JP4763415B2 - Image processing device - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an image processing apparatus for efficiently applying restoration processing to a deteriorated image due to a camera shake. <P>SOLUTION: The image processing apparatus includes a processing section 4 that restores a preceding image before being changed from an original image of a processing object, or an image having to be substantially photographed, or an image closer to the images. The processing section 4 utilizes vibration data configuring causes to image deterioration to carry out the restoration processing and the vibration data utilized for the restoration processing are data with a frequency range in excess of a frequency of 0 Hz and up to F Hz (20&le;F&le;120). Moreover, the image processing apparatus includes a detection section 6 for detecting the vibration data and preferably adopts its sampling frequency used for the detection to be any frequency from 60 to 240 Hz. Further, the vibration data are preferably limited to a frequency band from several Hz to several tens of Hz in a frequency space. <P>COPYRIGHT: (C)2007,JPO&amp;INPIT

Description

  The present invention relates to an image processing apparatus such as a digital camera or a digital video camera.

  Conventionally, it is known that when a subject is photographed by an image processing apparatus such as a camera, the photographed image sometimes deteriorates. As a factor of image deterioration, there is camera shake at the time of photographing. Therefore, a technique is proposed in which the frequency characteristics for correcting degraded images caused by camera shake are known in advance for the user (photographer) of the photographic device, customized for the shooter, and then sold. (See Patent Document 1).

JP 2001-346094 A

  When the technique of Patent Document 1 is adopted, when a photographic device once sold is lent or transferred to another person, the blur correction is not appropriate, and the corrected image is not a beautiful image. In order to correct the blur appropriately, first, the borrower or the transferred person needs to make the frequency characteristic table again before photographing.

  Accordingly, in order to solve the above-described problems, the problem to be solved by the present invention is to provide an image processing apparatus that efficiently restores deterioration due to camera shake.

To solve the above problem, images processing device of the present invention was to be the previous image or original shooting changing an imaging unit having an imaging device, from the original image generated by the imaging by the imaging unit images Or a processing unit that restores these approximate images (hereinafter referred to as original images) by data processing using change factor information indicating dispersion of light energy corresponding to pixels at the time of capturing the original image, The processing unit generates, from predetermined image data, comparison data in which light energy is dispersed according to a dispersion method indicated by change factor information at the time of capturing the original image, and the original image data and the comparison data are generated. When the difference data is larger than the predetermined value or greater than the predetermined value, the difference data is distributed to the predetermined image data according to the way of dispersion of the light energy indicated by the change factor information. The first process of generating data is executed, the process of generating new restored data using the restored data instead of the predetermined image data in the first process is repeatedly executed, and the difference data is less than or equal to the predetermined value Alternatively, the second process of generating restoration data smaller than a predetermined value is executed, and the processing unit generates image data using change factor information at the time of shooting from image data different from the original image data. Then, the first process and the second process are executed by using the image data obtained by superimposing the created image data and the original image data as predetermined image data, and the difference data obtained by the execution is obtained. The original image data is obtained by removing other image data from the restored data that is the basis of the restored data that is equal to or less than the predetermined value or smaller than the predetermined value .

According to the present invention, since the original image is generated only by generating the predetermined data using the factor information of the image change, there is almost no increase in hardware and the apparatus is not enlarged. Also, the process of creating comparison data from the restored data and comparing the comparison data with the original image data to be processed is repeated, and the original image is gradually obtained, which is a realistic restoration work. For this reason, when restoring an image, an image processing apparatus having a realistic circuit processing method can be obtained. Further, according to the present invention, since the comparison data is generated using the change factor information such as the image deterioration and the comparison is made with the original image, and the original image is generated only when the difference is small, the hardware There is almost no increase, and the device does not become large. Also, the process of creating comparison data from the restored data and comparing the comparison data with the original image data to be processed is repeated, and the original image is gradually obtained, which is a realistic restoration work. For this reason, when restoring an image, an image processing apparatus having a realistic circuit processing method can be obtained. Further, according to the present invention, when the image data to be restored includes a sudden contrast change, the sudden contrast change can be reduced by adding another image data, and the restoration process can be performed. The number of iterations can be reduced.

  The image processing apparatus of the present invention can provide an image processing apparatus that efficiently restores deterioration due to camera shake.

  Hereinafter, an image processing apparatus 1 according to an embodiment of the present invention will be described with reference to the drawings. The image processing apparatus 1 is a consumer camera. However, the image processing apparatus 1 may be a camera for other uses such as a surveillance camera, a television camera, a handheld video camera, an endoscope camera, a microscope, a binocular, Can also be applied to devices other than cameras, such as diagnostic imaging apparatuses such as NMR imaging.

  FIG. 1 shows an outline of the configuration of the image processing apparatus 1. The image processing apparatus 1 includes a photographing unit 2 that photographs an image of a person, the like, a control system unit 3 that drives the photographing unit 2, and a processing unit 4 that processes an image photographed by the photographing unit 2. ing. The image processing apparatus 1 according to this embodiment further includes a recording unit 5 that records an image processed by the processing unit 4 and an angular velocity sensor, and detects change factor information that causes a change such as image degradation. And a factor information storage unit 7 for storing known change factor information that causes image degradation and the like.

  The photographing unit 2 includes a photographing optical system having a lens and a photographing element such as a CCD or C-MOS that converts light passing through the lens into an electric signal. The control system unit 3 controls each unit in the image processing apparatus, such as the photographing unit 2, the processing unit 4, the recording unit 5, the detection unit 6, and the factor information storage unit 7.

  The processing unit 4 is configured by an image processing processor, and is configured by hardware such as an ASIC (Application Specific Integrated Circuit). The processing unit 4 may perform processing for reducing the number of pixels of the image captured by the imaging unit 2. The processing unit 4 generates a sampling frequency for detecting vibrations such as camera shake to be detected and supplies the sampling frequency to the detection unit 6. The processing unit 4 controls the start and end of vibration detection. The vibration detection period is controlled by a timer or the like disposed in the processing unit 4.

  In addition, the processing unit 4 may store an image serving as a base when generating comparison data to be described later. Further, the processing unit 4 is not configured as hardware such as an ASIC, but may be configured to perform processing by software. The recording unit 5 is composed of a semiconductor memory. However, a magnetic recording unit such as a hard disk drive, an optical recording unit using a DVD, or the like may be employed.

  As shown in FIG. 2, the detection unit 6 includes two angular velocity sensors that detect the speeds around the X axis and the Y axis that are perpendicular to the Z axis that is the optical axis of the image processing apparatus 1. is there. Further, the detection unit 6 may be provided with means for recognizing the presence or absence of contact between the photographer and the image processing apparatus 1 and determining the vibration data detection start time as necessary. The means is, for example, the position of the camera that the photographer will almost always touch when holding the camera for taking a picture (this is different depending on the shape of the camera, but cannot be generally stated, for example, shutter etc. A button for starting shooting or a peripheral portion thereof), a press sensor, a contact sensor, and the like.

  By the way, camera shake at the time of shooting with a camera may cause movement in each direction of the X direction, Y direction, and Z direction, and rotation around the Z axis, but it is Y axis that is most affected by each variation. Rotation around the X axis. These two variations are only slightly varied, and the captured image is greatly blurred. For this reason, in this embodiment, only two angular velocity sensors around the X axis and the Y axis in FIG. 2 are arranged. However, for the sake of completeness, an angular velocity sensor around the Z axis may be further added, or a sensor for detecting movement in the X direction or the Y direction may be added. In particular, when vibrations such as camera shake generated by a photographer are detected before or after shooting (exposure), vibrations in all directions unrelated to shooting may be assumed. It is preferable to further add an angular velocity sensor. The sensor used may be an angular acceleration sensor instead of an angular velocity sensor.

  Moreover, in the detection part 6, the frequency of the vibration to detect is made into the range exceeding 0 Hz and 120 Hz. Here, the vibration to be detected can be changed within a frequency range of 0 Hz and FHz (20 ≦ F ≦ 120). Therefore, the frequency of vibration to be detected can be set to over 60 Hz, 60 Hz, 3 Hz to 60 Hz, 5 Hz to 60 Hz, 1 Hz to 45 Hz, 4 Hz to 30 Hz, 2 Hz to 15 Hz, or the like. The sampling frequency is preferably about 1.5 to 10 times the maximum frequency of vibration to be detected, in terms of accurate detection, and more preferably 2 to 8 times. Here, even if sampling is performed at a frequency exceeding 8 times, the detection accuracy is often the same as when it is 8 times. Therefore, for example, when the maximum frequency of the vibration to be detected that is assumed is 15 Hz, that is, when the upper limit of the camera shake frequency is 10 and several Hz, it is only necessary to measure the vibration of 15 Hz. It is preferable to set it within one of these ranges. The lower limit of 30 Hz may be set to 40 Hz in order to improve accuracy. Note that the detection unit 6 may detect all vibrations and limit the data used in the processing unit 4 to any data within the range from 0 Hz to FHz.

  Here, in general, the frequency range from 0 Hz to 20 Hz substantially matches the frequency range of vibrations generated by the human body in an unconscious state. However, the first reason for expanding the detectable range of the vibration frequency to be detected exceeding 0 Hz to 120 Hz is to ensure the detection of human-generated vibration that can exceed 20 Hz. is there. The second reason is to detect vibrations exceeding 20 Hz generated by other than the human body as detection targets, and to detect vibrations that can affect image changes within a reasonable range. Here, the vibration generated by a person other than the human body is, for example, a vibration generated by the vehicle when the photographer is riding a vehicle such as an automobile. However, when designing a product with priority given to saving the sensing cost and the amount of blur information and reducing the restoration processing burden of the image processing apparatus, the frequency of vibration to be detected is several Hz (eg, 1, 2, 3, etc.). It is preferable to employ a configuration such as limiting from 4 or 5 Hz to a few dozen Hz (for example, 12, 13, 14, 15, 16 or 17 Hz), 20, 30, 40, 50 or 60 Hz.

  The factor information storage unit 7 is a recording unit that stores change factor information such as known deterioration factor information, such as a point spread function calculated based on aberrations of the optical system or detected vibrations. When recording the point spread function, if the detection unit 6 detects vibrations before and / or after the end of shooting, the vibration generated by the photographer is detected, for example, during the entire detection period. Of the detected vibration data, the data to be used is limited to the camera shake frequency band, and a point spread function or the like calculated using the data of the exposure period is recorded. When the vibration detection range is limited to the low frequency range as described above, it is not necessary to limit the camera shake frequency band. However, in order to reduce measurement errors and speed up the processing time, the camera shake frequency is anyway. It is preferable to limit the frequency to only a few Hz to a few dozen Hz. In a situation where the photographer is on a vehicle or the like, it is preferable not to perform the process of limiting to the frequency band of camera shake. The camera shake frequency band limiting process is preferably operated by the photographer by providing an on / off switch in the image processing apparatus 1.

  The point spread function recorded by the factor information storage unit 7 is used by the processing unit 4 in, for example, the restoration process of the original image taken most recently after the calculation. In the next shooting, the data from the end of the previous shooting to the end of the next shooting or from the data of the period from the start of the next shooting to the elapse of a predetermined period after the end of the next shooting is similarly used. The calculated and recorded point spread function is used in the original image restoration process. Here, when the original image restoration process is executed, the original image is taken when the imaging power is turned off, when the processing unit 4 is not operating, or when the operating rate of the processing unit 4 is low. It can be a period delayed from In that case, the original image data stored in the recording unit 5 and the change factor information such as the point spread function for the original image stored in the factor information storage unit 7 are long in a state where they are associated with each other. Stored for a period of time. As described above, the advantage of delaying the time for executing the restoration process of the original image from the time of shooting the original image is that the burden on the processing unit 4 at the time of shooting involving various processes can be reduced.

  When calculating this point spread function, the period during which the detection unit 6 detects the photographer's vibration is fixed and set in advance within a range from 1 second before the start of shooting (exposure) to 1 second after the end of shooting. Can be kept. Here, since it is “within range”, the detection period is, for example, a range from 20 milliseconds before the start of shooting to 20 milliseconds after the end of shooting, a range from 20 milliseconds before the start of shooting to the end of shooting, or at the start of shooting. Or the like after 20 milliseconds from the end of shooting. Thus, including at least the shooting period (exposure period) from the start of shooting to the end of shooting in the vibration detection period reflects the vibration during the actual shooting period in the vibration data, and so on. Can be recorded in the factor information storage unit 7. Here, if a part of the shooting period from the start of shooting is included in the detection period, vibration data in the entire shooting period can be predicted to some extent, so that it is preferable to include the entire shooting period in the detection period. Therefore, the detection period can be from before the start of shooting to a part of the shooting period.

  Here, a detailed description will be given of a case where the period for detecting the vibration of the photographer is set to be a period within a range from 1 second before the start of shooting to 1 second after the end of shooting.

  During the shooting period of the image processing apparatus 1, camera shake vibration given to the image processing apparatus 1 by the photographer is consciously suppressed. And that consciousness generally works from the start of shooting to the end of shooting. On the other hand, in a period other than before the end of shooting after the start of shooting, the photographer's vibration is often accompanied by an action other than camera shake, such as an action of walking while holding the image processing apparatus 1. . Therefore, by adopting this configuration, such movements and vibrations can be excluded from detection targets, and vibrations that include a lot of appropriate vibrations can be detected as camera shake vibrations generated by the human body. Further, since the point data function or the like is calculated and recorded in the factor information storage unit 7 using the vibration data, the restoration processing accuracy can be improved.

  On the other hand, when viewed from the image processing apparatus 1 side, immediately after the start of vibration detection, vibration generated continuously before the start of detection is detected. Here, any high-performance angular velocity sensor or angular acceleration sensor has a slight response delay, so it is difficult for the detection unit 6 to detect the vibration following the vibration immediately after the start of detection. Therefore, if the vibration detection timing coincides with the shooting (exposure) start timing, the vibration data immediately after the start of shooting becomes inaccurate. Thus, by detecting vibration from one second before the start of shooting, vibration data immediately after the start of shooting can be made accurate. Then, using vibration data for only a period corresponding to the shooting period among vibration data from 1 second before the start of shooting to 1 second after the end of shooting, a more accurate point spread function can be calculated. Furthermore, by collecting vibration data other than the shooting period and the shooting period, the vibration data is increased and more accurate vibration data is obtained, so that the accuracy of the restored image is increased.

  Here, it goes without saying that the period for detecting the vibration can be arbitrarily set. Of course, the period from the start of vibration detection to the start of shooting can be made different from the period from the end of shooting to the end of vibration detection. When this configuration is adopted, it is preferable that the photographer can adjust and set the vibration detection photographing period. The reason is that image restoration processing can be performed in consideration of operations peculiar to individual photographers.

  Instead of controlling by time as described above, vibration data is detected when the shutter or the like is half-pressed, or vibration data is always recorded when the photographing power is turned on, and the latest number is always updated. Second-time data may be stored, and vibration data for a predetermined time before exposure and for a predetermined time after completion of exposure may be used in addition to vibration data during exposure when the shutter is pressed.

  In this embodiment, the factor information storage unit 7 stores information on aberrations of the optical system and lens distortion. However, when restoring blurring of camera shake described later, the information is Not used.

  Next, an outline of the image restoration processing method of the processing unit 4 of the image processing apparatus 1 configured as described above will be described with reference to FIG.

In FIG. 3, “I ₀ ” is an arbitrary initial image and is image data stored in advance in the recording unit of the processing unit 4. “I ₀ ′” indicates data of a degraded image of I ₀ of the initial image data, and is comparison data for comparison. “G” is data of change factor information (= deterioration factor information (point image function)) detected by the detection unit 6 and is stored in the recording unit of the processing unit 4. “Img ′” indicates captured image data, that is, degraded image data, and is data of an original image to be processed in this processing.

“Δ” is difference data between the original image data Img ′ and the comparison data I ₀ ′. “K” is an allocation ratio based on the data of the change factor information. “I ₀ + n” is restored image data (restored data) newly generated by allocating the difference data δ to the initial image data I ₀ based on the data of the change factor information. “Img” is data of the original image. Here, it is assumed that the relationship between Img and Img ′ is expressed by the following equation (1).
Img ′ = Img × G (1)

The difference data δ may be a simple difference between the corresponding pixels, but generally differs depending on the data G of the change factor information and is expressed by the following equation (2).
δ = f (Img ′, Img, × G) (2)

Processing routine of the processing unit 4 first begins to prepare any image data _{I 0} (step S101). As the initial image data I ₀ , the deteriorated original image data Img ′ may be used, and any image data such as black solid, white solid, gray solid, or checkered pattern may be used. good. In step S102, data I ₀ of an arbitrary image that is an initial image is inserted instead of Img in equation (1), and comparison data I ₀ ′ that is a degraded image is obtained. Next, the original image data Img ′, which is a deteriorated image, is compared with the comparison data I ₀ ′, and difference data δ is calculated (step S103).

Next, in step S104, it is determined whether or not the difference data δ is equal to or larger than a predetermined value. If the difference data δ is equal to or larger than the predetermined value, a process for generating new restored image data (= restored data) in step S105. I do. That is, the difference data δ is distributed to arbitrary image data I ₀ based on the data G of the change factor information, and new restored data I ₀ + n is generated. Thereafter, steps S102, S103, and S104 are repeated.

If the difference data δ is smaller than the predetermined value in step S104, the process is terminated (step S106). Then, the restored data I ₀ + n at the time when the processing is completed is estimated as the original image data Img and the data is recorded in the recording unit 5. The initial image data I ₀ and change factor information data G may be recorded in the recording unit 5 and transferred to the processing unit 4 as necessary.

  The concept of the above processing method is summarized as follows. That is, in this processing method, the processing solution is not solved as an inverse problem, but is solved as an optimization problem for obtaining a rational solution. When solving as an inverse problem, it is theoretically possible, but it is difficult as a real problem.

Solving as an optimization problem assumes the following conditions.
That is,
(1) The output corresponding to the input is uniquely determined.
(2) If the output is the same, the input is the same.
(3) The solution is converged by iteratively processing while updating the input so that the outputs are the same.

In other words, as shown in FIGS. 4A and 4B, if comparison data I ₀ ′ (I ₀ + n ′) that is approximate to the original image data Img ′ can be generated, The data I ₀ or the restored data I ₀ + n of the initial image as data is approximate to the data Img of the original image.

  In this embodiment, the sampling rate of the angular velocity detection sensor is set within 60 Hz to 240 Hz. However, the angular velocity may be detected every 5 μsec so that a high frequency can be detected. In addition, in this embodiment, the value serving as a determination criterion for the difference data δ is “6” when each data is represented by 8 bits (0 to 255). That is, when it is less than 6, that is, 5 or less, the processing is finished.

  Next, details of the processing method shown in FIGS. 3 and 4 will be described based on FIGS. 5, 6, 7, 8, 9, 10, 11, and 12.

(Image restoration algorithm)
When there is no camera shake, the light energy corresponding to a given pixel is concentrated on that pixel during the exposure time. In addition, when there is camera shake, light energy is dispersed to pixels that are blurred during the exposure time. Further, if the blur during the exposure time is known, it is possible to know how the energy is dispersed during the exposure time, so that it is possible to create a blur-free image from the blurred image.

  Hereinafter, for the sake of simplicity, the description will be made in one horizontal dimension. The pixels are designated as n-1, n, n + 1, n + 2, n + 3,... In order from the left, and attention is paid to a certain pixel n. When there is no blur, the energy during the exposure time is concentrated on the pixel, so the energy concentration is “1.0”. This state is shown in FIG. The imaging results at this time are shown in the table of FIG. What is shown in FIG. 6 is the correct image data Img when no deterioration occurs. Each data is represented by 8 bits (0 to 255).

  There is blurring during the exposure time, 50% of the exposure time is blurred to the nth pixel, 30% of time is shifted to the n + 1th pixel, and 20% of time is shifted to the n + 2th pixel. Suppose that The way of energy dispersion is as shown in the table of FIG. This becomes the data G of the change factor information.

  Since blurring is uniform for all pixels, if there is no upper blurring (vertical blurring), the blurring situation is as shown in the table of FIG. The data shown as “imaging result” in FIG. 8 is the original image data Img, and the data shown as “blurred image” is the degraded original image data Img ′. Specifically, for example, “120” of the pixel “n−3” is in accordance with the distribution ratio of “0.5”, “0.3”, and “0.2” of the data G of the change factor information that is the blur information. It is distributed such that “60” is distributed to “n-3” pixels, “36” is distributed to “n-2” pixels, and “24” is distributed to “n-1” pixels. Similarly, “60” which is pixel data of “n−2” is distributed as “30” in “n−2”, “18” in “n−1”, and “12” in “n”. The original image is calculated from the deteriorated original image data Img 'and the change factor information data G shown in FIG.

The arbitrary image data I ₀ shown in step S101, can be adopted also What, When this description, use of Img 'to data of the original image. That is, the process starts with I ₀ = Img ′. In the table of FIG. 9, “input” corresponds to the initial image data I ₀ . The data I _0, that is, Img ′ is multiplied by the change factor information data G in step S102. That is, for example, “60” of the “n-3” pixel of the data I ₀ of the initial image is “30” for the n-3 pixel, “18” for the “n-2” pixel, “12” is assigned to each pixel of “−1”. The other pixels are similarly distributed, and comparison data I ₀ ′ shown as “output I ₀ ′” is generated. Therefore, the difference data δ in step S103 is as shown in the bottom column of FIG.

Thereafter, the size of the difference data δ is determined in step S104. Specifically, the processing is terminated when all the difference data δ is 5 or less in absolute value, but the difference data δ shown in FIG. 9 does not meet this condition, so the process proceeds to step S105. That is, the difference data δ is distributed to arbitrary image data I ₀ using the data G of the change factor information to generate restored data I ₀ + n shown as “next input” in FIG. In this case, since this is the first time, it is expressed as I ₀ +1 in FIG.

The distribution of the difference data δ is, for example, “15” obtained by multiplying the pixel data “30” of “n−3” by 0.5, which is the distribution ratio of own place (= “n-3” pixel). “4” which is distributed to the pixel of “n−3” and multiplied by 0.3 which is the distribution ratio which should have come to the pixel of “n−2” to the data “15” of the pixel of “n−2”. .5 ”, and the data“ 9.2 ”of the pixel“ n−1 ”is multiplied by 0.2 which is the distribution ratio that should have come to the pixel“ n−1 ”. 1.84 "is allocated. The total amount allocated to the “n−3” pixels is “21.34”, and this value is added to the initial image data I ₀ (in this case, the original image data Img ′ is used) to restore the restored data I ₀ +1 is generated.

As shown in FIG. 11, the restored data I ₀ +1 becomes the input image data (= initial image data I ₀ ) in step S102, step S102 is executed, and the process proceeds to step S103. Obtain δ. The size of the new difference data δ is determined in step S104. If it is larger than the predetermined value, the new difference data δ is distributed to the previous restored data I ₀ +1 in step S105 to generate new restored data I ₀ +2. (See FIG. 12). After that, new comparison data I ₀ +2 ′ is generated from the restored data I ₀ +2 by performing step S102. As described above, after steps S102 and S103 are executed, the process goes to step S104, and the process proceeds to step S105 or shifts to step S106 depending on the determination there. Such a process is repeated.

  In this image processing apparatus 1, in processing, in step S <b> 104, one or both of the number of processes and the determination reference value of the difference data δ can be set in advance. For example, an arbitrary number such as 20 times or 50 times can be set as the number of times of processing. Further, the value of the difference data δ for stopping the processing is set to “5” in 8 bits (0 to 255), and when it becomes 5 or less, the processing is ended, or “0.5” is set to “0”. .5 "or less, the process can be terminated. This set value can be set arbitrarily. When both the number of processing times and the criterion value are input, the processing is stopped when either one is satisfied. When both settings are possible, the determination reference value may be given priority, and if the predetermined number of processes does not fall within the determination reference value, the predetermined number of processes may be repeated.

  In the description of this embodiment, the information stored in the factor information storage unit 7 is not used, but known deterioration factors stored here, such as optical aberrations and lens distortions, are used. It may be used. In this case, for example, in the processing method (repetitive processing) of the previous example (FIG. 3), it is preferable to perform processing by combining blur information and optical aberration information as one deterioration factor. After the process in step S3 is completed, the restoration process using the optical aberration information may be performed. Further, the factor information storage unit 7 may not be installed, and the image may be corrected or restored only by dynamic factors at the time of shooting, for example, only blurring.

  The image processing apparatus 1 according to the embodiment of the present invention has been described above, but various modifications can be made without departing from the gist of the present invention. For example, the processing performed by the processing unit 4 is configured by software, but may be configured by hardware composed of parts that perform part of the processing.

  In addition to the original image, the image to be processed may be a processed image such as color-corrected or Fourier-transformed. In addition to the data generated using the change factor information data G, the comparison data includes data generated using the change factor information data G, color correction, or Fourier transform. It is also good. Further, the data of the change factor information includes not only the data of the deterioration factor information but also information that simply changes the image and information that improves the image contrary to the deterioration.

  When the number of processing iterations is set automatically or fixedly on the image processing apparatus 1 side, the set number of times may be changed by the data G of the change factor information. For example, when the data of a certain pixel is distributed over many pixels due to blurring, the number of iterations may be increased, and when the variance is small, the number of iterations may be decreased.

  Furthermore, when the difference data δ diverges during the iterative process, that is, when the difference data δ becomes larger, the process may be stopped. For example, it is possible to adopt a method of determining whether or not the light is diverging by observing the average value of the difference data δ and determining that the light is diverging if the average value is larger than the previous value. In addition, during an iterative process, if an input is to be changed to an abnormal value, the process may be stopped. For example, in the case of 8 bits, if the value to be changed is a value exceeding 255, the processing is stopped. Further, during an iterative process, when an input that is new data is to be changed to an abnormal value, the value may not be used but may be set to a normal value. For example, when a value exceeding 255 within the 8-bit range of 0 to 255 is used as input data, it is processed as a maximum value of 255.

  In addition, when generating restoration data to be an output image, depending on the data G of the change factor information, there may be data that goes out of the area of the image to be restored. In such a case, data that protrudes outside the area is input to the opposite side. Also, if there is data that should come from outside the area, it is preferable to bring that data from the opposite side. For example, when data allocated to a lower pixel is generated from the data of the pixel XN1 positioned at the bottom in the area, the position is outside the area. Therefore, the data is processed to be allocated to the pixel X11 located at the top right above the pixel XN1. Similarly, the pixel N2 adjacent to the pixel XN1 is assigned to the topmost pixel X12 (= next to the pixel X11) directly above.

Further, when generating the restoration data I ₀ + n, the distribution ratio k is not used, and the difference data δ of the corresponding pixel is added to the corresponding pixel of the previous restoration data I ₀ + n−1 or the corresponding pixel The difference data δ is added after scaling, or the data kδ (value indicated as “update amount” in FIGS. 10 and 12) after the difference data δ is allocated is scaled, May be added to the restored data I ₀ + n−1. If these processing methods are used well, the processing speed is increased.

  Further, when the restoration data Io + n is generated, the center of gravity of a change factor such as deterioration may be calculated, and the difference of only the center of gravity or the scaling of the difference may be added to the previous restoration data Io + n-1. This concept, that is, the processing method using the center of gravity of the change factor will be described below as a first processing method using the processing method shown in FIG. 3 with reference to FIGS.

  As shown in FIG. 13, when correct image data Img is composed of pixels 11 to 15, 21 to 25, 31 to 35, 41 to 45, and 51 to 55, as shown in FIG. Attention is paid to the pixel 33. When the pixel 33 moves to the positions of the pixels 33, 43, 53, and 52 due to camera shake or the like, in the original image data Img ′ that is a degraded image, as shown in FIG. 43, 52, and 53 are affected by the first pixel 33.

  In the case of such deterioration, if the pixel 33 moves and is located at the position of the pixel 43 for the longest time, the center of deterioration, that is, the change factor is the original pixel 33 in the correct image data Img. In the image data Img ′, the pixel 43 is located. As a result, the difference data δ is calculated as the difference between the respective pixels 43 of the original image data Img ′ and the comparison data Io ′, as shown in FIG. The difference data δ is added to the pixels 33 of the initial image data Io and the restored data Io + n.

  In the above example, the three centroids of “0.5”, “0.3”, and “0.2” are the positions of “0.5” having the largest value, and are their own positions. Therefore, the allocation of “0.3” or “0.2” is not considered, and only “0.5” or 0.5 magnification of the difference data δ is allocated to its own position. Such a process is suitable when the blur energy is concentrated.

  Furthermore, each processing method described above can be automatically selected according to the contents of the data G of the change factor information. For example, as a processing method, as shown in FIGS. 5 to 12, (1) a method of allocating difference data δ using the distribution ratio k (example method), (2) a difference between corresponding pixels, Alternatively, a program that can execute three methods: a method of scaling the difference data δ (corresponding pixel method) and (3) a method of detecting the centroid of the deterioration factor and using the data of the centroid (centroid method) 4 is stored, the state of the deterioration factor is analyzed, and one of the three methods is selected based on the analysis result. Alternatively, any one of the three methods may be selected and used alternately every routine, or may be processed by a certain method for the first several times, and then processed by another method.

  In addition, there is a method combined with the inverse problem in order to speed up the restoration process. That is, for example, iterative processing is performed with reduced data obtained by subtracting the number of pixels from image data of an original image, and a transfer function from the reduced original image to reduced restored data is calculated. Then, the calculated transfer function is enlarged and interpolated, and restored data of the original image is obtained using the enlarged and interpolated transfer function. This processing method is advantageous for processing a large image (an image having a large number of pixels).

  The basic concept of high-speed processing that is advantageous for restoring a large image will be described below.

  Only iterative processing will inevitably take time to converge. This drawback becomes noticeable for large images. On the other hand, deconvolution in the frequency space is very attractive because high-speed calculation can be performed using Fast Fourier Transform (FFT). The optical deconvolution referred to here is to restore the original image which is not deteriorated by removing the distortion or the like from the image deteriorated due to distortion or blur.

In the case of an image, when the input is in (x), the output is ou (x), and the transfer function is g (x), in the ideal state, the output ou (x) is a convolution integral,
ou (x) = ∫in (t) g (x−t) dt (3)
It becomes. “∫” is an integral symbol. This equation (3) is a frequency space,
O (u) = I (u) G (u) (4)
It becomes. Deconvolution is to determine the transfer function g (x) or the unknown input in (x) from this known output ou (x). For this purpose, I (u) = O (u ) / G (u) is obtained, the unknown input in (x) can be obtained by returning it to the real space.

However, in reality, due to noise or the like, Expression (3) becomes “ou (x) + α (x) = ∫in (t) g (x−t) dt + α (x)”. Here, “ou (x) + α (x)” is known, but ou (x) and α (x) are unknown. Even if this is approximated as an inverse problem, it is practically difficult to obtain a sufficiently satisfactory solution. Therefore, iterative processing is performed on jn (x) where ou (x) + α (x) = ∫in (t) g (x−t) dt + α (x) ≈∫jn (t) g (x−t) dt. The process flow of FIG. 3 described above is obtained by converging using the method.
Here, if “α (x) << ou (x)”, it is considered that jn (x) ≈in (x).

  However, since this method iterates and converges the calculation in the entire data area, a sufficiently satisfactory solution can be obtained, but it takes a long time if the number of data increases. On the other hand, in an ideal state without noise, a solution can be obtained at high speed by deconvolution calculation in a frequency space. Thus, by combining these two processes, a sufficiently satisfactory solution can be obtained at high speed.

  As such a processing method, two methods are conceivable. The first is a method of reducing the number of pixels from which data is thinned out. When thinning out, for example, as shown in FIG. 15, when the original image data Img ′ is composed of pixels 11 to 16, 21 to 26, 31 to 36, 41 to 46, 51 to 56, 61 to 66, There is a method in which every other pixel is thinned out, and the original image reduced data ISmg ′ having a size of a quarter composed of the pixels 11, 13, 15, 31, 33, 35, 51, 53, and 55 is generated.

  In this manner, the original image data Img ′ and the change factor information data G are thinned out to generate the thinned original image reduced data ISmg ′ and the reduced change factor information data GS, and the original image reduced data ISmg ′. Using the reduced change factor information data GS, the iterative process shown in FIG. 3 is performed, and a sufficiently satisfactory thinned approximation to approximate the reduced original image ISmg before changing to the original image reduced data ISmg ′ is performed. Reduced restoration data ISo + n is obtained.

  The reduced approximate restored data ISo + n is estimated as a reduced original image ISmg before changing to the original image reduced data ISmg ', that is, a reduced image of the correct image Img. The original image reduced data ISmg ′ is considered as a convolution integral of the reduced restoration data ISo + n and the transfer function g (x), and an unknown transfer function g1 is obtained from the obtained reduced restoration data ISo + n and the known original image reduced data ISmg ′. (X) can be obtained.

  The reduced and restored data ISo + n is sufficiently satisfactory, but is only an approximation. Therefore, the transfer function g (x) of the original restoration data Io + n and the original image data Img ′ is not the transfer function g1 (x) obtained by the iterative process with the reduced data. Therefore, the transfer function g1 (x) is calculated from the reduced restoration data ISo + n and the original image reduced data ISmg ′ which is the reduced original image data, the calculated transfer function g1 (x) is enlarged, and the enlarged portion is interpolated. Then, the new transfer function g2 (x) obtained by the correction is set as a transfer function g (x) for the original image data Img ′ as the original data. The new transfer function g2 (x) is obtained by multiplying the obtained transfer function g1 (x) by the reciprocal of the reduction ratio of the original image reduction data, and thereafter, the enlarged value is interpolated such as linear interpolation or spline interpolation. It is obtained by processing. For example, as shown in FIG. 15, when the length and width are both halved, the reduction ratio is ¼, so that the reciprocal number is four times.

  Then, using the modified new transfer function g2 (x) (= g (x)), deconvolution calculation (calculation for removing blur by calculation from a group of images including blur) is performed in the frequency space, Obtain complete restoration data Io + n and estimate it as the original correct image Img (original image) that has not been degraded.

  The above processing flow is shown in the flowchart of FIG.

  In step S201, the original image data Img 'and the change factor information data G are reduced to 1 / M. In the example of FIG. 15, it is reduced to ¼. Steps S102 to S105 shown in FIG. 3 are repeated using the obtained original image reduction data ISmg ', reduction change factor information data GS, and arbitrary image (predetermined image) data Io. Then, the reduced restoration data ISo + n approximate to the reduced original image ISmg before changing to the original image reduced data ISmg 'is obtained (step S202). At this time, “G, Img ′, Io + n” shown in FIG. 3 is replaced with “GS, ISmg ′, ISo + n”.

  A transfer function g1 (x) from the original image reduced data ISmg 'to the reduced restored data ISo + n is calculated from the obtained reduced / restored data ISo + n and the known original image reduced data ISmg' (step S203). After that, in step S204, the obtained transfer function g1 (x) is enlarged by M times (4 times in the example of FIG. 15), and the enlarged portion is interpolated by an interpolation method such as linear interpolation to obtain a new transfer. The function g2 (x) is obtained. The new transfer function g2 (x) is estimated as the transfer function g (x) for the original image.

  Next, deconvolution is performed from the calculated new transfer function g2 (x) and original image data Img 'to obtain restored data Io + n. The restored data Io + n is used as an original image (step S205). As described above, a) iterative processing and b) determining transfer functions g1 (x) and g2 (x) and using the calculated new transfer function g2 (x) together, The restoration process can be speeded up.

  In the case of this process, the obtained correct image and the restored data Io + n estimated are used as the initial image data Io of the process shown in FIG. 3, and the change factor information data G and the deteriorated original image data Img ′ Further, iterative processing may be executed using

  A second method of using the reduced data is a method for obtaining original image reduced data ISmg 'by extracting data of a partial area of the original image data Img'. This method will be described as a third processing method using the processing method shown in FIG. For example, as shown in FIG. 17, when the original image data Img ′ is composed of pixels 11 to 16, 21 to 26, 31 to 36, 41 to 46, 51 to 56, 61 to 66, the center This is a method of taking out an area having a size of 1/6 consisting of pixels 32, 33, 34, 42, 43, and 44, and generating original image reduced data ISmg ′.

  This second method will be described in detail with reference to the flowchart of FIG.

  In the second method, first, in step S301, the original image reduced data ISmg 'is obtained as described above. Next, the original image reduced data ISmg ′, the change factor information data G, and the initial image data Io having the same size (= the same number of pixels) as the original image reduced data ISmg ′ in arbitrary image data are used. The processing in steps S102 to S105 shown in FIG. 3 is repeated to obtain reduced restoration data ISo + n (step S302). In this process, “Img ′” in FIG. 3 is replaced with “ISmg ′”, and “Io + n” is replaced with “ISo + n”.

  A transfer function g1 '(x) from the reduced / restored data ISo + n to the original image reduced data ISmg' is calculated from the obtained reduced / restored data ISo + n and the known original image reduced data ISmg '(step S303). Next, the calculated transfer function g1 ′ (x) is set as a transfer function g ′ (x) for the original image Img, and this transfer function g1 ′ (x) (= g ′ (x)) and the data of the known original image Using Img ′, the original image Img is obtained by inverse calculation. Note that the obtained data is actually image data that approximates the original image Img.

  As described above, it is possible to speed up the restoration process by using a) iterative processing and b) obtaining the transfer function g1 ′ (x) and using the obtained transfer function g1 ′ (x). Can be achieved. Note that the obtained transfer function g1 '(x) may be corrected using the change factor information data G without using the entire transfer function g' (x) as it is.

  Thus, in the second method for speeding up described above, the entire image area is not restored by iterative processing, but a part of the area is iteratively processed to obtain a good restored image, which is used to transmit to that part. A function g1 ′ (x) is obtained, and the entire image is restored using the transfer function g1 ′ (x) itself or a modification (enlarged or the like) thereof. However, the area to be extracted needs to be an area sufficiently larger than the fluctuation area. In the previous example shown in FIG. 5 and the like, since it fluctuates over 3 pixels, it is necessary to extract an area of 3 pixels or more.

  In the method of extracting the reduced area shown in FIGS. 17 and 18, the original image data Img ′ is divided into four parts as shown in FIG. 19, for example, and a part of the area is extracted from each divided area. Four original image reduction data ISmg ′, which are small areas, are iteratively processed, each of the four divided areas is restored, and the restored four divided images are combined into a single original image. . In addition, when dividing into a plurality of areas, it is preferable to always have an overlapping area (overlapping area) over a plurality of areas. Further, it is preferable to perform processing such as using an average value or smoothly connecting the overlap regions of the restored images in the overlap regions.

  Further, when the processing method of FIG. 3 is actually adopted, it has been found that convergence of an image with a sharp change in contrast to a good approximate restored image is slow. Thus, depending on the nature of the subject that is the original image, the convergence speed of the iterative process may be slow and the number of iterations may have to be increased. In the case of such a subject, it is estimated that this problem can be solved by adopting the following processing method.

The method is as follows. That is, for a subject with a sharp change in contrast, if the restoration process by the restoration method shown in FIG. 3 is used to obtain an approximation to the original image, the number of iterations increases and many Even after the number of processes has been performed, the restoration data I ₀ + n that approximates the original subject cannot be generated. Therefore, blur data B ′ is generated by using the data G of the change factor information at the time of photographing from the data B of the known image as data Img ′ of the photographed original image (blurred image). To create “Img ′ + B ′”. Thereafter, the superimposed image is restored by the processing shown in FIG. 3, and the data B of the added image that has been added is removed from the result data C that becomes the restored data I ₀ + n, and the restored image data Img to be obtained is taken out. .

  In this method, the correct image data Img includes a sharp contrast change, but by adding the known image data B, this sharp contrast change can be reduced and the number of iterations of the restoration process is reduced. can do.

Also, other processing methods can be adopted as a processing method for a subject that is difficult to restore and a high-speed processing method. For example, if the number of iterations of the restoration process is increased, it can be brought closer to a good restored image, but the process takes time. Therefore, by using an image obtained by a certain number of iterations, an error component included in the image is calculated, and a good restored image, that is, restored data I ₀ + n is obtained by removing the calculated error from the restored image including the error. Can be obtained.

  This method will be specifically described below. The correct original image to be obtained is A, the captured original image is A ′, the image restored from the original image A ′ is A + γ, and the blurred comparison data generated from the restored data is A ′ + γ ′. When the captured original image “A ′” is added to “A ′ + γ ′” and restored, it becomes “A + γ + A + γ + γ”, which is “2A + 3γ”, and “2 (A + γ) + γ”. is there. Since “A + γ” is obtained in the previous restoration process, “2 (A + γ) + γ−2 (A + γ)” can be calculated, and “γ” is obtained. Therefore, by removing “γ” from “A + γ”, the correct original image A to be obtained can be obtained.

  Each processing method described above, that is, (1) a method of allocating difference data δ using the distribution ratio k (a method according to the embodiment), (2) a corresponding pixel difference, or scaling of the difference data δ Method (corresponding pixel method), (3) a method of detecting the centroid of the deterioration factor and using the data of the centroid part (centroid method), (4) a method of thinning out the data and combining with the inverse problem (inverse problem decimating method) ), (5) A method for extracting a reduced area and combining it with an inverse problem (inverse problem area extraction method), (6) A method for repeatedly processing a predetermined image by overlaying, and then removing the predetermined image (measures for poor images) A program for each processing method (superposition method) and (7) a method of removing the calculated error from the restored image including the error (error extraction method) is stored in the processing unit 4 and selected by the user or the type of image The processing method may be automatically selected according to the process.

  Further, the processing unit 4 classifies the data G of the change factor information into any of a plurality of types, and performs different processing (any one of the above-described methods) for each classification, Further, the number of repetitions may be varied for each classification.

  Alternatively, any one of (1) to (7) may be stored in the processing unit 4 so that the processing method can be automatically selected according to the user's selection or the type of image. In addition, select any one of these five methods and use them alternately or in sequence every routine, or process them in one method for the first few times, and then process them in another method. Also good. Note that the image processing apparatus 1 may have a different processing method in addition to any one or a plurality of (1) to (7) described above.

  Moreover, each processing method mentioned above may be programmed. Alternatively, the program may be stored in a storage medium, such as a CD, DVD, or USB memory, and read by a computer. In this case, the image processing apparatus 1 has reading means for reading a program in the storage medium. Further, the program may be stored in an external server of the image processing apparatus 1, downloaded as necessary, and used. In this case, the image processing apparatus 1 has communication means for downloading a program in the storage medium.

  Even when the subject is photographed with the image processing apparatus 1 fixed to a tripod, the original image may be deteriorated due to vibration. The vibration in that case is vibration that propagates to the image processing apparatus 1 through the ground and the tripod when a car or a person moves near the tripod. Since the vibration in this case propagates on a relatively hard ground, it may have a higher frequency than vibrations such as camera shake. Therefore, when a tripod is attached to the image processing apparatus 1, the vibration is detected within a frequency range from 0 Hz to 240 Hz. When the tripod is not attached to the image processing apparatus 1, the vibration is detected using the frequency. It is preferable to perform the image restoration process such as an iterative process using the vibration data in the range of 0 Hz to FHz (20 ≦ F ≦ 120). When adopting such a configuration, it is more preferable to detect the presence or absence of a tripod and automatically switch the vibration detection frequency because the burden on the photographer can be reduced.

  The above-described configuration in which the vibration detection period includes the time before the start of shooting and / or the time after the end of shooting is an image processing apparatus having a processing unit that restores an original image. The restoration processing is performed by using the vibration data constituting the deterioration factor, and the vibration data used for the restoration processing is stored in the image processing apparatus in which the frequency exceeds 0 Hz and is in the range of FHz (20 ≦ F ≦ 120). It is not the only valid configuration. Even if this configuration is added to another image processing apparatus having a restoration function, for example, an image processing apparatus that detects vibrations over a wide range such as more than 0 Hz and several kHz, exposure (exposure) of the image processing apparatus ) Not only the period but also the vibration of the photographer that occurs over a long period of time can be detected, and the point spread function and the like can be calculated using the vibration data, which can advantageously improve the restoration processing accuracy. Needless to say, an effect can be obtained.

  In each of the above-described examples, the obtained vibration data is approximately restricted to the camera shake frequency band in the frequency space, but the obtained vibration data is used as it is in the real space without being restricted in the frequency space. Can do. Further, when limiting the detection data to only the camera shake frequency band, if there is a frequency different from the camera shake frequency in the vibration data, the detection data automatically used by the processing unit 4 is subjected to the camera shake. Although the frequency band may be limited, the processing unit 4 may be configured in advance so that the detection data itself is limited to the camera shake frequency band.

  In order to detect vibration for a predetermined time after shooting is completed, it is preferable to operate a timer from the end of exposure. Various methods such as a predetermined time from the time of detection may be employed.

It is a block diagram which shows the main structures of the image processing apparatus which concerns on embodiment of this invention. It is an external appearance perspective view which shows the outline | summary of the image processing apparatus shown in FIG. 1, and is a figure for demonstrating the arrangement position of an angular velocity sensor. FIG. 2 is a processing flowchart for explaining a processing routine related to an image restoration processing method (repetitive processing) performed by a processing unit of the image processing apparatus shown in FIG. 1. It is a figure for demonstrating the concept of the processing method shown in FIG. FIG. 4 is a diagram for specifically explaining the processing method shown in FIG. 3 using camera shake as an example, and is a table showing energy concentration when there is no camera shake. It is a figure for demonstrating concretely the processing method shown in FIG. 3 taking an example of camera shake, and is a figure which shows image data when there is no camera shake. It is a figure for demonstrating concretely the processing method shown in FIG. 3 taking an example of camera shake, and is a figure which shows dispersion | distribution of energy when camera shake occurs. FIG. 4 is a diagram for specifically explaining the processing method shown in FIG. 3 using camera shake as an example, and is a diagram for explaining a situation in which comparison data is generated from an arbitrary image. FIG. 3 is a diagram for specifically explaining the processing method shown in FIG. 3 using camera shake as an example, in which the comparison data and the blurred original image to be processed are compared to generate difference data It is a figure for demonstrating. FIG. 3 is a diagram for specifically explaining the processing method shown in FIG. 3 using camera shake as an example, and a diagram for explaining a situation in which restored data is generated by allocating difference data and adding it to an arbitrary image. is there. FIG. 3 is a diagram for specifically explaining the processing method shown in FIG. 3 using camera shake as an example, and generating new comparison data from the generated restored data, and the blurred original image to be processed It is a figure for demonstrating the condition which produces | generates the data of a difference by comparing. FIG. 3 is a diagram for specifically explaining the processing method shown in FIG. 3 using camera shake as an example, and a diagram for explaining a situation in which newly generated difference data is allocated and new restored data is generated. It is. FIG. 4 is a diagram for explaining processing using the center of gravity of a change factor, which is a first processing method using the processing method shown in FIG. 3, and (A) shows a state in which attention is paid to one pixel in correct image data. FIG. 4B is a diagram illustrating a state in which the data of the pixel of interest is expanded in the diagram illustrating the data of the original image. It is a figure for demonstrating concretely the process using the gravity center of the change factor which is the 1st processing method shown in FIG. FIG. 4 is a diagram for explaining a second processing method using the processing method shown in FIG. 3, in which (A) shows data of an original image to be processed, and (B) shows data obtained by thinning out the data of (A). FIG. It is a flowchart figure of the 2nd processing method shown in FIG. FIG. 4 is a diagram for explaining a third processing method using the processing method shown in FIG. 3, (A) shows data of an original image to be processed, and (B) shows a part of the data of (A). It is a figure which shows the taken-out data. It is a flowchart figure of the 3rd processing method shown in FIG. FIG. 17 is a diagram for explaining a modification of the third processing method shown in FIGS. 17 and 18 in which the original image data is divided into four parts, and a part of the area for repeated processing is extracted from each divided area. FIG.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Image processing apparatus 2 Image pick-up part 3 Control system part 4 Processing part 5 Recording part 6 Detection part 7 Factor information preservation | save part Io Initial image data (data of arbitrary images)
Io 'comparison data G Change factor information data (degradation factor information data)
GS Reduced change factor information data Img 'Original image data (captured image)
ISmg 'Original image reduced data δ Difference data k Distribution ratio Io + n Restored data (Restored image data)
ISo + n Reduced restoration data Img Original correct image data without deterioration (original image)
ISmg Reduced original image
g (x), g ′ (x), g2 (x) transfer function (transfer function for restoring a large image)
g1 (x), g1 ′ (x) transfer function (transfer function obtained from reduced data)

Claims (1)

An imaging unit comprising an imaging element;
An image before changing from an original image generated by imaging by the imaging unit, an image that should have been originally captured, or an approximate image thereof (hereinafter referred to as an original image) is used as a pixel when the original image is captured. A processing unit that performs restoration processing by data processing using change factor information indicating dispersion of corresponding light energy, and
The processing unit
From the predetermined image data, comparison data in which light energy is dispersed according to the manner of dispersion indicated by the change factor information at the time of capturing the original image is generated, and the original image data and the comparison data are When the difference data is greater than or equal to a predetermined value, the restoration data is generated by allocating the difference data to the predetermined image data in accordance with the light energy distribution indicated by the change factor information. 1 process is executed,
The restoration data is used in place of the predetermined image data in the first process, and a process for generating new restoration data is repeatedly executed, and the difference data is reduced to a predetermined value or less or smaller than a predetermined value. The second process to be generated is executed,
The processing unit generates image data using change factor information at the time of shooting from image data different from the original image data, and image data obtained by superimposing the created image data and the original image data Is used as the predetermined image data, and the first process and the second process are executed, and the difference data obtained by the execution is the original of the restored data whose difference data is equal to or less than a predetermined value or smaller than the predetermined value. The original image data is obtained by removing the other image data from the restored data.
An image processing apparatus.

Images