of the entire screen). If the size of the facial region is equal to or larger than the predetermined threshold, the flow goes to S 14 . Otherwise, the flow goes to S 15 . 
         [0130]    In S 14 , the color-difference signal noise reduction processing unit  30 d cuts a high-frequency noise component of the color-difference signal C using the first low pass filter L 1  (see  FIG. 9B ) and thereby generates a low-frequency color-difference signal C 1 . 
         [0131]    In S 15 , the color-difference signal noise reduction processing unit  30   d  cuts a high-frequency noise component of the color-difference signal C using the second low pass filter L 2  (see  FIG. 10B ) and thereby generates a low-frequency color-difference signal C 1 . The second low pass filter L 2  has a narrower high-frequency cutoff range than the first low pass filter L 1 . 
         [0132]    In S 16 , the color-difference signal noise reduction processing unit  30   d  subtracts the color-difference signal C 1  from the original color-difference signal C using a subtractor (not shown) and thereby extracts a high-frequency differential signal Cd. 
         [0133]    In S 17 , a noise reduction is applied to the differential signal Cd so as to generate a high-frequency differential signal C 2  with reduced noise. This is done, for example, using coring. That is, coring is a process for preventing passage of small-amplitude signals out of the differential signal Cd and involves producing an output by removing or suppressing signals of smaller amplitude than predetermined amplitude by regarding the small-amplitude signals as noise. 
         [0134]    In S 18 , the color-difference signal noise reduction processing unit  30   d  combines the color-difference signal C 1  and signal C 2  using an adder (not shown) and thereby generates a final color-difference signal C 3 , which is made up of large-amplitude signals (which correspond to the background) left after small-amplitude signals (which correspond to blemishes and wrinkles) have been removed from the high-frequency component Cd obtained by passing the original color-difference signal C through the filter L 1 . 
         [0135]    Concrete examples of image processing using the noise reduction process are shown in  FIGS. 9A to 10G , where  FIGS. 9A to 9G  assume that a facial region is equal to or larger than a predetermined threshold and  FIGS. 10A to 10G  assume that the facial region is smaller than the predetermined threshold. 
         [0136]    First, a facial region is detected in an original photographic image IM 1  as shown in  FIG. 9A  and frequency characteristic of a color-difference signal C of the original photographic image IM 1  is obtained as shown in  FIG. 9B . If the size of the facial region in the original photographic image IM 1  is equal to or larger than the predetermined threshold, the low-frequency color-difference signal C 1  is obtained by cutting a high-frequency component of the color-difference signal using a first low pass filter L 1  exemplified in  FIG. 9C . 
         [0137]    Next, as shown in  FIG. 9D , the color-difference signal C 1  is subtracted from the original color-difference signal C to extract the high-frequency differential signal Cd.  FIG. 9E  exemplifies frequency characteristic of the high-frequency differential signal Cd. A noise reduction process such as coring is applied to the high-frequency differential signal Cd to obtain the high-frequency differential signal C 2  with reduced noise. Then, as shown in  FIG. 9F , the low-frequency color-difference signal C 1  and the differential signal C 2  are added to generate the final color-difference signal C 3 . Image data which includes C and Y is final image data IM 2  (shown in  FIG. 9G ) which has gone through the skin tone improvement process. 
         [0138]    On the other hand, if the size of a facial region in the original photographic image IM 1  is smaller than the predetermined threshold as shown in  FIG. 10A , the low-frequency color-difference signal C 1  is obtained by cutting a high-frequency component of the color-difference signal using a second low pass filter L 2  exemplified in  FIG. 10C .  FIG. 10C  shows the LPF L 2  which is used when the facial region is small with a solid line and the LPF L 1  which is used when the facial region is large with a broken line. 
         [0139]    Next, as shown in  FIG. 10D , the color-difference signal C 1  is subtracted from the original color-difference signal C to extract the high-frequency differential signal Cd.  FIG. 10E  exemplifies frequency characteristics of the high-frequency differential signal Cd.  FIG. 10E  shows the high-frequency differential signal Cd when the facial region is small with a solid line and that when the facial region is large with a broken line. 
         [0140]    A noise reduction process such as coring is applied to the high-frequency differential signal Cd to obtain the high-frequency differential signal C 2  with reduced noise. Then, as shown in  FIG. 10F , the low-frequency color-difference signal C 1  and the differential signal C 2  are added to generate the final color-difference signal C 3 . Image data which includes C and Y is final image data IM 2  (shown in  FIG. 10G ) which has gone through the skin tone improvement process. 
         [0141]    Even if the same subject is photographed, color irregularities appear on facial surfaces differently between a close shot and long shot. Color irregularities are less noticeable on a small face, which has a smaller skin area. Hence, it is desirable to change noise reduction effect also on the color-difference signal depending on the size of the facial region. 
         [0142]    Thus, the low pass filter L 2  used for small facial regions is designed to have a wider high-frequency cutoff range than the low pass filter L 1  used for large facial regions to extract the high-frequency component Cd in a narrower range. Varying a high-frequency component to be extracted depending on the size of the facial region results in a difference of effect of skin tone improvement between when a size of the face region is large and when it is small, even though the same noise reduction process is applied. 
         [0143]    The high-frequency differential signal Cd has a narrower frequency range when the facial region is small than when the facial region is large as shown in  FIG. 10F . However, since details of the face are omitted and color irregularities recognized on a large face become less noticeable on a small face when the size of the facial region is small as described above, noise reduction in such a narrow range has a sufficient effect. In addition, when the size of the facial region is small, only a simple noise reduction process will serve the purpose unlike conventional noise reduction, making it possible to prevent color bleeding and blurring in the background. That is, the present embodiment can both improve skin tones and maintain background image quality. 
       Third Embodiment 
       [0144]      FIG. 11  shows a detailed configuration of the digital signal processing unit  30  in the digital camera  10  according to a third embodiment. The digital signal processing unit  30  includes a luminance signal frequency splitting unit  30   e  and frequency band-specific luminance signal noise reduction processing unit  30   f  as well as the luminance/color-difference signal separating unit  30   a  and the face size determining unit  30   b.  The same components as those in the other embodiments are designated by the same reference numerals as the corresponding components in the other embodiments. 
         [0145]      FIG. 12  illustrates a flow of a skin tone improvement process performed by the digital camera  10  according to the third embodiment. 
         [0146]    S 21  is the same as S 1 . 
         [0147]    In S 22 , the luminance signal Y is divided into a plurality of frequency bands (e.g., three bands: high, medium, and low). 
         [0148]    In S 23 , the frequency band-specific luminance signal noise reduction processing unit  30   f  extracts from the luminance signal Y, a frequency component each of which corresponds to each of the frequency bands (e.g., three bands: high, medium, and low). 
         [0149]    In S 24 , as in S 2 , it is determined whether a facial region has been detected successfully. If it is determined that a facial region has been detected successfully, the flow goes to S 25 . 
         [0150]    In S 25 , the face size determining unit  30   b  determines whether the size of the facial region is equal to or larger than a predetermined threshold (e.g., whether width of the detected facial region is equal to or larger than ⅛ the horizontal pixel width of the entire screen). If the size of the facial region is equal to or larger than the predetermined threshold, the flow goes to S 26 . Otherwise, the flow goes to S 27 . 
         [0151]      FIG. 13  shows an example of a weight table according to the third embodiment. The example shown in  FIG. 13  is a weight table which is used when the luminance signal Y is divided into three frequency bands. In the weight table, a noise reduction weight is assigned to each of the frequency bands according to a size of a facial region. 
         [0152]    In S 26 , each of the frequency components of the luminance signal Y is subjected to a first noise reduction which is performed when the face is large. For example, as shown in a weight table in  FIG. 13 , the component in the high frequency band is subjected to a process with a high noise reduction effect (e.g., by cutting a wide frequency band by coring),the component in the medium frequency band is subjected to a process with a high noise reduction effect, and the component in the low frequency band is subjected to a process with a high noise reduction effect. Thus, the noise reduction weight corresponding to each of frequency bands is determined and each of frequency components is subjected to an appropriate weighted noise reduction process depending on the determined weight so as to remove only the frequency components which correspond to blemishes and wrinkles from the luminance signal Y. 
         [0153]    In S 27 , each of the frequency components of the luminance signal Y is subjected to a second noise reduction which is performed when the face is small. For example, as shown in a weight table in  FIG. 13 , the component in the high frequency band is subjected to a process with a high noise reduction effect (e.g., by cutting a wide frequency band by coring), the component in the medium frequency band is subjected to a process a medium noise reduction effect (e.g., by cutting a moderately wide frequency band by coring), and the component in the low frequency band is subjected to a process with a low noise reduction effect (e.g., by cutting a narrow frequency band by coring). Thus, the noise reduction weight corresponding to each of frequency bands is determined and each of frequency components is subjected to an appropriate weighted noise reduction process depending on the determined weight so as to remove only the frequency components which correspond to blemishes and wrinkles from the luminance signal Y. 
         [0154]    Concrete examples of image processing using the noise reduction process are shown in  FIGS. 14A to 15C , where a frequency band of a luminance signal is divided into three.  FIGS. 14A to 14C  assume that a facial region is equal to or larger than a predetermined threshold and  FIGS. 15A to 15C  assume that the facial region is smaller than the predetermined threshold. 
         [0155]    First, as shown in  FIG. 14B , a luminance signal Y is extracted from an original photographic image IM 1  (shown in  FIG. 14A ) and divided into three frequency bands: high, medium, and low using a plurality of LPFs. Next, with small-amplitude component in each frequency band being regarded as noise (blemishes and wrinkles), a weight for coring (i.e., coring weight) is assigned to each frequency band according to the table in  FIG. 13  (and  FIG. 14C ) and a noise reduction process is applied to each frequency band using the coring weight. 
         [0156]    As shown in  FIG. 15B , the luminance signal Y is extracted from the original photographic image IM 1  (shown in  FIG. 15A ) and divided into three frequency bands: high, medium, and low using a plurality of LPFs. Then, with small-amplitude component in each frequency band being regarded as noise (blemishes and wrinkles), a coring weight is assigned to each frequency band according to the table in  FIG. 13  (and  FIG. 15C ) and a noise reduction process is applied to each frequency band using the coring weight. 
         [0157]    Medium- to high-frequency components of winkles and blemishes(noise components) existing on a large face will shift to high-frequency side as a size of the facial region decreases as shown in  FIG. 15B . This is because the noise components which are distinct at high resolution become fuzzy when resolution of the facial region decreases, i.e., frequency characteristics on a surface of the facial region constituting the image gather on the low-frequency side as the facial region becomes small. 
         [0158]    When the facial region is small, noise reduction in the high-frequency band is enough to achieve desired effect and noise reduction in the low-frequency band does not make much sense. Thus, when the facial region is small, a particularly great weight is assigned to the noise reduction in the high-frequency band. 
         [0159]    In this way, as the frequency band of the luminance signal targeted for noise reduction is changed depending on the size of the facial region, it is possible to achieve higher noise reduction effect. 
       Fourth Embodiment 
       [0160]      FIG. 16  shows a detailed configuration of the digital signal processing unit  30  in the digital camera  10  according to a fourth embodiment. The digital signal processing unit  30  includes a color-difference signal frequency splitting unit  30   g  and frequency band-specific color-difference signal noise reduction processing unit  30   h  as well as the luminance/color-difference signal separating unit  30   a  and the face size determining unit  30   b.  The same components as those in the other embodiments are designated by the same reference numerals as the corresponding components in the other embodiments. 
         [0161]    In the third embodiment, a luminance signal is used for the skin tone improvement process. On the other hand, in the fourth embodiment, a color-difference signal is used for the skin tone improvement process in stead of the luminance signal. 
         [0162]      FIG. 17  illustrates a flow of a skin tone improvement process performed by the digital camera  10  according to the fourth embodiment. 
         [0163]    S 31  and S 32  are the same as S 1  and S 22 , respectively. 
         [0164]    In S 33 , with the color-difference signal C being divided into a plurality of frequency bands (e.g., three bands: “high”, “medium”, and “low”), the frequency band-specific color-difference signal noise reduction processing unit  30   h  extracts a frequency component which corresponds to each frequency band from the color-difference signal C. 
         [0165]    In S 34 , as in S 2 , it is determined whether a facial region has been detected successfully. If it is determined that a facial region has been detected successfully, the flow goes to S 35 . 
         [0166]    In S 35 , the face size determining unit  30   b  determines whether the size of the facial region is equal to or larger than a predetermined threshold (e.g., whether a width of the detected facial region is equal to or larger than ⅛ the horizontal pixel width of the entire screen). If the size of the facial region is equal to or larger than the predetermined threshold, the flow goes to S 36 . Otherwise, the flow goes to S 37 . 
         [0167]      FIG. 18  shows an example of a weight table according to the fourth embodiment. The example shown in  FIG. 18  is a weight table which is used when the color-difference signal C is divided into three frequency bands. In the weight table, a noise reduction weight is assigned to each frequency band according to a size of a facial region. 
         [0168]    In S 36 , each of the frequency components of the color-difference signal C is subjected to a first noise reduction which is performed when the face is large. For example, as shown in a weight table in  FIG. 1   8 , the component in the high frequency band is subjected to a process with a high noise reduction effect (e.g., by cutting a wide frequency band by coring), the component in the medium frequency band is subjected to a process with a high noise reduction effect, and the component in the low frequency band is subjected to a process with a high noise reduction effect. Thus, the noise reduction weight corresponding to each of frequency bands is determined and each of frequency components is subjected to an appropriate weighted noise reduction process depending on the determined weight. 
         [0169]    In S 37 , the frequency components of the color-difference signal C are subjected to second noise reduction which is performed when the face is small. For example, as shown in a weight table in  FIG. 18 , the component in the high frequency band is subjected to a process with a high noise reduction effect (e.g., by cutting a wide frequency band by coring), the component in the medium frequency band is subjected to a process a medium noise reduction effect (e.g., by cutting a moderately wide frequency band by coring), and the component in the low frequency band is subjected to a process with a low noise reduction effect (e.g., by cutting a narrow frequency band by coring). Thus, the noise reduction weight corresponding to each of frequency bands is determined and each of the frequency components is subjected to an appropriate weighted noise reduction process depending on the determined weight. 
         [0170]    Concrete examples of image processing using the noise reduction process are shown in  FIGS. 19A to 20C , where a frequency band of a luminance signal is divided into three.  FIGS. 19A to 19C  assume that a facial region is equal to or larger than a predetermined threshold and  FIGS. 20A to 20C  assume that the facial region is smaller than the predetermined threshold. 
         [0171]    As shown in  FIG. 19B , the color-difference signal C is extracted from the original photographic image IM 1  (shown in  FIG. 19A ) and divided into three frequency bands: high, medium, and low using a plurality of LPFs. Next, with small-amplitude components in each frequency band being regarded as noise (blemishes and wrinkles), a coring weight is assigned to each frequency band according to the table in  FIG. 18  (and  FIG. 19C ) and a noise reduction process is applied to each frequency band. 
         [0172]    As shown in  FIG. 20B , the color-difference signal C is extracted from the original photographic image IM 1  (shown in  FIG. 20A ) and divided into three frequency bands: high, medium, and low using a plurality of LPFs. Next, with small-amplitude components in each frequency band being regarded as noise (blemishes and wrinkles), a coring weight is assigned to each frequency band according to the table in  FIG. 18  (and  FIG. 20C ) and a noise reduction process is applied to each frequency band. 
         [0173]    Medium- to high-frequency components of winkles and blemishes (noise components) existing on a large face will shift to high-frequency side as a size of the facial region decreases as shown in  FIG. 20B . This is because the noise components which are distinct at high resolution become fuzzy when resolution of the facial region decreases, i.e., frequency characteristics on a surface of the facial region constituting the image gather on the low-frequency side as the facial region becomes small. 
         [0174]    When the facial region is small, noise reduction in the high-frequency band is enough to achieve desired effect and noise reduction in the low-frequency band does not make much sense. Thus, when the facial region is small, a particularly great weight is assigned to the noise reduction in the high-frequency band. 
         [0175]    In this way, as the frequency band of the color-difference signal targeted for noise reduction is changed according to the size of the facial region, it is possible to achieve higher noise reduction effect. 
       Fifth Embodiment 
       [0176]      FIG. 21  shows a detailed configuration of the digital signal processing unit  30  in the digital camera  10  according to a fifth embodiment. 
         [0177]    As shown in  FIG. 21 , the digital signal processing unit  30  according to the fifth embodiment includes: luminance/color-difference signal separating unit  30   a;  face size determining unit  30   b;  luminance signal frequency splitting unit  30   e;  and frequency band-specific luminance signal noise reduction processing unit  30   f.  The same components as those in the other embodiments are designated by the same reference numerals as the corresponding components in the other embodiments. 
         [0178]      FIG. 22  illustrates a flow of a skin tone improvement process performed by the digital camera  10  according to the fifth embodiment. 
         [0179]    S 41  to S 44  are the same as S 21  to  824 . 
         [0180]    In S 45 , it is determined whether a facial region is “large” or not.  FIG. 23  shows an example of a face size determination table in which the determination criteria are prescribed according to a ratio of a horizontal width of a facial region to that of an entire image. For example, the facial region is determined to be large according to a face size determination table in  FIG. 23  if a ratio of a horizontal width of the facial region to that of the entire image is equal to or larger than ⅞. If it is determined that the facial region is large, the flow goes to S 46 . 
         [0181]    In S 46 , the frequency band-specific luminance signal noise reduction processing unit  30   f  determines weights for frequency bands of the luminance signal Y according to rules used when the size of the facial region is “large” and performs a noise reduction process on the frequency bands according to the determined weights.  FIG. 24  shows an example of a weight table which prescribes weights according to a size of a facial region In S 46 , weights used when the size of the facial region is “large” are assigned: specifically, “large” weights are assigned to all the high, medium, and low frequency bands. This is the same as in S 26 . 
         [0182]    In S 47 , it is determined whether the size of the facial region is “small” or not. For example, if the width of the facial region is smaller than ⅛ the horizontal pixel width of the entire screen, it is determined based on the face size determination table in  FIG. 23  that the facial region is small, and the flow goes to S 48 . Otherwise, the flow goes to S 49 . 
         [0183]    In  848 , the frequency band-specific luminance signal noise reduction processing unit  30   f  determines weights for frequency bands of the luminance signal Y according to rules used when the size of the facial region is “small” and performs a noise reduction process on the frequency bands according to the determined weights. 
         [0184]      FIG. 24  shows an example of a weight table which prescribes weights according to the size of the facial region. In S 48 , weights used when the size of the facial region is “small” are assigned: specifically, a “large” weight is assigned to the high frequency band, a “medium” weight is assigned to the medium frequency band, and a “small” weight is assigned to the low frequency band. This is the same as in S 27 . 
         [0185]    In S 49 , weights assigned to the frequency bands when the size of the facial region is “medium” (that is, the size of the facial region is intermediate between the threshold for “larger” and the threshold for “small”) are determined by linear interpolation from the weights assigned to the frequency bands when the size of the facial region is “large” and the weights assigned to the frequency bands when the size of the facial region is “small.” 
         [0186]    For example,  FIG. 25  generally shows parameters used for such linear interpolation, where weights for the high, medium, and low frequency bands of the luminance signal Y are given by parameters “Y_BL_H,” “Y_BL_M,” and “Y_BL_L” when the size of the facial region is “large” and weights for the high, medium, and low frequency bands are given by parameters “Y_BS_H,” “Y_BS_M,” and “Y_BS_L” when the size of the facial region is “small.” In this case, weights “Y_BX_H,” “Y_BX_M,” and “Y_BX_L” for the high, medium, and low frequency bands are calculated using respective linear interpolation formulas as follows.
   Parameter for high frequency band:   
 
         [0000]    
       
         
           
             
               Y_BX 
                
               _H 
             
             = 
             
               
                 ( 
                 
                   
                     Y_BL 
                      
                     _H 
                   
                   - 
                   
                     Y_BS 
                      
                     _H 
                   
                 
                 ) 
               
               × 
               
                 
                   X 
                   - 
                   S 
                 
                 
                   L 
                   - 
                   S 
                 
               
             
           
         
       
       
         Parameter for medium frequency band: 
       
     
         [0000]    
       
         
           
             
               Y_BX 
                
               _M 
             
             = 
             
               
                 ( 
                 
                   
                     Y_BL 
                      
                     _M 
                   
                   - 
                   
                     Y_BS 
                      
                     _M 
                   
                 
                 ) 
               
               × 
               
                 
                   X 
                   - 
                   S 
                 
                 
                   L 
                   - 
                   S 
                 
               
             
           
         
       
       
         Parameter for low frequency band: 
       
     
         [0000]    
       
         
           
             
               Y_BX 
                
               _L 
             
             = 
             
               
                 ( 
                 
                   
                     Y_BL 
                      
                     _L 
                   
                   - 
                   
                     Y_BS 
                      
                     _L 
                   
                 
                 ) 
               
               × 
               
                 
                   X 
                   - 
                   S 
                 
                 
                   L 
                   - 
                   S 
                 
               
             
           
         
       
     
         [0190]    where: 
         [0191]    L denotes a pixel size of the facial region determined to be large (equal to or larger than ⅞ of the entire image); 
         [0192]    S denotes a pixel size of the facial region determined to be small (smaller than ⅛ of the entire image); and 
         [0193]    X denotes a pixel size of the facial region determined to be medium. 
         [0194]    Thus, appropriate noise reduction can be applied to the luminance signal Y according to frequency bands even when the size of the face is medium, making it possible, when the size of the face is neither large nor small, to avoid using inappropriate parameters for noise reduction, which could result in a phenomenon known as hunting. 
       Sixth Embodiment 
       [0195]      FIG. 26  shows a detailed configuration of the digital signal processing unit  30  in the digital camera  10  according to a sixth embodiment. 
         [0196]    As shown in  FIG. 26 , the digital signal processing unit  30  according to the sixth embodiment includes: luminance/color-difference signal separating unit  30   a;  face size determining unit  30   b;  color-difference signal frequency splitting unit  30   g;  and frequency band-specific color-difference signal noise reduction processing unit  30   h.  The same components as those in the other embodiments are designated by the same reference numerals as the corresponding components in the other embodiments. 
         [0197]    In the fifth embodiment, a luminance signal is used for the skin tone improvement process. On the other hand, in the sixth embodiment, a color-difference signal is used for the skin tone improvement process in stead of the luminance signal. 
         [0198]      FIG. 27  illustrates a flow of a skin tone improvement process performed by the digital camera  10  according to the sixth embodiment. 
         [0199]    S 51  to S 54  are the same as S 31  to S 34 . 
         [0200]    In S 55 , it is determined whether a facial region is “large” or not  FIG. 28  shows an example of a face size determination table in which the determination criteria are prescribed according to a ratio of a horizontal width of a facial region to that of an entire image. For example, the facial region is determined to be large according to a face size determination table in  FIG. 28  if a ratio of a horizontal width of the facial region to that of the entire image is equal to or larger than ⅞. If it is determined that the facial region is large, the flow goes to S 56 . 
         [0201]    In S 56 , the frequency band-specific color-difference signal noise reduction processing unit  30   h  determines weights for frequency bands of the color-difference signal C according to rules used when the size of the facial region is “large” and performs a noise reduction process on the frequency bands according to the determined weights.  FIG. 29  shows an example of a weight table which prescribes weights according to a size of a facial region. In S 56 , weights used when the size of the facial region is “large” are assigned: specifically, “large” weights are assigned to all the high, medium, and low frequency bands. This is the same as in S 36 . 
         [0202]    In S 57 , it is determined whether the size of the facial region is “small” or not. For example, if the width of the facial region is smaller than ⅛ the horizontal pixel width of the entire screen, it is determined based on the face size determination table in  FIG. 28  that the facial region is small, and the flow goes to S 58 . Otherwise, the flow goes to S 59 . 
         [0203]    In S 58 , the frequency band-specific color-difference signal noise reduction processing unit  30   h  determines weights for frequency bands of the color-difference signal C according to rules used when the size of the facial region is “small” and performs a noise reduction process on the frequency bands according to the determined weights. 
         [0204]      FIG. 29  shows an example of a weight table which prescribes weights according to the size of the facial region. In S 58 , weights used when the size of the facial region is “small” are assigned: specifically, a “large” weight is assigned to the high frequency band, a “medium” weight is assigned to the medium frequency band, and a “small” weight is assigned to the low frequency band. This is the same as in S 37 . 
         [0205]    In S 59 , weights assigned to the frequency bands when the size of the facial region is “medium” (that is, the size of the facial region is intermediate between the threshold for “larger” and the threshold for “small”) are determined by linear interpolation from the weights assigned to the frequency bands when the size of the facial region is “large” and the weights assigned to the frequency bands when the size of the facial region is “small.” 
         [0206]    For example,  FIG. 30  generally shows parameters used for such linear interpolation, where weights for the high, medium, and low frequency bands of the color-difference signal C are given by parameters “C_BL_H,” “C_BL_M” and “C_BL_L” when the size of the facial region is “large” and weights for the high, medium, and low frequency bands are given by parameters “C_BS_H,” “C_BS_M,” and “C_BS_S_L” when the size of the facial region is “small.” In this case, weights “C_BX_H,” “C_BX_M,” and “C_BX_L” for the high, medium, and low frequency bands are calculated using respective linear interpolation formulas as follows.
   Parameter for high frequency band:   
 
         [0000]    
       
         
           
             
               C_BX 
                
               _H 
             
             = 
             
               
                 ( 
                 
                   
                     C_BL 
                      
                     _H 
                   
                   - 
                   
                     C_BS 
                      
                     _H 
                   
                 
                 ) 
               
               × 
               
                 
                   X 
                   - 
                   S 
                 
                 
                   L 
                   - 
                   S 
                 
               
             
           
         
       
       
         Parameter for medium frequency band: 
       
     
         [0000]    
       
         
           
             
               C_BX 
                
               _M 
             
             = 
             
               
                 ( 
                 
                   
                     C_BL 
                      
                     _M 
                   
                   - 
                   
                     C_BS 
                      
                     _M 
                   
                 
                 ) 
               
               × 
               
                 
                   X 
                   - 
                   S 
                 
                 
                   L 
                   - 
                   S 
                 
               
             
           
         
       
       
         Parameter for low frequency band: 
       
     
         [0000]    
       
         
           
             
               C_BX 
                
               _L 
             
             = 
             
               
                 ( 
                 
                   
                     C_BL 
                      
                     _L 
                   
                   - 
                   
                     C_BS 
                      
                     _L 
                   
                 
                 ) 
               
               × 
               
                 
                   X 
                   - 
                   S 
                 
                 
                   L 
                   - 
                   S 
                 
               
             
           
         
       
     
         [0210]    where: 
         [0211]    L denotes a pixel size of the facial region determined to be large (equal to or larger than ⅞ of the entire image); 
         [0212]    S denotes a pixel size of the facial region determined to be small (smaller than ⅛ of the entire image); and 
         [0213]    X denotes a pixel size of the facial region determined to be medium. 
         [0214]    Thus, appropriate noise reduction can be applied to the color-difference signal C according to frequency bands even when the size of the face is medium, making it possible, when the size of the face is neither large nor small, to avoid the use of inappropriate parameters for noise reduction, which could result in a phenomenon known as hunting. 
         [0215]    While embodiments of the present invention have been explained in detail, the present invention is not limited to the above examples, and, needless to say, various improvements and modifications may be added without departing from the scope of the present invention. 
         [0216]    For example, in the fifth and sixth embodiments, weights for large face and small face are given for each of frequency bands, a luminance signal or a color-difference signal is divided according to the frequency bands, and noise reduction process is applied to each signal using one of the weight for a large face, the weight for a small face or an interpolated weight for a medium size face, depending on a size of a facial region. However, in a modified embodiment, weights for large face and small face may be given for only high-frequency band. And, a high-frequency component is extracted from a luminance signal or a color-difference signal, and the noise reduction process is applied to the extracted high-frequency component of the signal using any one of a weight for a large face, a weight for a small face or an interpolated weight for a medium size face depending on a size of a facial region included in an original image.