Abstract:
Noises are reduced in an image picked up in natural light without using lighting equipment that affect the picked up image. The present invention provides an image processing apparatus that includes an image acquisition unit that acquires a visible light image and an invisible light image corresponding to the visible light image, and a noise reduction unit that reduces noises of the visible light image by using the invisible light image.

Description:
CROSS REFERENCES TO RELATED APPLICATIONS 
     The present invention contains subject matter related to Japanese Patent Application JP 2004-371985 filed in the Japanese Patent Office on Dec. 22, 2004, the entire contents of which being incorporated herein by reference. 
     BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     This invention relates to an image processing apparatus, image processing method, an image pickup apparatus, a computer program and a recording medium adapted to eliminate noises, while storing edges of images. 
     2. Description of the Related Art 
     When an image is picked up by a camera, the color tone of the image depends on lighting to a large extent. For example, the picked up image of a subject bears an orangey color tone when the subject is irradiated with candle light but bears a bluish color tone when it is irradiated with moon light. Images of a subject may be picked up with completely different color tones on the same site depending on lighting. 
     When picking up an image, it is possible to correctly reproduce the original color tone by utilizing natural light without using lighting equipment such as a flash bulb. However, the picked up image contains noises to a considerable extent when the subject is shot indoors or at night because of insufficient exposure to light. When, on the other hand, flash light is used for imaging, the edges of the objects and other details in the picked up image appear clearly but the color tone of the image may not be accurate and/or shades and high lights that do not actually exist may also appear. 
     An image processing apparatus as illustrated in  FIG. 1  of the accompanying drawings has been proposed to dissolve the above-identified problem. Referring to  FIG. 1 , the image processing apparatus  11  includes a pair of low pass filters  12 ,  13 , a high pass filter  14 , a pair of image synthesizing sections  15 ,  17  and a single shadow extracting section  16 . 
     The low pass filter  13  is a cross bilateral filter that detects edges from an image picked up by using flash light (to be referred to as flash image hereinafter) and remove noises from an image picked up without using flash light (to be referred to as natural light image hereinafter). The high pass filter  14  extracts edges of objects in the picked up image. For extracting edges, the image processing apparatus executes a process of dividing each pixel value in the natural light image by the corresponding one of the flash image. 
     The image synthesizing section  15  synthetically combines the natural light image from which noises are removed by the low pass filter  13  and the flash image from which edges are extracted by the high pass filter  14  to generate a synthetic image Comp. For synthesizing an image, the image processing apparatus executes a process of multiplying each pixel value of the low pass filter  13  by the corresponding one of the output image of the high pass filter  14 . The synthetic image Comp provides the advantages of the flash image and those of the natural light image so that its color tone is accurate and it contains fewer noises. 
     The low pass filter  12  removes noises from the natural light image. A bilateral filter is used for the low pass filter  12 . A bilateral filter can detect edges and remove noises from a single image. 
     The shadow extracting section  16  extracts differences between the two images including the flash image and the natural light image and evaluates the probability by which the flash image is changed from the natural light image by high lights and shadows. The image synthesizing section  17  carries out an operation of weighted addition of the output image from the low pass filter  12  and the output image Comp of the image synthesizing section  15  according to the outcome of the evaluation of the shadow extracting section  16 . More specifically, the image synthesizing section  17  raises the weight of the image in a part thereof where shadows and high lights may highly probably have been produced by flash light and reduces the weight of the image in a part hereof where they may probably have not been produced. On the other hand, the image synthesizing section  17  removes unnecessary shadows and high lights from the synthetic image Comp and generates an output image Out. 
     As described above, the known image processing apparatus  11  can obtain an image where edges in a flash image and the color tone of a corresponding natural light image are optimally blended by generating two images including an image where edges are synthesized from a flash image and an image from which noises are removed without referring to the edges of the flash image, raising the coefficient of the output image of the high pass filter  12  for pixels where shadows and high lights may highly probably have been produced by flash light and reducing the coefficient of the output image Comp of the image synthesizing section  17  for pixels where shadows and high lights may probably have not been produced (see, for example, Non-Patent Document 1: Georg Petschnigg et al, Digital Photography with Flash and No-Flash Image pairs, acm Transaction on Graphics, Vol. 23, Number 3, pp. 664-672, August 2004). 
     SUMMARY OF THE INVENTION 
     While edges and details of an image become clear when flash light is used for picking up the image, shadows and high lights that do not exist in a corresponding natural light image can appear as described above. Although it is desirable for the image processing apparatus  11  to leave edges and details in the image, while removing shadows and high lights, it is not easy to discriminate them from each other. In other words, it costs a lot to discriminate them from each other by way of computations. 
     Additionally, the image processing apparatus  11  requires two images including a flash image and a natural light image. Since it is not possible to pick up a flash image and a natural light image at the same time, the technique of obtaining two images cannot be applied to moving pictures and to operations of picking up a moving subject. Still additionally, it is not possible to obtain a flash image in areas where the use of flashlight is prohibited. 
     In view of the above identified problems, it is therefore desirable to provide an image processing apparatus, an image processing method, an image pickup apparatus, a computer program and a recording medium that can reduce noises in an image picked up in natural light without using lighting equipment that affect the picked up image. 
     According to the present invention, there is provided an image processing apparatus including: an image acquisition means for acquiring a visible light image and an invisible light image corresponding to the visible light image; and a noise reduction means for reducing noises of the visible light image by using the invisible light image. 
     According to the present invention, there is also provided an image pickup apparatus including: a visible light image pickup means for picking up a visible light image according to a first spectral characteristic of having sensitivity mainly relative to visible light; an invisible light image pickup means for picking up an invisible light image according to a second spectral characteristic of having sensitivity mainly to invisible light; an aberration correction means for correcting the aberration of the visible light image and that of the invisible light image; and a noise reduction means for reducing noises of the visible light image by using the invisible light image. 
     According to the present invention, there is also provided an image processing method including: an image acquisition step of acquiring a visible light image and an invisible light image corresponding to the visible light image and picked up with the same number of pixel as those of the visible light image; and a noise reduction step of reducing noises of the visible light image by using the invisible light image. 
     According to the present invention, there is also provided a computer program for causing a computer to execute a predetermined process, the program including: an image acquisition step of acquiring a visible light image and an invisible light image corresponding to the visible light image and picked up with the same number of pixel as those of the visible light image; and a noise reduction step of reducing noises of the visible light image by using the invisible light image. 
     According to the present invention, there is also provided a recording medium where a computer program for causing a computer to execute a predetermined process, the program including: an image acquisition step of acquiring a visible light image and an invisible light image corresponding to the visible light image and picked up with the same number of pixel as those of the visible light image; and a noise reduction step of reducing noises of the visible light image by using the invisible light image. 
     Thus, according to the present invention, it is possible to reduce noises in an image picked up in natural light without using lighting equipment that affect the picked up image because noises are reduced from an invisible light image by using a corresponding visible light image. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a schematic block diagram of a known image processing apparatus; 
         FIG. 2  is a schematic block diagram showing the configuration of an embodiment of image pickup apparatus; 
         FIG. 3  is a schematic block diagram showing the configuration of an image processing section; 
         FIG. 4  is a schematic block diagram showing the configuration of a low pass filter; 
         FIG. 5  is a flowchart of operation of the low pass filter; 
         FIG. 6  is a flowchart of operation of a level 1 filter in the X-direction; 
         FIG. 7  is a schematic block diagram showing the configuration of a high pass filter; 
         FIG. 8  is a schematic block diagram of a first modified image processing section; 
         FIG. 9  is a schematic block diagram of a second modified image processing section; 
         FIG. 10  is a schematic block diagram showing the configuration of an aberration correcting section; 
         FIG. 11  is a flowchart of operation of the aberration correcting section; and 
         FIG. 12  is a flowchart of a scale value computing process of the aberration correcting section. 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     Now an embodiment of image pickup apparatus of the present invention will be described below by referring to the related drawings.  FIG. 2  is a schematic block diagram showing the configuration of the embodiment of image pickup apparatus. Referring to  FIG. 2 , the image pickup apparatus  1  includes an image pickup section  2  for picking up both a visible light image Visible and an infrared image Infr at a time, an image processing section  3  for removing noises from the visible light image Visible, a memory  4  for providing storage area for storing images and data, a system control section  8  adapted to output images to LCD (liquid crystal display)  5  and exchange data with external recording apparatus  10  by way of interfaces such as serial interface  6  and USB (universal serial bus)  7  and a signal processing section  9  adapted to operate for AGC (automatic gain control) and CDS (correlated double sampling) on the image input from image pickup element  21  and output the image to the image processing section  3 . 
     The image pickup section  2  outputs RGB images including a visible light image Visible and an infrared image Infr. The image pickup section  2  includes an image pickup element  21  typically formed by using a CCD (charge coupled device) and a CMOS (complementary Metal-Oxide Semiconductor), a motor  23  for focusing lens  22  and switching the shutter speed  23  and a driver  24  for controlling the motor  23 . 
     The image pickup element  21  may include two image pickup elements  21  including one that operates for picking up an infrared image Infr and one that operate for picking up a visible light image Visible. The image pickup element  21  for picking up an infrared image Infr and the image pickup element  21  for picking up a visible light image Visible have a same number of pixels and a same view angle and are adapted to be exposed to light simultaneously for a same period of time. Alternatively, it may be so arranged that the output of a single image pickup element  21  is used for both an infrared image Infr and a visible light image Visible. 
     The image processing section  3  removes noises from the visible light image Visible input to it and generates an output image Out, preserving edges of the visible light image Visible.  FIG. 3  is a schematic block diagram showing the configuration of the image processing section  3 . The image processing section  3  includes a gain adjustment section  31  for adjusting the gain of the visible light image Visible, a low pass filter  33  for removing noises from the visible light image Visible, a high pass filter  34  for extracting edges and details of the infrared image Infr and an image synthesizing section  35  for synthetically combining base image Base, which is the output image of the low pass filter  33 , and detail image Edge, which is the output image of the high pass filter  34 . 
     The gain adjustment section  31  boosts the pixel values of the visible light image Visible that may be an underexposed dark image to make it show pixel values close to those of a correctly exposed image. Techniques that can be used for gain adjustment include one that multiplies pixel values of the visible light image by a constant and one that involves the use of gamma correction based on an exponential function or some other correction based on a polynominal function. The maximum value is limited for the pixels after adjustment. 
       FIG. 4  is a schematic block diagram showing the configuration of the low pass filter  33 . The low pass filter  33  includes an X edge detecting section  41  for detecting edges in the X-direction (transversal direction) of the infrared image Infr, a Y edge detecting section  42  for detecting edges in the Y-direction (longitudinal direction) of the infrared image Infr, an X evaluation value storage section  43  for storing the edge evaluation value of the X-direction, a Y evaluation value storage section  44  for storaging the edge evaluation value of the Y-direction, three X low pass filters  45   a ,  45   b ,  45   c  for removing edges in the X-direction, three low pass filters  46   a ,  46   b ,  46   c  for removing edges in the Y-direction, three X comparator sections  47   a ,  47   b ,  47   c  for comparing the X edge evaluation value with threshold values and three Y comparator sections  48   a ,  48   b ,  48   c  for comparing the Y edge evaluation value with threshold values. 
     The X low pass filters are FIR low pass filters with 5×1 taps in the X-direction. Noises are removed in the X-direction from the visible light image by the X low pass filters. The Y low pass filters are FIR low pass filters with 1×5 taps in the Y-direction. Noises are removed in the Y-direction from the visible light image by the Y low pass filters. 
     The X edge detecting section  41  is an FIR filter with 4×4 taps for detecting edges in the X-direction. The Y edge detecting section  42  is an FIR filter with 4×4 taps for detecting edges in the Y-direction. The X value storage section  43  computationally determines an edge evaluation value by applying absolute value computations to the filtering outcome of the X edge detecting section  42  and stores the value as X edge image. The Y value storage section  44  computationally determines an edge evaluation value by applying absolute value computations to the filtering outcome of the Y edge detecting section  43  and stores the value as Y edge image. 
     The X comparator sections  47  and the Y comparator sections  48  compare the respective edge evaluation values with predetermined threshold values. Threshold value n is equal to ½ of the largest possible edge evaluation value. The X comparator section  47   a  compares the edge evaluation value in the X-direction with the threshold value n and the X comparator section  47   b  compares the edge evaluation value in the X-direction with threshold value n/2, whereas the X comparator section  47   c  compares the edge evaluation value in the X-direction with threshold value n/4. Similarly, the Y comparator section  48   a  compares the edge evaluation value in the Y-direction with the threshold value n and the Y comparator section  48   b  compares the edge evaluation value in the Y-direction with threshold value n/2, whereas the Y comparator section  48   c  compares the edge evaluation value in the Y-direction with threshold value n/4. 
     The low pass filter  33  is a three-stage filter including three filters for three different levels including a level 1 filter  49   a , a level 2 filter  49   b  and a level 3 filter  49   c . The level of the low pass filter  33  depends on the magnitudes of the threshold values of the X comparator sections  47  and the Y comparator sections  48 . The low pass filter for the threshold value n, the low pass filter for the threshold value n/2 and the low pass filter for the threshold value n/4 are respectively referred to as level 1 filter  49   a , level 2 filter  49   b  and level 3 filter  49   c.    
     The visible light image Visible is firstly output to the X comparator section  47   a . The X comparator section  47   a  compares the edge evaluation value in the X-direction stored in the X evaluation value storage section  43  with the threshold value n. The X comparator section  47   a  outputs the visible light image Visible to the X low pass filter  45   a  when the edge evaluation value is smaller than the threshold value n but it outputs the visible light image Visible to the Y comparator section  48   a  when the edge evaluation value is not smaller than the threshold value n. The Y comparator section  48   b  compares the edge evaluation value in the Y-direction stored in the Y evaluation value storage section  44  with the threshold value n. The Y comparator section  48   a  outputs the visible light image Visible to the Y low pass filter  46   a  when the edge evaluation value is smaller than the threshold value n but it outputs the visible light image Visible to the next X comparator section  47   b  when the edge evaluation value is not smaller than n. 
     Similarly, the X comparator section  47   b , the Y comparator section  46   b , the X comparator section  47   c  and the Y comparator section  48   c  compare the edge evaluation value with the respective threshold values and outputs the visible light image Visible to the immediately downstream low pass filter when the edge evaluation value is smaller than the respective threshold values but they output the visible light image Visible to the next comparator section when the edge evaluation value is not smaller than the respective threshold values. 
       FIG. 5  is a flowchart of operation of the low pass filter  33 . The low pass filter  33  firstly inputs the infrared image Infr (Step S 1 ). The X edge detecting section  41  detects edges that are found in the X-direction in the infrared image Infr. The X evaluation value storage section  43  computationally determines the edge evaluation value in the X-direction by applying predetermined absolute value computations to the edges detected in the X-direction and stores the computationally determined edge evaluation value as X edge image (Step S 2 ). Then, the Y edge detecting section  42  detects edges that are found in the Y-direction in the infrared image Infr. The Y evaluation value storage section  44  computationally determines the edge evaluation value in the Y-direction by applying predetermined absolute value computations to the edges detected in the Y-direction and stores the computationally determined edge evaluation value as Y edge image (Step S 3 ). 
     The low pass filter  33  inputs the visible light image Visible from the gain adjustment section  31  (Step S 4 ). The low pass filter  33  then applies the input visible light image Visible to the level 1 filter  49   a  for a process in the X-direction (Step S 5 ). 
       FIG. 6  is a flowchart of the process of the level 1 filter  49   a  in the X-direction. In the process of the level 1 filter  49   a  in the X-direction, the level 1 filter  49   a  firstly initializes the coordinate Y in the Y-direction to 0 (Step S 11 ) and also initializes the coordinate X in the X-direction to 0 (Step S 12 ). The X comparator section  47   a  inputs the X evaluation value image from the X evaluation value storage section  43 . Then, the X comparator section  47   a  compares the edge evaluation value in the X-direction at the coordinates (X, Y) of the X evaluation value image with the threshold value n. If the edge evaluation value at the coordinates (X, Y) is smaller than the threshold value n (Step S 13 ; YES), it applies the X low pass filter  45   a  to the visible light image at the coordinates (X, Y) (Step S 14 ). On the other hand, if the edge evaluation value at the coordinates (X, Y) is smaller than the threshold value (Step S 13 ; NO), the process proceeds to Step S 15 . The level 1 filter  49   a  increments the value of the X-coordinate by 1 in Step S 15 . It then compares the value of the X-coordinate with the width of the visible light image Visible. If the X-coordinate is smaller than the width of the visible light image Visible (Step S 16 ; YES), the process returns to Step S 13 . On the other hand, if the X-coordinate is not larger than the width of the visible light image Visible (Step S 16 ; NO), the level 1 filter  49   a  increments the value of the Y-coordinate by 1 (Step S 17 ). The level 1 filter  49   a  compares the value of the Y-coordinate with the height of the visible light image Visible in Step S 18 . If the Y-coordinate is smaller than the height of the visible light image Visible (Step S 18 ; YES), the process returns to Step S 12 . On the other hand, if the Y-coordinate is not smaller than the height of the visible light image Visible (Step S 18 ; NO), the level 1 filter  49   a  ends the process in the X-direction. The level 1 filter compares the edge evaluation value consisting the visible image with the threshold value n. If the edge evaluation value is smaller than the threshold value n, it removes noises in the X-direction by applying the X low pass filter  45   a.    
     When the process by the level 1 filter  49   a  in the X-direction is completed in the level 1 filter, another process is executed by the level 1 filter  49   a  in the Y-direction. This process is substantially the same as the process by the level 1 filter  49   a  in the X-direction illustrated in  FIG. 6 . The level 1 filter  49   a  in the Y-direction uses the Y edge image instead of the X edge image as image to be evaluated and the Y low pass filter  46   a  instead of the X low pass filter  45   a  as low pass filter (Step S 6 ). 
     As the level 1 filter  49   a  completes its own process, it outputs the visible light image Visible to the level 2 filter  49   b . The level 2 filter  49   b  executes a process in the X-direction (Step S 7 ). As it completes the process in the X-direction, it then executes another process in the Y-direction (Step S 8 ). As the level 3 filter  49   c  receives the output image of the level 2 filter  49   b , it executes a process in the X-direction (Step S 9 ) and, as it completes the process in the X-direction, it executes another process in the Y-direction (Step S 10 ). The processes executed by the level 2 filter and the level 3 filter are the same except that the two filters use different respective threshold values. 
     The number of times of filtering operations that the low pass filter  33  carries out is large for pixels showing a low edge evaluation value and small for pixels showing a high edge evaluation value. In other words, an edge is preserved at a pixel having a large evaluation value because of the small number of filtering operations carried out there, whereas a noise is removed at a pixel having a small evaluation value because of the large number of filtering operations carried out there. A filter having such a functional feature is referred to as edge preservation filter. In terms of the types of edge preservation filters, there are bilateral filters and cross bilateral filters besides those illustrated in  FIG. 4 . Any of these filters may be used for the low pass filter  33 . 
     The low pass filter  33  outputs an image obtained by removing noises from the visible light image Visible. This image is referred to as base image Base. The base image Base provides an advantage that the color tone of the image is correct and a disadvantage that the details of edges are not clear and gives an impression of a blurred image. 
     The high pass filter  34  extracts edges of the infrared image Infr.  FIG. 7  is a schematic block diagram of the high pass filter  34 , showing a typical configuration thereof. The high pass filter  34  in  FIG. 7  is a two-dimensional FIR filter. The high pass filter  34  is formed by using a low pass filter  71  and a division section  72 . The low pass filter  71  is typically an edge preservation type low pass filter. The low pass filter  71  removes noises from the visible light image Visible and outputs the obtained image to the division section  72 . The division section  72  divides the infrared image Infr by the output of the low pass filter  71  and extracts the high pass components of the infrared image Infr. The image output from the high pass filter  34  preserves the edges and the details of the infrared image Infr. This image is referred to as detail image Edge. 
     The image synthesizing section  35  generates a synthetic image by multiplying the base image Base by the detail image Edge. This image is the output image OUT of the image processing section  3 . The output image OUT has the advantages of the base image Base and those of the detail image Edge and is characterized in that it provides the correct color tone and clearly shows details. 
     As described above, the image pickup apparatus  1  of this embodiment provides an output image OUT by synthetically combining a base image Base obtained by removing noises from a visible light image Visible and a detail image Edge obtained by extracting edges and details of a corresponding infrared image Infr. Thus, the output image OUT is obtained by removing noises from the visible light image Visible and contains edges and details attenuated by the removal of noises. 
     Since the infrared image Infr can be picked up simultaneously with the visible light image Visible, no time lag arises between the two images and hence the image processing method of the invention can be applied to moving pictures and to operations of picking up a moving subject. 
     Known image processing apparatus are adapted to extract edges from a flash image so that the cost of arithmetic operations for removing shadows and high lights that can be produced due to differences of lighting conditions between a natural light image and a flash image is enormous. However, according to the present invention, the infrared image Infr can be picked up under lighting conditions same as those of the visible light image Visible. In other words, it is no longer necessary to correct the differences of lighting conditions. 
     Additionally, known image processing apparatus are accompanied by the problem that the pixels of details are discarded when the apparatus mistakes details and edges for high lights. On the other hand, the infrared image Infr and the visible light image Visible are picked up under the same lighting conditions so that shadows and high lights would not be produced due to differences of lighting conditions and hence no necessary pixels would be discarded. 
     Now, a first modified image processing section  3  will be described by referring to  FIG. 8 . This image processing section  50  detects edges by using the luminance of the visible light image Visible. Since luminance has only a single variable, the cost of arithmetic operations for detecting edges will be much less than that of arithmetic operations for detecting edges from the three variables of RGB. Since human beings are generally sensitive to luminance but not to color components, it is sufficiently effective to detect edges by using luminance. 
       FIG. 8  is a schematic block diagram of the image processing section  50 . The image processing section  50  includes a gain adjustment section  52 , a matrix section  53 , a color low pass filter  54 , a luminance low pass filter  55 , a high pass filter  56 , an image synthesizing section  57  and an inverse matrix section  58 . 
     The gain adjustment section  52  boosts the pixel values of the visible light image Visible that may be an underexposed dark image to make it show pixel values close to those of a correctly exposed image. Techniques that can be used for gain adjustment include one that multiplies pixel values of the visible light image by a constant and one that involves the use of gamma correction based on an exponential function or some other correction based on a polynominal function. The maximum value is limited for the pixels after adjustment. 
     The matrix section  53  transforms an RGB image into color images Cb, Cr and a luminance image Yd by means of matrix transformation of the RGB image. The color low pass filter  54  removes noises from the color images Cb, Cr. A bilateral filter may typically be used for the color low pass filter  54 . A bilateral filter can detect edges and remove noises from a single image. 
     The luminance low pass filter  55  removes noises from the luminance image Yd. A cross bilateral filter may typically be used for the luminance low pass filter  55 . A cross bilateral filter can remove noises from the image that is the object of filtering while preserving the edges detected from the image used for detecting edges. The luminance low pass filter  55  detects edges from an infrared image Infr and removes noises from the luminance image Yd. The image output from the luminance low pass filter  55  is referred to as base image Base. The base image Base has an advantage that the luminance of the image is correct and a disadvantage that it can give an impression of a blurred image because edges and details are not clear. 
     The high pass filter  56  extracts edges from the infrared image Infr. The image output from the high pass filter  56  preserves edges and details of the infrared image Infr. This image is referred to as detail image Edge. 
     The image synthesizing section  57  generates a synthetic image by multiplying the base image Base by the detail image Edge. This synthetic image has the advantages of the base image Base and those of the detail image Edge and is characterized in that it provides the correct luminance and clear details and edges. The inverse matrix section  58  transforms the luminance image into an RGB image by means of inverse matrix transformation of the synthetic image. This image is the output image OUT of the image processing section. 
     The image processing section  50  produces color images Cr, Cb and a luminance image Yd by transforming the RGB image and detects edges only from the luminance image Yd. Since human beings are generally sensitive to luminance but not to color components, it is sufficiently effective to detect edges by using luminance. Since luminance has only a single variable, the cost of arithmetic operations for detecting edges will be much less than that of arithmetic operations for detecting edges from the three variables of RGB. 
     Now, a second modified image processing section  3  will be described by referring to  FIG. 9 . As shown in  FIG. 9 , this image processing section  30  includes an aberration correcting section  32  for correcting the aberration of an infrared image. The configuration of the image processing section  30  is same as that of the image processing section  3  except that the former includes an aberration correcting section  32 . Therefore, the components same as those of the image processing section  3  will be denoted by the same reference symbols respectively and will not be described any further. 
     The aberration correcting section  32  corrects the aberration that is produced due to the difference of wavelength between infrared rays and rays of visible light.  FIG. 10  shows a configuration of the aberration correcting section  32 . The aberration correcting section  32  includes a matrix section  61 , an error computing bilinear scaler  62 , an aberration correcting bilinear scaler  63 , a parameter computing section  64  and an error computing section  65 . 
     The matrix section  61  generates a luminance image Yd from the input visible light image Visible. The error computing bilinear scaler  62  generates five different scale transformation images and distortion transformation images on the basis of the scale values and the distortion values output from the parameter computing section  64 . 
     The error computing section  65  compares the scale transformation images and the infrared image and the distortion transformation images and the infrared image and computes the error values of the images. The error values are PSNR values (peak signal to noise ratio). The parameter computing section  64  optimizes the scale values and the distortion values by referring to the PSNR values computed by the error computing section  65 . The aberration correcting bilinear scaler  63  corrects the aberration of the visible light image Visible, using the scale value and the distortion value optimized by the parameter computing section  64 . 
       FIG. 11  is a flowchart of operation of the aberration correcting section  32 . The aberration correcting section  32  inputs the visible light image Visible and the infrared image Infr. The visible light image Visible is an RGB image (Step S 21 ). The matrix section  61  multiplies the visible light image Visible by a matrix to generate a luminance image Yd (Step S 22 ). The parameter computing section  64  initializes the maximum value, the current value and the minimum value of the scale value and the maximum value, the current value and the minimum value of the distortion value (Step S 23 ). 
     The parameter computing section  64  inputs the PSNR values from the error computing section  65  and determines the scale value that maximizes the PSNR values (Step S 24 ). 
       FIG. 12  is a flowchart of the scale value computing process. The parameter computing section  64  prepares five parameters including the maximum value S 1  of the scale value, the median value S 2  of the maximum value and the current value, the current value S 3 , the median value S 4  of the minimum value and the current value and the minimum value S 5 . The error detecting bilinear scaler  62  carries out a scale transformation of the luminance image Yd, using the five scale values. As a result, five luminance images Yd 1  through Yd 5  are generated to correspond to the five parameters (Step S 31 ). 
     The error computing section  65  compares the luminance images Yd 1  through Yd 5  and the infrared image Infr to determine five PSNR values (Step S 32 ). If the PSNR value of the luminance image Yd 1  generated by the scale transformation, using the maximum value S 1  and that of the infrared image Infr, are largest (Step S 33 ; YES), the parameter computing section  64  replaces the current value S 3  with the maximum value S 1  and the maximum value S 1  with the value obtained by doubling the maximum value S 1  and subtracting the minimum value S 5  from the doubled maximum value S 1  (Step S 34 ). 
     If the PSNR value is largest for the median value of the maximum value S 1  and the current value S 3  (Step S 35 ; YES), the parameter computing section  64  replaces the maximum value S 5  with the current value S 3  and the current value S 3  with the median value of the maximum value S 1  and the current value S 3  (Step S 36 ). 
     If the PSNR value is largest for the current value S 3  (Step S 37 ; YES), the parameter computing section  64  replaces the maximum value S 1  with the current value S 3  and the current value S 3  with the median value of the current value S 3  and the minimum value S 5  (Step S 38 ). 
     If the PSNR value is largest for the median value of the minimum value S 5  and the current value S 3  (Step S 39 ; YES), the parameter computing section  64  replaces the current value S 3  with the minimum value S 5  and the minimum value S 5  with the value obtained by doubling the maximum value S 5  and subtracting the maximum value S 1  from the doubled minimum value S 5  (Step S 40 ). 
     If the PSNR value is largest for the minimum value S 5  (Step S 33 , Step S 35 , Step S 37 , Step S 39 ; NO), the parameter computing section  64  replaces the current value S 3  with the minimum value S 5  and the minimum value S 5  with the value obtained by doubling the minimum value S 5  and subtracting the maximum value S 5  from the doubled minimum value S 5  (Step S 41 ). In this way, the process of optimizing the scale value is completed. 
     Then, the parameter computing section  64  optimizes the distortion value. The process of optimizing a distortion value is identical with the process of optimizing a scale value. To begin the process of optimizing the distortion value, parameters are converted from scale values to distortion values (Step S 25 ). As the process of optimization of scale values and distortion values is completed, the parameter computing section  64  compares the extent of improvement of the PSNR value with a predetermined threshold value. If the extent of improvement of the PSNR value is not greater than a predetermined threshold value (Step S 26 ; NO), the processing operation returns to Step S 25  to repeat the process of optimizing the scale value and the distortion value. 
     If, on the other hand, the extent of improvement is greater than the predetermined threshold value (Step S 26 ; YES), the parameter computing section  64  outputs the current scale value and distortion value to the aberration correcting bilinear scaler  63 . The aberration correcting bilinear scaler  63  corrects the aberration of the visible light image Visible, using the current scale value and distortion value (Step S 27 ), and outputs the corrected image to the high pass filter  56  (Step S 28 ). 
     Due to the provision of the aberration correcting section  32 , it is now possible to correct the difference of aberration that is produced by the difference of wavelength between rays of visible light and infrared rays. As a result, the image picked up as visible light image Visible and the image picked up as infrared image Infr agree with each other. 
     Generally, the refractive index of infrared rays is high and expanded to a slight extent. A large image is obtained by correcting the visible light image Visible so that the distortion of the image will also be boosted. Since there is a tradeoff between the size and the distortion of an image, the corrected image may be different depending on if priority is given to size or to distortion. 
     The filtering process and the aberration correcting process of the image processing section  3  may be executed according to a control program. Such a control program may be recorded on the firmware of the image pickup apparatus  1 . Alternatively, the external recording apparatus  10  may acquire the control program that is recorded in a recording medium in a form readable to the external recording apparatus  10 . Recording mediums that can be used for recording the control program include those that operate with a magnetic reading system (e.g., magnetic tapes, flexible discs, magnetic cards), those that operate with an optical reading system (e.g., CD-ROMs, MOs, CD-Rs, DVDs) and semiconductor memories (e.g., memory cards, IC cards). The control program may be acquired by way of the Internet or some other network. 
     It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and alterations may occur depending on design requirements and other factors insofar as they are within the scope of the appended claims and the equivalents thereof.