Abstract:
The purpose of the present invention is to provide image processing technology that can reduce shimmering in the entirety of images that include both still regions and moving bodies. This image processing device finds gradient distribution for each of a frame to be corrected and a frame for correction, changes, according to the degree of similarity in the gradient distribution, the proportion in which the frame to be corrected and the frame for correction are used, and then corrects the frame to be corrected using the frame for correction (see FIG.  1 ).

Description:
TECHNICAL FIELD 
     The present invention relates to an image processing technique for reducing degrades in image quality due to such as heat hazes. 
     BACKGROUND ART 
     In recent years, in order to achieve safe and secure society, there are increasing needs for monitoring cameras that watch local societies on behalf of human eyes. It is important for monitoring cameras to improve visibility of videos. Image correction techniques such as blurring correction, gradation correction, or disturbance (rain, fog, smog, yellow sand, etc.) correction have been developed so far. 
     Heat haze is one of phenomena that reduces visibility of monitoring camera videos. Heat haze is a natural phenomenon that occurs from optical refraction that is caused by portions of atmosphere with different local densities mixed together. Heat haze is likely to be monitored when imaging asphalt of roads or car roofs using telescopic lens in high temperature days. When heat haze occurs, the imaged subject is monitored with deformed shapes. Accordingly, when reproducing the captured heat haze video, a specific portion in the captured image seems significantly wavered. Therefore, the visibility of the imaged subject is significantly reduced. 
     Patent Literature 1 listed below: determines whether a distortion occurs due to air fluctuation; if it is determined that the distortion due to air fluctuation occurs, creates a plurality of images by consecutive shooting; and adds up (averages) the plurality of images, thereby creating an image with the distortion being corrected. 
     CITATION LIST 
     Patent Literature 
     Patent Literature 1: JP Patent Publication (Kokai) 2012-182625 A 
     SUMMARY OF INVENTION 
     Technical Problem 
     However, if a plurality of past images captured by consecutive shooting is added (averaged), the image may be significantly degraded when the image includes a moving object, because the moving object could be imaged as a double image. In addition, no technique has been developed for correcting image distortions due to heat haze across the image including both a static region and a moving object. It is an important technical problem in the image correction field to establish a technique for correcting such image distortions. 
     The present invention is made in the light of the above-mentioned technical problem. It is an objective of the present invention to provide an image processing technique that can reduce heat haze across the image including both a static region and a moving object. 
     Solution to Problem 
     An image processing device according to the present invention: calculates a gradation distribution of a corrected frame and of a correcting frame respectively; changes a usage ratio between the corrected frame and the correcting frame; and corrects the corrected frame using the correcting frame. 
     Advantageous Effects of Invention 
     With the image processing device according to the present invention, it is possible to provide an image with high quality in which heat hazes across the image including both a static region and a moving object are reduced. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a functional block diagram of an image processing device  1  according to an embodiment 1. 
         FIG. 2  is a diagram showing an operational example of an image smoother  11 . 
         FIG. 3  is a diagram showing that a gradation distribution calculator  12  calculates a gradation distribution using pixels of peripheral regions around a target pixel. 
         FIG. 4  is a diagram showing that a histogram H 2  changes depending on conditions of moving objects included in past, current, and future images. 
         FIG. 5  is a diagram showing an operation of an image corrector  14 . 
         FIG. 6  is an example of a sharping filter used by a resolution enhancer  15 . 
         FIG. 7  is a flowchart showing an operation of the image processing device  1 . 
         FIG. 8  is a functional block diagram of the image processing device  1  according to an embodiment 2. 
         FIG. 9  is s diagram showing an operation of the gradation distribution calculator  12 . 
         FIG. 10  is a flowchart showing an operation of the image processing device  1 . 
         FIG. 11  is functional block diagram of the image processing device  1  according to an embodiment 3. 
         FIG. 12  is a diagram showing a processing example of a removal image creator  1111 . 
         FIG. 13  is a diagram showing a process of a similarity calculator  13  and of the image corrector  14 . 
         FIG. 14  is a flowchart showing an operation of the image processing device  1 . 
         FIG. 15  is a functional block diagram of an imaging device  1500  according to an embodiment 4. 
         FIG. 16  is a functional block diagram of a monitoring system  1600  according to an embodiment 5. 
         FIG. 17  is a functional block diagram of a coding-decoding system  1700  according to an embodiment 6. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     Hereinafter, embodiments of the present invention will be described with reference to Figures. A same reference sign is assigned to same components within each Figure. 
     Embodiment 1 
       FIG. 1  is a functional block diagram of an image processing device  1  according to an embodiment 1 of the present invention. The configuration of the image processing device  1  includes an inputter  10 , an image smoother  11 , a gradation distribution calculator  12 , a similarity calculator  13 , an image corrector  14 , a resolution enhancer  15 , a storage unit  16 , and a controller  91 . 
     The inputter  10  receives moving picture data captured by imaging means (not shown). For example, the inputter  10  may comprise image input ports or network connection ports and may be a tuner for TV broadcast. The inputter  10  may continuously acquire static image data of such as JPEG format, JPEG 2000 format, PNG format, or BMP format that is captured by imaging means such as monitoring cameras at a predetermined time interval, and may store a plurality of past, current, and future images as input images into a memory  90 . The inputter  10  may extract static image data from moving image data of such as MotionJPEG format, MPEG format, H.264 format, or HD/SD format, and may store a plurality of past, current, and future images as input images into the memory  90 . The inputter  10  can acquire a plurality of past, current, and future input images from the memory  90  using such as DMA (Direct Memory Access). The inputter  10  may be configured by imaging means that store a plurality of past, current, and future input images into the memory  90  through buses or through networks. The inputter  10  may, as described later, store a plurality of past, current, and future input images into the memory  90  that are previously stored in removable storage media. 
     When acquiring a plurality of past, current (time t), and future input images, the inputter  10  performs delay processing if necessary. The inputter  10  outputs, to the image smoother  11 , a current image and images before and after the current image. 
     The image smoother  11  synthesizes a plurality of past, current, and future input images chronologically, thereby creating a smoothed image corresponding to time t. An example for creating the smoothed image will be described later with reference to  FIG. 2 . 
     For each of the input image of time t and the smoothed image, the gradation distribution calculator  12  calculates gradation distributions of each image region which is centered at pixels of each image. This process is performed at each pixel for each of the input image of time t and the smoothed image. An example of the gradation distribution will be described later with reference to  FIG. 3 . 
     The similarity calculator  13  calculates a similarity between a gradation distribution of the input image at time t and of the smoothed image calculated by the gradation distribution calculator  12 . An example of the similarity between the gradation distributions will be described later with reference to  FIG. 4 . 
     The image corrector  14  corrects the input image of time t by synthesizing the input image of time t and the smoothed image. The synthesizing ratio between both images is modified depending on the similarity calculated by the similarity calculator  13 . The correction above creates a corrected image of time t with reduced heat haze. An example of the correction process will be described later with reference to  FIGS. 4-5 . 
     The resolution enhancer  15  creates an image with enhanced resolution of time t from the corrected image of time t. An example of resolution enhancement will be described later with reference to  FIG. 6 . 
     The storage unit  16  stores the image of time t with enhanced resolution when storing an image of time t with enhanced resolution. The storage unit  16  stores the input image when not storing an image of time t with enhanced resolution. 
     The storage unit  16  can switch the image to be stored into the memory depending on operational modes. For example, if the mode is 1, the storage unit  16  stores a high resolution image S created by the resolution enhancer  15  into the memory  90 , or alternatively outputs the high resolution image S to outside of the image processing device  1 . If the mode is 0, the storage unit  16  does not store the high resolution image S into the memory  90 , and outputs the input image to outside of the image processing device  1 . 
     The controller  91  is connected to each component in the image processing device  1 . Each of the components of the image processing device  1  operates autonomously, or alternatively operates under instruction of the controller  91 . 
       FIG. 2  is a diagram showing an operational example of the image smoother  11 .  FIG. 2  shows an example where smoothed images are created using past and future images (image N−2, image N−1, image N+1, image N+2) with an image N as a reference point and using the image N itself. 
     It is assumed that: a pixel value of the image N−2 is q1(i, j); a pixel value of the image N−1 is q2(i, j); a pixel value of the image N is q3(i, j); a pixel value of the image N+1 is q4(i, j); and a pixel value of the image N+2 is q5(i, j), each located at coordinate (i, j). It is also assumed that a pixel value of the smoothed image M at coordinate (i, j) is m(i, j). It is also assumed that p1-p5 are weight factors. The image smoother  11  creates, by synthesizing each of the images using Equation 1 below, the smoothed image M in which each of the images is smoothed chronologically. 
     
       
         
           
             
               
                 
                   
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     If there are five pieces of past, current, and future images for image synthesizing process, the parameter D in Equation 1 is 5. The pixel values in Equation 1 may be in any format. For example, the pixel values may be component values of three primary colors R (Red), G (Green), B (Blue) in RGB color space, or may be each component value of HSV color space (Hue, Saturation, Value). 
     It can be assumed that the displacement by which the imaged subject deforms due to heat haze follows Gaussian distribution statistically. Therefore, by smoothing pixels using a plurality of past and future images, it is possible to acquire an image in which the images subject has a shape close to its original shape. In addition, the reference image is reconfigured using past and future images within a limited range (In  FIG. 2 , D=5). Therefore, compared to a case smoothing the image by repeatedly synthesizing a plurality of past images for a long time span, it is possible to reduce significant effects from past images. Further, even if the input image includes a moving object, it is possible to create the smoothed image M that includes information being capable of reducing heat hazes of the moving object. 
       FIG. 3  is a diagram showing that the gradation distribution calculator  12  calculates a gradation distribution using pixels of peripheral regions around a target pixel. The gradation distribution calculator  12  calculates a histogram H 1  that represents a gradation distribution of an image region E 1  (e.g. 32×32 pixels) including a target pixel  310  of the input image N corresponding to time t. Similarly, the gradation distribution calculator  12  calculates a histogram H 2  that represents a gradation distribution of an image region E 2  corresponding to the image region E 1  in the smoothed image M. The horizontal axes of the histograms H 1  and H 2  represent gradation values, and the vertical axes represent frequency (number of appearance) of the gradation values. It applies to other histograms describes below. 
     When calculating the histograms H 1  and H 2 , the gradation distribution calculator  12  aggregates a plurality of gradation values within a same class (horizontal axis), thereby rounding the gradation values. Since image fluctuations within a same gradation range are included within a same class, the histogram with rounded gradation values has a characteristic that the histogram is robust against deformations such as heat hazes. This can be utilized to distinguish image regions deformed by heat hazes from image regions deformed by moving objects. Hereinafter, specific examples will be described. 
       FIG. 4  is a diagram showing that the histogram H 2  changes depending on conditions of moving objects included in past, current, and future images. It is assumed that the histogram H 1  is for a region E 3  that includes a target pixel  410  of the image N. The histogram H 2  is for a region E 4  of a target pixel  411  of the smoothed image M, and has different shapes depending on the speed of the moving object included in past, current, and future images. 
     When the movement pattern of the moving object is from still to low speed, the shape of the histogram H 2  is similar to that of the histogram H 1 , as shown in  FIG. 4  ( 1 ). When the movement pattern of the moving object is from low speed to middle speed, the image includes a small piece of picture information different from the moving object. Thus the shape of the histogram H 2  is slightly different from that of the histogram H 1 , as shown in  FIG. 4  ( 2 ). When the movement pattern of the moving object is from middle speed to high speed, the image includes a lot of picture information significantly different from the moving object. Thus the shape of the histogram H 2  is much different from that of the histogram H 1 , as shown in  FIG. 4  ( 3 ). Therefore, it is possible to perform a simplified inspection of the movement of the moving object within the image, by comparing the histogram H 1  of the image N with the histogram of the smoothed image M. 
     Utilizing the above-mentioned characteristic of gradation distribution histogram, it is possible to distinguish an image fluctuation of static region due to heat hazes from a movement of moving object. In addition, by using the histogram H 2  of each pixel in the smoothed image M created from past, current, and future images, it is possible to perform a simplified inspection of existence of moving object or its velocity. 
     The similarity calculator  13  calculates a similarity between the gradation distributions calculated by the gradation distribution calculator  12 , e.g. between the histograms described with reference to  FIGS. 3-4 . For example, the similarity calculator  13 : calculates a distance B 32 ( c ) between the histograms utilizing Bhattacharyya distance for each of RGB components using Equation 2 below, for example; and calculates a similarity R using B 32 ( r ) related to r component, B 32 ( g ) related to g component, and B 32 ( b ) related to b component. The similarity R corresponds to a distance between the histograms. Thus the smaller R is, the higher the similarity is. The parameter c in Equation 2 represents one of rgb. 
     
       
         
           
             
               
                 
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       FIG. 5  is a diagram showing an operation of the image corrector  14 . The image corrector  14  determines pixel values of the corrected image using the input image N and the smoothed image M. Specifically, the image corrector  14 : determines a correcting value for each pixel using the similarity R calculated by the similarity calculator  13  and using two thresholds T 1  and T 2  (T 1 &lt;T 2 ); and corrects the input image N using the correcting value. 
     If the similarity R≦T 1  (the similarity between the histograms is high), the regions  600  and  601  in  FIG. 5  correspond to such situation. This corresponds to a case when the movement pattern of the object in the image is from still to low speed. In this case, it is preferable to perform an intensive correction using the smoothed image M. Thus the pixel value of the smoothed image M is used as the correcting value. 
     If T 2 &lt; the similarity R, the regions  620  and  621  in  FIG. 5  correspond to such situation. This corresponds to a case when the movement pattern of the object in the image is from middle speed to high speed. In this case, if the correction is performed using the smoothed image M, a blurred image is acquired in which the movement of the moving object is smoothed. Thus the smoothed image M is not used and the pixel value of the input image N is used as the correcting value. 
     If T 1 &lt; the similarity R≦T 2 , the boundary of the frame border of the region  610  in  FIG. 5  corresponds to such situation. This corresponds to a case when the movement pattern of the object in the image is from low speed to middle speed. This is an intermediate case between the two cases above. Thus the image corrector  14  calculates the correcting value by blending the pixel values of the input image N and of the smoothed image M using Equation 3 below. In Equation 3, it is assumed that: n(i, j) is a pixel value of the image N at coordinate (i, j); m(i, j) is a pixel value of the smoothed image M at coordinate (i, j); d(i, j) is a blended value of each pixel value; e(i, j)=m(i, j), f(i, j)=n(i, j).
 
[Formula 3]
 
 d ( i,j )=(1− R )× e ( i,j )+ R×f ( i,j )  Equation 3
 
       FIG. 6  is an example of a sharping filter used by the resolution enhancer  15 . The resolution enhancer  15  sharpens each correcting value using a filter such as shown in  FIG. 6 , for example, thereby creating the high resolution image S. 
     The smoothed image M may have low resolution. Therefore, for a region with small deformations such as the regions  600  and  601  in  FIG. 5 , the sharpness is intensified by setting K=1.0 in  FIGS. 6  ( 1 ) and ( 2 ). For the boundary of the frame border of the region  610  in  FIG. 5  where a blended value of the image N and the smoothed image M is used, an intermediate value is used such as K=0.75. For the regions  620  and  621  in  FIG. 5  where the pixel value of the image N is used, K=0.5 so as not to intensify noises. By performing the above-mentioned processes for all pixels, it is possible to create the high resolution image S in which heat hazes are reduced across the image including a static region and a moving object, while improving the resolution. 
       FIG. 7  is a flowchart showing an operation of the image processing device  1 . Hereinafter, each step in  FIG. 7  will be described. 
     ( FIG. 7 : Step S 701 ) 
     The inputter  10  acquires past, current, and future image frames, and outputs those image frames into the image smoother  11 . The image smoother  11  calculates the smoothed image M. If the total number of past, current, and future images is 5, the suffix u in step  701  is 2. 
     ( FIG. 7 : Steps S 702 -S 703 ) 
     The gradation distribution calculator  12  calculates the histograms H 1  and H 2  for each of pixels of the image N and of the smoothed image M (S 702 ). The similarity calculator  13  calculates the similarity R between the histograms H 1  and H 2  (S 703 ). 
     ( FIG. 7 : Step S 704 ) 
     The image corrector  14  compares the similarity R with each of the thresholds. If R≦T 1 , the process proceeds to step  705 . Otherwise the process proceeds to step  706 . 
     ( FIG. 7 : Step S 705 ) 
     The image corrector  14  sets the pixel value of the smoothed image M as the correcting value. 
     ( FIG. 7 : Steps S 706 -S 708 ) 
     The image corrector  14  determines whether the similarity R satisfies the threshold T 1 &lt;R≦T 2 . If the condition is satisfied, the process proceeds to step  707 . Otherwise the process proceeds to step S 708 . 
     ( FIG. 7 : Steps S 707 -S 708 ) 
     If the condition in step S 706  is satisfied, the image corrector  14  sets a blended value of pixel values of the image N and of the smoothed image M as the correcting value according to Equation 3 (S 707 ). If the condition is not satisfied, the image corrector  14  sets the pixel value of the image N as the correcting value (S 708 ). 
     ( FIG. 7 : Steps S 709 -S 710 ) 
     The resolution enhancer  15  creates the high resolution image S in which the resolution of the corrected image created by the image corrector  14  is improved (S 709 ). The image corrector  14  and the resolution enhancer  15  repeat steps S 702 -S 709  until the correcting value and pixel values with enhanced resolution are calculated for all pixels in the target image (S 710 ). 
     Embodiment 1: Summary 
     As discussed thus far, the image processing device  1  according to the embodiment 1: creates the smoothed image M using past, current (the image N), and future images; creates the histograms H 1  and H 2  for each of pixels of the image N and of the smoothed image M; and changes the correcting value for each of pixels depending on the similarity R between the histograms H 1  and H 2 . Accordingly, it is possible to create a corrected image in which heat haze is reduced within the image. 
     The image processing device  1  according to the embodiment 1 changes the correcting value for each of pixels depending on the similarity between the histograms H 1  and H 2  in which the gradation values are rounded. Accordingly, it is possible to distinguish image fluctuations at static region due to heat haze from movements of moving objects. In addition, it is possible to perform simplified inspections of movements of moving objects within the image. Thus it is possible to reduce heat hazes across the image that includes both static regions and moving objects. 
     Embodiment 2 
     The embodiment 1 describes that static regions and moving objects are distinguished from each other by comparing a gradation distribution histogram of the smoothed image M with that of the input image N. By using the smoothed image M, it is possible to save usage of storage capacity of the memory  90 , as well as to reduce the frequency of comparison to suppress processing loads. On the other hand, if there is no strict hardware limit regarding those resources, it is possible to compare each image frame with the input image N without creating the smoothed image M. In an embodiment 2 of the present invention, a configuration example will be described where a gradation distribution histogram of the input image N is compared with that of image frames before and after the input image N. 
       FIG. 8  is a functional block diagram of the image processing device  1  according to the embodiment 2. In contrast to the configuration in the embodiment 1, the image processing device  1  according to the embodiment 2 does not include the image smoother  11 . Other configurations are generally same as those of the embodiment 1. Thus differences of each component will be mainly described below. 
       FIG. 9  is s diagram showing an operation of the gradation distribution calculator  12 . The gradation distribution calculator  12  calculates gradation distributions using pixels of peripheral regions around a target pixel for each of past, current, and future images. In the example shown in  FIG. 9 , a histogram H 1  is calculated using a partial image of a region E 5  (e.g. 32×32 pixels) that includes a target pixel  910  of the image N−2. Similarly: a histogram H 2  is calculated using a partial image of a region E 6  that includes a target pixel  911  of the image N−1; a histogram H 3  is calculated using a partial image of a region E 7  that includes a target pixel  912  of the image N; a histogram H 4  is calculated using a partial image of a region E 8  that includes a target pixel  913  of the image N+1; and a histogram H 5  is calculated using a partial image of a region E 9  that includes a target pixel  914  of the image N+2. Each of the histograms has rounded gradation values as in the embodiment 1. 
     The similarity calculator  13  calculates similarities between gradation distributions calculated by the gradation distribution calculator  12 , e.g. similarities between each of the histograms above. For example, using the histogram H 3  as a reference, a distance B 32 ( c ) and a similarity R 1  between the histograms H 1  and H 3  are calculated for each of RGB according to Equation 2. Similarly: a distance B 32 ( c ) and a similarity R 2  between the histograms H 2  and H 3  are calculated; a distance B 32 ( c ) and a similarity R 3  between the histograms H 4  and H 3  are calculated; and a distance B 32 ( c ) and a similarity R 4  between the histograms H 5  and H 3  are calculated. 
     The image corrector  14  determines pixel values of the corrected image using the input image N and the images N−2, N−1, N+1, and N+2 before and after the input image N. Specifically, the image corrector  14 : determines the correcting value for each of pixels using the similarities R 1 -R 4  calculated by the similarity calculator  13  and two thresholds T 1  and T 2  (T 1 &lt;T 2 ); and corrects the input image N using the correcting value. 
     The image corrector  14 : compares the similarity R 1  between the histograms of the images N−2 and N with the thresholds; and calculates a correcting value C 1  assuming that the image N is corrected using the image N−2. If the similarity R 1 ≦T 1  (the similarity between the histograms is high), the pixel value of the image N−2 is set as the correcting value C 1 . If T 1 &lt; the similarity R 1 ≦T 2 , a blended value of pixel values of the images N and N−2 is set as the correcting value C 1 . If T 2 &lt; the similarity R 1 , the pixel value of the image N is set as the correcting value C 1 . 
     Similarly, the image corrector  14  determines correcting values C 2 -C 4  for the similarities R 2 -R 4  between the histograms of other images and the image N, respectively. The image corrector  14  calculates finally used correcting values according to Equation 4 using the correcting values C 1 -C 4  and pixel value n of the image N. In Equation 4, if the total number of past, current, and future images is 5, A=4 and p6=1+A=5. 
     
       
         
           
             
               
                 
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     Instead of Equation 4 and without performing threshold comparison, the finally used correcting value may be calculated according to Equation 5 below using the similarities Ri (i=1 to 4) and pixel values of each image. In Equation 5, ai is pixel values of each image. a1 represents pixel values of the image N−2, a2 represents pixel values of the image N−1, a3 represents pixel values of the image N+1, and a4 represents pixel values of the image N+2. 
     
       
         
           
             
               
                 
                   
                       
                   
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     According to the procedure above: heat haze is corrected in the regions  920  and  930  in  FIG. 9  using all images (image N−2, image N−1, image N, image N+1, image N+2); heat haze is corrected in the regions  940  and at the boundary of the frame border  950  in  FIG. 9  using images that satisfy the similarity Ri≦T 1 ; and pixel values of the image N are set for the regions  960  and  961  in  FIG. 9 . Therefore, it is possible to correct heat hazes across the image that includes both static regions and moving objects. 
     If each of the correcting values is calculated by synthesizing each pixel value of a plurality of images, the image resolution may be reduced. Thus the resolution enhancer  15  sets K=1.0 in  FIG. 9  for the regions  920  and  930  in  FIG. 9 . The resolution enhancer  15  sets K=0.75 for the region  940  and the boundary of the frame border  950  in  FIG. 9 , for example. The resolution enhancer  15  sets K=0.5 for the regions  960  and  961  in  FIG. 9 , for example. 
       FIG. 10  is a flowchart showing an operation of the image processing device  1 . Hereinafter, each step in  FIG. 10  will be described. 
     ( FIG. 10 : Step S 1001 ) 
     The gradation distribution calculator  12  calculates the histograms H 1 -H 5  for each of pixels of past, current, and future images (the images N−2 to N+2) outputted from the inputter  10 . If the total number of past, current, and future images is 5, the suffix u in step S 1001  is 2. 
     ( FIG. 10 : Step S 1002 ) 
     The similarity calculator  13  calculates the similarities R 1 -R 4  between the histogram H 3  and each of the histograms (H 1 , H 2 , H 4 , H 5 ). The image corrector  14  initializes a suffix i used in calculating the correcting values C 1 -C 4  (i=1). i=1 corresponds to the image N−2; i=2 corresponds to the image N−1; i=3 corresponds to the image N+1; and i=4 corresponds to the image N+2. 
     ( FIG. 10 : Step S 1003 ) 
     The image corrector  14  determines whether the similarity Ri≦ threshold T 1  is satisfied. If the condition is satisfied, the process proceeds to step S 1004 . Otherwise the process proceeds to step S 1005 . 
     ( FIG. 10 : Step S 1004 ) 
     The image corrector  14  uses the pixel value of the other image (the image other than the image N) as the correcting value Ci. 
     ( FIG. 10 : Step S 1005 ) 
     The image corrector  14  determines whether threshold T 1 &lt;Ri≦T 2  is satisfied. If the condition is satisfied, the process proceeds to step S 1006 . Otherwise the process proceeds to step S 1007 . 
     ( FIG. 10 : Step S 1006 ) 
     The image corrector  14  uses a blended value of pixel value of the image N and pixel value of the other image (the image other than the image N) as the correcting value Ci. In other words, assuming that n(i, j) is a pixel value of the image N at coordinate (i, j) and n2(i, j) is a pixel value of the other image at coordinate (i, j), e(i, j)=n2(i, j) and f(i, j)=n(i, j) in Equation 3. 
     ( FIG. 10 : Step S 1007 ) 
     The image corrector  14  uses the pixel value of the image N as the correcting value Ci. 
     ( FIG. 10 : Step S 1008 ) 
     The image corrector  14  proceeds to step S 1009  if all the correcting values Ci (i=1 to 4) are calculated. If there still is the correcting value Ci that has not been calculated yet, the image corrector  14  increments the suffix i and returns to step S 1003 . 
     ( FIG. 10 : Steps S 1009 -S 1011 ) 
     The image corrector  14  calculates a finally used correcting value using Equations 4 or 5 (S 1009 ). The resolution enhancer  15  calculates a value in which the resolution of the correcting value is enhanced (S 1010 ). The image corrector  14  and the resolution enhancer  15  repeat steps S 1001 -S 1010  until the correcting value and pixel values with enhanced resolution are calculated for all pixels in the target image (S 1011 ). 
     Embodiment 2: Summary 
     As discussed thus far, the image processing device  1  according to the embodiment 2: creates the histograms H 1 -H 5  for each pixel of past, current (the image N), and future images (the images N−2 to N+2); compares the histogram H 3  with each of the histograms (H 1 , H 2 , H 4 , H 5 ) to calculate the similarities R 1 -R 4 ; and changes the correcting value for each of pixels depending on the similarities R 1 -R 4 . Accordingly, it is possible to more clearly calculate the difference between the histograms compared to the embodiment 1. In other words, the embodiment 1 compares the smoothed image M in which a plurality of images is smoothed with the image N. Thus it may be likely that the difference between the histograms is larger than that of the embodiment 2. According to the embodiment 2, it is possible to more clearly calculate the difference between the histograms, thereby selecting more appropriate correcting values. 
     The image processing device  1  according to the embodiment 2 determines the finally used correcting value using Equations 4 or 5. Accordingly, it is possible to dynamically change the number of images used for heat haze correction for each pixel. For example, the correcting value Ci calculated from the images N−2 to N+2 and the pixel value of the image N are evenly used when using Equation 4, and the pixel values of the images N−2 to N+2 are added with weight factors depending on the similarity Ri when using Equation 5. Therefore, it is possible to correct heat hazes of images including moving objects, using appropriate numbers of images for each of pixels. Further, only image(s) selected from the images N−2 to N+2 may be used according to some other appropriate rules. 
     Embodiment 3 
     The embodiments 1 and 2 distinguish image fluctuations due to heat haze from moving objects using histograms with rounded gradation values, thereby correcting heat hazes in images using appropriate numbers of images. In an embodiment 3 of the present invention, a configuration example will be described where: a moving object removal image T is created in which the moving object is previously removed from the input image N; and by comparing the moving object removal image T with the input image N, heat hazes are preferably removed in static regions where the moving object does not exist. 
       FIG. 11  is functional block diagram of the image processing device  1  according to the embodiment 3. The image processing device  1  according to the embodiment 3 includes a removal image creator  1111  in addition to the configurations described in the embodiments 1 or 2. Other configurations are generally same as those of the embodiments 1 and 2. Thus hereinafter the difference will be mainly described. The description below assumes the configuration of the embodiment 1 for the sake of convenience. 
     The removal image creator  1111  creates the moving object removal image T in which the moving object is removed from the input image N using Equation 6 below. In Equation 6, the parameter U has a value of 0&lt;U&lt;1. 
     
       
         
           
             
               
                 
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       FIG. 12  is a diagram showing a processing example of the removal image creator  1111 . The removal image creator  1111  calculates the histogram and the similarity R described in the embodiment 1, for a partial image of a region E 10  that includes a target pixel  1210  (n3(i, j)) at coordinate (i, j) of the image N−3, and for a partial image of a region E 11  that includes a target pixel  1211  (n2(i, j)) at coordinate (i, j) of the image N−2. If R≦ threshold T 1 , the removal image creator  1111  sets v(i, j)=n3(i, j) and w(i, j)=n2(i, j) in Equation 6, and calculates a correcting value h(i, j) at coordinate (i, j) using Equation 6. If R&gt;threshold T 1 , the removal image creator  1111  uses pixel values of the past image (i.e. the image N−3) as the correcting value. The removal image creator  1111  performs the determination above for all pixels, thereby creating a moving object removal image T−2. 
     The removal image creator  1111  then performs the same process, for a partial image of a region E 14  that includes a target pixel  1214  (t2(i, j)) at coordinate (i, j) of the moving object removal image T−2, and for a partial image of a region E 12  that includes a target pixel  1212  (n1(i, j)) at coordinate (i, j) of the image N−1, thereby creating the moving object removal image T−1. 
     The removal image creator  1111  then performs the same process, for a partial image of a region E 15  that includes a target pixel  1215  (t1(i, j)) at coordinate (i, j) of the moving object removal image T−1, and for a partial image of a region E 13  that includes a target pixel  1213  (n(i, j)) at coordinate (i, j) of the image N, thereby creating the moving object removal image T. 
     According to the process above, only the static region of the image is repeatedly smoothed. Therefore, the heat haze at the static region of the moving object removal image T is significantly reduced. In  FIG. 12 , the process begins from the image N−3 where the moving object does not exist. However, even if the process begins from the image N−2, it is possible to create an image including the static region only, by repeating the same process after the image N. Therefore, it is not significantly important from which image the process begins. 
     The gradation distribution calculator  12  calculates, for each pixel, a histogram H 6  of the moving object removal image T created by the moving object removal image creator  1111 , a histogram H 7  of the image N, and a histogram H 8  of the smoothed image M created by the image smoother  11 . 
       FIG. 13  is a diagram showing a process of the similarity calculator  13  and of the image corrector  14 . The similarity calculator  13  calculates a distance B 32 ( c ) and the similarity R between the histograms H 7  and H 8  calculated by the gradation distribution calculator  12 , for each of RGB. In addition, the similarity calculator  13  calculates a distance B 32 ( c ) and the similarity RT between the histograms H 7  and H 6  calculated by the gradation distribution calculator  12 , for each of RGB. 
     The image corrector  14  determines pixel values of the corrected image using the input image N, the moving object removal image T, and the smoothed image M. Specifically, the image corrector  14 : determines the correcting value for each pixel using the similarities R, RT calculated by the similarity calculator  13  and two thresholds T 1 , T 2  (T 1 &lt;T 2 ); and corrects the input image N using the correcting value. 
     The image corrector  14 : compares the similarity R calculated by the similarity calculator  13  with each of the thresholds; and calculates a correcting value C assuming that the image N is corrected using the smoothed image M. If the similarity R≦T 1  (the similarity between the histograms is high), the pixel value of the smoothed image M is set as the correcting value C. If T 1 &lt; the similarity R≦T 2 , a blended value of pixel values of the images N and of the smoothed image M is set as the correcting value C according to Equation 3. If the similarity R&gt;T 2 , the pixel value of the image N is set as the correcting value C. 
     The image corrector  14  then: compares the similarity RT calculated by the similarity calculator  13  with each of the thresholds; and calculates a correcting value CT assuming that the image N is corrected by the moving object removal image T. If the similarity RT≦T 1 , the regions  1300  and  1320  in  FIG. 13  correspond to such situation, and the pixel value of the moving object removal image T is set as the correcting value CT. If T 1 &lt; the similarity RT≦T 2 , the regions  1310  and the boundary of the frame border  1330  in  FIG. 13  correspond to such situation, and a blended value of the pixel value of the moving object removal image T and the correcting value C is set as the correcting value CT according to Equation 3. If the similarity RT&gt;T 2 , the regions  1340  and  1341  in  FIG. 13  correspond to such situation, and the correcting value C is set as the correcting value CT. The image corrector  14  uses the calculated correcting value CT as the finally determined correcting value. 
     The moving object removal image T or the smoothed image M may have low resolution. Therefore, for the regions  1300  and  1320  in  FIG. 13 , the resolution enhancer  15  sets K=1.0 in  FIG. 13 , for example. For the region  1310  and the boundary of the frame border of the region  1330  in  FIG. 13 , the resolution enhancer  15  sets K=0.75, for example. For the regions  1340  and  1341  in  FIG. 13 , the resolution enhancer  15  sets K=0.5 so as not to intensify noises, for example. 
       FIG. 14  is a flowchart showing an operation of the image processing device  1 . Hereinafter, each step in  FIG. 14  will be described. 
     ( FIG. 14 : Step S 1401 ) 
     The removal image creator  1111  removes the moving object from the input image N according to the sequence described in  FIG. 12 , thereby creating the moving object removal image T. Image fluctuations due to heat haze at static regions are reduced within the moving object removal image T. 
     ( FIG. 14 : Step S 1402 ) 
     The image smoother  11  creates the smoothed image M. Heat hazes at the moving object and at the border between the moving object and the static region are reduced within the smoothed image M. 
     ( FIG. 14 : Steps S 1403 -S 1404 ) 
     The gradation distribution calculator  12  calculates the histogram H 7  of the image N at time t, the histogram H 8  of the smoothed image M, and the histogram H 6  of the moving object removal image T (S 1403 ). The similarity calculator  13  calculates the similarity R between the histograms H 7  and H 8 , and the similarity RT between the histograms H 7  and H 6  (S 1404 ). 
     ( FIG. 14 : Step S 1405 ) 
     If R≦ threshold T 1 , the image corrector  14  proceeds to step S 1406 . Otherwise the image corrector  14  proceeds to step S 1407 . 
     ( FIG. 14 : Step S 1406 ) 
     The image corrector  14  sets the pixel value of the smoothed image M as the correcting value C. 
     ( FIG. 14 : Step S 1407 ) 
     The image corrector  14  determines whether the similarity R satisfies threshold T 1 &lt;R≦T 2 . If the condition is satisfied, the process proceeds to step S 1408 . Otherwise the process proceeds to step S 1409 . 
     ( FIG. 14 : Step S 1408 ) 
     The image corrector  14  sets a blended value of the pixel values of the smoothed image M and of the image N as the correcting value C. In other words, assuming that m(i, j) is a pixel value of the smoothed image M at coordinate (i, j) and n(i, j) is a pixel value of the image N at coordinate (i, j), the image corrector  14  sets e(i, j)=m(i, j) and f(i, j)=n(i, j) in Equation 3, thereby calculating a blended value. 
     ( FIG. 14 : Step S 1409 ) 
     The image corrector  14  sets the pixel value of the image N as the correcting value C. 
     ( FIG. 14 : Step S 1410 ) 
     The image corrector  14  determines whether the similarity RT satisfies RT≦ threshold T 1 . If the condition is satisfied, the process proceeds to step S 1411 . Otherwise the process proceeds to step S 1412 . 
     ( FIG. 14 : Step S 1411 ) 
     The image corrector  14  sets the pixel value of the moving object removal image T as the correcting value CT. 
     ( FIG. 14 : Step S 1412 ) 
     The image corrector  14  determines whether the similarity RT satisfies threshold T 1 &lt;RT≦T 2 . If the condition is satisfied, the process proceeds to step S 1413 . Otherwise the process proceeds to step S 1414 . 
     ( FIG. 14 : Step S 1413 ) 
     The image corrector  14  sets a blended value of the pixel value of the moving object removal image T and the correcting value C as the correcting value CT. In other words, assuming that t0(i, j) is a pixel value of the moving object removal image T at coordinate (i, j), e(i, j)=t0(i, j) and f(i, j)=the correcting value C in Equation 3. 
     ( FIG. 14 : Step S 1414 ) 
     The image corrector  14  sets the correcting value C at coordinate (i, j) as the correcting value CT. The image corrector  14  employs the correcting value CT calculated in the foregoing process as the finally used correcting value. 
     ( FIG. 14 : Steps S 1415 -S 1416 ) 
     The resolution enhancer  15  calculates a value in which the resolution of the correcting value is enhanced (S 1415 ). The image corrector  14  and the resolution enhancer  15  repeat steps S 1403 -S 1415  until the correcting value and pixel values with enhanced resolution are calculated for all pixels in the target image (S 1416 ). 
     Embodiment 3: Summary 
     As discussed thus far, the image processing device  1  according to the embodiment 3: creates the moving object removal image T in addition to the smoothed image M; and changes the correcting value for each pixel depending on the similarity R (of the histograms) between the input image N and the smoothed image M and on the similarity RT (of the histograms) between the input image N and the moving object removal image T. By introducing the pixel value of the moving object removal image T as the correcting value CT, it is possible to preferably reduce heat hazes of the static region within the input image N. 
     Among the pixel value of the moving object removal image T, the pixel value of the image N at time t, and the pixel value of the smoothed image M, the image processing device  1  according to the embodiment 3 uses the most appropriate one as the finally used correcting value CT depending on the similarities. Accordingly, it is possible to further effectively suppress heat hazes at static regions of the image, as well as to calculate the corrected image where heat hazes are reduced across the image that includes both static regions and moving objects. 
     Embodiment 4 
       FIG. 15  is a functional block diagram of an imaging device  1500  according to an embodiment 4 of the present invention. The imaging device  1500  includes an imager  1501 , an image processing device  1 , and an image displayer  1502 . The imager  1501  is an imaging device that receives light irradiated from the imaged subject, and that converts the received optical image into image data. The image processing device  1  is the image processing device  1  according to one of the embodiments 1-3. The image processing device  1  receives the image data captured by the imager  1501  to correct heat hazes. The image displayer  1502  is a device such as display that displays the corrected image outputted from the image processing device  1 . 
     The image displayer  1502  switches images depending on operational modes. For example, if the mode is 1, the image displayer  1502  displays the corrected image with reduced heat hazes. If the mode is 0, the image displayer  1502  displays the input image. 
     With the imaging device  1500  according to the embodiment 4, it is possible to provide an imaging device that displays to the photographer the corrected image in which heat hazes are reduced across the image that includes both static regions and moving objects. 
     Embodiment 5 
       FIG. 16  is a functional block diagram of a monitoring system  1600  according to an embodiment 5 of the present invention. The monitoring system  1600  includes an imaging device  1601 , an image processing device  1 , a server  1602 , and a display device  1603 . The imaging device  1601  is an imaging device such as one or more of monitoring cameras that capture image data. The image processing device  1  is the image processing device  1  according to one of the embodiments 1-3. The image processing device  1  receives the image data captured by the imaging device  1601  to correct heat hazes. The server  1602  is a computer that equips the image processing device  1 . The display device  1603  is a device such as display that displays the corrected image outputted from the image processing device  1 . 
     It is possible to connect, through networks such as Internet, between the imaging device  1601  and the server  1602  and between the server  1602  and the display device  1603 , depending on the physical allocations between the monitored location and the monitoring operator. 
     With the monitoring system  1600  according to the embodiment 5, it is possible to provide a monitoring system that displays to the monitoring operator the corrected image in which heat hazes are reduced across the image that includes both static regions and moving objects. 
     Embodiment 6 
       FIG. 17  is a functional block diagram of a coding-decoding system  1700  according to an embodiment 6 of the present invention. The coding-decoding system  1700  includes a coding device  1710 , a decoder  1721 , and a display device  1722 . The coding device  1710  includes an imaging device  1711 , an image processing device  1 , and a coder  1712 . 
     The imaging device  1711  is an imaging device such as monitoring camera that captures image data. The image processing device  1  is the image processing device  1  according to one of the embodiments 1-3. The image processing device  1  receives the image data captured by the imaging device  1711  to correct heat hazes. The coder  1712  encodes the corrected image data outputted from the image processing device  1 , and transmits the encoded image data toward the decoder  1721  through a network. The decoder  1721  decodes the transmitted corrected image data. The display device  1722  displays the image decoded by the decoder  1721 . 
     With the coding-decoding system  1700  according to the embodiment 6, it is possible to provide a coding-decoding system that displays the decoded image in which heat hazes are reduced across the image that includes both static regions and moving objects. In addition, due to the reduced heat haze within the image, the difference between images transmitted by the coder  1712  is small, and thus the coding efficiency is improved. 
     Embodiment 7 
     In an embodiment 7 of the present invention, examples with modified components of the embodiments 1-6 will be described. 
     It is described above that the image smoother  11  creates the smoothed image M using five pieces of past, current, and future images. However, the number of images used for creating the smoothed image M is not limited to 5. It also applies to the number of images for comparing with the image N in the embodiment 2. In addition, the image smoother  11  may create the smoothed image M using past images within a certain period and current image, or using future images within a certain period and current image. This also achieves the same advantageous effect. 
     It is describes above that the similarity calculator  13  calculates the similarity between the histograms utilizing Bhattacharyya distance. However, the similarity calculator  13  may calculate the similarity using other comparing methods for calculating the distance between the histograms such as a total sum of difference of frequencies between the histograms. This also achieves the same advantageous effect. 
     It is described above that the resolution enhancer  15  uses  FIG. 6  as the sharpening filter. However, the resolution enhancer  15  may enhance the resolution of image with reduced heat haze using other sharpening filters or using conventional super resolution techniques. This also achieves the same advantageous effect. 
     The embodiment 3 describes that the image corrector  14  uses the moving object removal image T when calculating the correcting value CT. However, the image corrector  14  may use the moving object removal image T−1 in  FIG. 12 . When using the moving object removal image T−1, the image corrector  14  sets the pixel value of the moving object removal image T−1 as the correcting value CT in step S 1411  of  FIG. 14 , and sets a blended value of the pixel value of the moving object removal image T−1 and the correcting value C as the correcting value CT in step S 1413  of  FIG. 14 . 
     The present invention is not limited to the embodiments, and various modified examples are included. The embodiments are described in detail to describe the present invention in an easily understood manner, and the embodiments are not necessarily limited to the embodiments that include all configurations described above. Part of the configuration of an embodiment can be replaced by the configuration of another embodiment. The configuration of an embodiment can be added to the configuration of another embodiment. Addition, deletion, and replacement of other configurations are also possible for part of the configurations of the embodiments. 
     The configurations, the functions, the processing units, the processing means, etc., may be realized by hardware such as by designing part or all of the components by an integrated circuit. A processor may interpret and execute programs for realizing the functions to realize the configurations, the functions, etc., by software. Information, such as programs, tables, and files, for realizing the functions can be stored in a recording device, such as a memory, a hard disk, and an SSD (Solid State Drive), or in a recording medium, such as an IC card, an SD card, and a DVD. 
     REFERENCE SIGNS LIST 
     
         
           1  image processing device 
           10  inputter 
           11  image smoother 
           12  gradation distribution calculator 
           13  similarity calculator 
           14  image corrector 
           15  resolution enhancer 
           16  storage unit 
           91  controller 
           1111  removal image creator 
           1500  imaging device 
           1600  monitoring system 
           1700  coding-decoding system