JP2008228071A - Method, program and device for distortion correction, and imaging apparatus - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a method, program and device for distortion correction and imaging apparatus in which distortion residual correction can be performed with small information missing and high correction accuracy. <P>SOLUTION: A distortion correction method of the present invention includes: a recognition step (S3) of recognizing distortion of a photographic image designated by a user on the basis of attached information of the photographic image; an input step (S4) of causing the user to input the distortion to be made residual in the photographic image after distortion correction; and a correction step (S6) of calculating a correction expression of distortion correction to be applied to the photographic image on the basis of both the recognized distortion and the input distortion and applying the distortion correction to the photographic image in accordance with the correction expression. <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

  The present invention relates to a distortion correction method, a distortion correction program, a distortion correction apparatus, and an imaging apparatus that perform distortion correction on a captured image by digital processing.

  Japanese Patent Application Laid-Open No. 2004-228561 discloses a printer that allows a user to specify a correction characteristic for distortion correction while displaying a captured image on a monitor and executes a distortion correction process using the correction characteristic. According to this printer, in addition to setting the distortion generated in the photographed image due to the distortion aberration of the photographing lens to zero, it is possible to leave arbitrary distortion in the photographed image. Hereinafter, the distortion correction for reducing the distortion of the photographed image to zero is referred to as “distortion suppression correction”, and the distortion correction for allowing arbitrary distortion to remain in the photographed image is referred to as “distortion residual correction”.

The distortion suppression correction is effective when priority is given to distortion suppression of the captured image, and the distortion remaining correction is effective when considering the balance between distortion suppression of the captured image and lack of peripheral images in the captured image. . For example, in an image taken by a user while looking at the optical viewfinder, such as an image taken with a single-lens reflex camera, there may be an important subject in the periphery of the image. Since the subject may be lost when the distortion suppression correction is performed, the distortion remaining correction is effective.
JP-A-11-331543

However, it is extremely difficult for the user to specify the correction characteristics for distortion remaining correction unless the distortion of the captured image is zero. This is because the distortion remaining correction needs to reflect not only the distortion that should remain in the captured image but also the distortion that has occurred in the captured image.
If the distortion remaining correction is performed after the distortion suppression correction is performed on the captured image, it is not necessary to reflect the distortion that has occurred in the captured image in the correction characteristics of the distortion remaining correction. However, if the distortion suppression correction and the distortion remaining correction are sequentially performed on the photographed image, the number of distortion correction processes increases to twice, so the amount of information loss (neighboring image loss or interpolation error) is doubled accordingly. Become.

  SUMMARY OF THE INVENTION An object of the present invention is to provide a distortion correction method, a distortion correction program, a distortion correction apparatus, and an imaging apparatus capable of performing distortion remaining correction with small information loss and high correction accuracy.

  The distortion correction method of the present invention allows the user to input a recognition procedure for recognizing distortion of a photographed image based on incidental information of the photographed image specified by the user and distortion to be left in the photographed image after distortion correction. An input procedure, a correction procedure for calculating a distortion correction to be performed on the captured image based on both the recognized distortion and the input distortion, and a correction procedure for performing distortion correction on the captured image using the correction formula; It is characterized by including.

In the correction procedure, it is preferable that the input distortion information is added to the accompanying information of the captured image after the distortion correction.
The information added to the accompanying information is preferably information on the distortion expressed as a function of a position on the undistorted image.
Further, in the recognition procedure, when the photographed image designated by the user is an uncorrected image, based on the lens position of the photographing lens at the time of photographing the photographed image and the distortion aberration information of the photographing lens, It is desirable to recognize distortion of the captured image.

In the recognition procedure, if there is no significant accompanying information in the captured image designated by the user, it is desirable that the distortion of the captured image is regarded as zero.
The distortion correction program of the present invention causes a computer to execute any one of the distortion correction methods of the present invention.
In addition, the distortion correction apparatus of the present invention provides a recognition means for recognizing distortion of a photographed image based on incidental information of the photographed image specified by the user, and distortion to be left in the photographed image after distortion correction to the user. A correction formula for correcting distortion to be applied to the captured image based on both the input means to be input and the recognized distortion and the input distortion, and correction for performing distortion correction on the captured image using the correction formula Means.

It is preferable that the correction unit further adds the input distortion information to the accompanying information of the captured image after the distortion correction.
The information added to the accompanying information is preferably information on the distortion expressed as a function of a position on the undistorted image.
In addition, when the captured image designated by the user is an uncorrected image, the recognizing unit, based on the lens position of the photographing lens at the time of photographing the photographed image, and distortion aberration information of the photographing lens, It is desirable to recognize distortion of the captured image.

Further, it is desirable that the recognition means regards the distortion of the captured image as zero when there is no significant accompanying information in the captured image designated by the user.
In addition, an imaging apparatus according to the present invention includes an imaging apparatus that acquires a captured image and any one of the distortion correction apparatuses according to the present invention that performs distortion correction on the captured image acquired by the imaging apparatus.

  According to the present invention, a distortion correction method, a distortion correction program, a distortion correction apparatus, and an imaging apparatus capable of performing distortion residual correction with small missing information and high correction accuracy are realized.

[Embodiment]
Hereinafter, a camera system will be described as an embodiment of the present invention.
FIG. 1 is a diagram illustrating the configuration of a camera system. As shown in FIG. 1, the camera system includes a digital camera 10 and a computer 21.
The digital camera 10 is equipped with a photographing lens 11 whose lens position is variable. The lens position of the photographic lens 11 is a combination of the focal position d and the focal length f of the photographic lens 11. The subject image formed by the photographing lens 11 is converted into an image I by the image sensor of the digital camera 10 at the time of photographing. The digital camera 10 creates an image file of the image I and stores it in a memory (such as a card memory) of the digital camera 10.

When the digital camera 10 is connected to the computer 21, the digital camera 10 is under the control of the computer 21. In this state, the computer 21 transfers the image file stored in the memory of the digital camera 10 to the computer 21 and stores it in the memory (such as a hard disk) of the computer 21.
The computer 21 is preinstalled with a distortion correction program, and operates according to the distortion correction program. It is assumed that the operation of the computer 21 described below follows this distortion correction program.

The computer 21 can perform both the distortion suppression correction for making the distortion of the image zero and the distortion remaining correction for allowing the distortion specified by the user to remain in the image. At that time, the computer 21 displays necessary information on the monitor 22a, and the user operates the input device 22b while confirming the information to input the necessary information to the computer 21.
Here, as shown in FIG. 1A, the image file transferred from the digital camera 10 to the computer 21 includes an image storage area AI in which data of the image I is written, and a tag in which incidental information of the image I is written. And an area At. This additional information includes at least the lens type information IL of the photographing lens 11 and the value of the lens position (d, f) of the photographing lens 11 at the time of photographing. It is assumed that the image file transferred from the other digital camera to the computer 21 has the same information.

Therefore, the computer 21 can make the distortion occurring in each image known based on the accompanying information of each image. However, in order to make this possible, the computer 21 stores a lens information table in advance as shown in FIG. Hereinafter, the contents of the lens information table will be described in detail.
Usually, the distortion amount D given to the image by the photographing lens is expressed as a function of the position on the undistorted image. When the position on the undistorted image is expressed by an image height ratio r (= image height / maximum image height), the distortion amount D is expressed as a function of the image height ratio r as shown in Expression (1). Hereinafter, the distortion amount D (r) expressed as a function of the image height ratio r is referred to as “distortion amount distribution D (r)”.

  However, the coefficient A that defines the distortion distribution D (r) varies depending on the lens position (d, f) of the photographic lens. That is, the coefficient A is a function of the lens position (d, f), and is expressed by, for example, the following formula (2).

  The coefficient B that defines the distortion distribution D (r) also changes according to the lens position (d, f) of the taking lens. That is, the coefficient B is a function of the lens position (d, f), and is expressed by, for example, the following formula (3).

  The coefficient C that defines the distortion amount distribution D (r) also changes according to the lens position (d, f) of the photographic lens. That is, the coefficient C is a function of the lens position (d, f), and is expressed by, for example, the following formula (4).

Then, the 36 coefficients Γ ₀₀ , Γ ₀₁ , Γ ₀₂ , Γ ₁₀ , Γ ₁₁ , Γ ₁₂ , Γ ₂₀ , Γ ₂₁ , Γ ₂₂ , Γ ₃₀ , Γ in the equations (2), (3), (4) ₃₁ , Γ ₃₂ , Δ ₀₀ , Δ ₀₁ , Δ ₀₂ , Δ ₁₀ , Δ ₁₁ , Δ ₁₂ , Δ ₂₀ , Δ ₂₁ , Δ ₂₂ , Δ ₃₀ , Δ ₃₁ , Δ ₃₂ , Λ ₀₀ , Λ ₀₁ , Λ ₀₂ , The combination of values of Λ ₁₀ , Λ ₁₁ , Λ ₁₂ , Λ ₂₀ , Λ ₂₁ , Λ ₂₂ , Λ ₃₀ , Λ ₃₁ , and Λ ₃₂ is determined by the type (specification) of the taking lens.

Therefore, the lens information table shown in FIG. 1B stores the values of these 36 coefficients (Γ ₀₀ , Γ ₀₁ ,..., Λ ₃₂ ) for each type of photographing lens.
For example, when the distortion amount distribution occurring in the image I is known, the computer 21 recognizes the type of the photographing lens used for photographing the image I from the lens type information IL attached to the image I, and sets the type. The values of the corresponding coefficients (Γ ₀₀ , Γ ₀₁ ,..., Λ ₃₂ ) are read from the lens information table, and those values are substituted into the equations (1) to (4). A calculation formula D (r) = D (r, d, f) for the distribution D (r) is derived. Then, if the value of the lens position (d, f) associated with the image I is applied to the calculation formula D (r) = D (r, d, f), the distortion amount distribution D (r generated in the image I ) Becomes known.

It is assumed that the computer 21 stores each of the formulas (1) to (4) in advance.
Here, since the number of terms in equation (1) is 3, and the number of terms in equations (2), (3), and (4) is 12, the number of coefficients stored in the lens information table (for each lens type). However, if the number of terms in equation (1) is different from the number of terms in equations (2) to (4), the number may be other than 36.

In addition, when performing the above-described distortion remaining correction, the computer 21 according to the present embodiment allows the user to specify a distortion amount distribution that should remain in the image after distortion correction. This distortion amount distribution is the same as the above-described distortion amount distribution. It can be expressed as a function type (formula (1)). Therefore, in this case, the user designates the values of the three coefficients A, B, and C in the equation (1).
Hereinafter, the operation of the computer 21 relating to distortion correction will be described in detail.

FIG. 2 is an operation flowchart of the computer 21 regarding distortion correction.
First, the computer 21 presents a list of image files stored in the memory to the user and designates an original image that is to be subjected to distortion correction (step S1). In addition, in the presented image file, an image file of an uncorrected image (image I or the like) that has not been subjected to distortion correction processing once, and an already corrected image that has been subjected to distortion correction processing at least once. However, first, the case where the image I which is an uncorrected image is designated as the original image and the user tries to obtain the image I ′ as the corrected image will be described first. (See FIG. 3). Hereinafter, the image I is referred to as “original image I”, and the corrected image I ′ is referred to as “corrected image I ′”.

When the original image I is designated (step S1 YES), the computer 21 displays the original image I on the monitor 22a (step S2). As shown in FIG. 3, the original image I is distorted, and the distortion amount distribution is caused by the distortion aberration of the photographing lens 11.
On the other hand, the computer 21 is based on the lens type information IL attached to the original image I, the value of the lens position (d, f) attached to the original image I, and the lens information table stored in advance. Is assumed to be known (step S3). The method is as described above. Hereinafter, this distortion amount distribution D (r) is referred to as “current distortion amount distribution D (r)”. The current distortion amount distribution D (r) represents the distortion amount distribution of the original image I with reference to the undistorted image I _i as shown in FIG.

Thereafter, the computer 21 waits for a distortion correction instruction from the user (step S4). In this state, the user designates the values of the three coefficients A, B, and C of the distortion amount distribution D ′ (r) to be left in the corrected image I ′ to the computer 21. Hereinafter, the distortion amount distribution D ′ (r) specified by the user is referred to as “specified distortion amount distribution D ′ (r)”.
The designated distortion amount distribution D ′ (r) represents the distortion amount distribution of the corrected image I ′ with reference to the undistorted image I _i as shown in FIG. 3B. Therefore, based on the specified distortion amount distribution D ′ (r) and the above-described current distortion amount distribution D (r), the relationship between the original image I and the corrected image I ′ should be understood.

Therefore, the computer 21 based on the current distortion amount distribution D (r) and the designated distortion amount distribution D ′ (r), as shown in FIG. 3 (1), the original image I based on the corrected image I ′. A distortion amount distribution D ^new (r) is calculated, and a coordinate conversion formula from the original image I to the corrected image I ′ is created from the distortion amount distribution D ^new (r) (step S5). Details of step S5 will be described later.
Then, the computer 21 performs distortion correction processing on the original image I using the coordinate conversion formula to obtain a corrected image I ′ (step S6), and displays the corrected image I ′ on the monitor 22a (step S6). S7).

Thereafter, when an instruction to save the corrected image I ′ is input from the user (step S8), the computer 21 creates an image file of the corrected image I ′ and stores the image file in the memory (step S9).
Therefore, the distortion remaining correction for the uncorrected image (image I or the like) is reliably performed by only one distortion correction process. Therefore, this distortion remaining correction is highly accurate with few missing information.

Moreover, in step S9, as shown in FIG. 4A, the computer 21 stores information on the distribution of the distortion amount generated in the corrected image I ′ in the tag area At of the image file of the corrected image I ′, that is, the immediately preceding information. Information (values of coefficients A, B, and C) of the specified distortion amount distribution D ′ (r) specified in step S4 is written.
Therefore, in the image file of the already corrected image (image I ′, etc.), as shown in FIG. 4A, information (coefficients A, B) of the current distortion amount distribution occurring in the already corrected image (image I ′, etc.). , C values) are written as they are.

Therefore, even when a corrected image (image I ′ or the like) is designated as the original image in the subsequent step S1, the computer 21 can perform highly accurate distortion remaining correction.
Next, a case will be described in which an image I ′ that is a corrected image is designated as an original image and the user tries to obtain an image I ″ as a corrected image (see FIG. 3). The original image I ′ ”is referred to, and the corrected image I ″ is referred to as“ corrected image I ″ ”.

When the original image I ′ is designated (step S1 YES), the computer 21 displays the original image I ′ on the monitor 22a (step S2). As shown in FIG. 3, the original image I ′ is distorted, and the distortion amount distribution is caused after the previous distortion correction.
On the other hand, the computer 21 applies the values of the coefficients A, B, and C attached to the original image I ′ (see FIG. 4A) to the equation (1), thereby causing distortion occurring in the original image I ′. The quantity distribution D ′ (r) is assumed to be known (step S3). Hereinafter, this distortion amount distribution D ′ (r) is referred to as “current distortion amount distribution D ′ (r)”. As shown in FIG. 3B, the current distortion amount distribution D ′ (r) represents the distortion amount distribution of the original image I ′ with the undistorted image I _i as a reference.

Thereafter, the computer 21 waits for a distortion correction instruction from the user (step S4). In this state, the user designates to the computer 21 the values of the three coefficients A, B, and C of the distortion amount distribution D ″ (r) that should remain in the corrected image I ″. Hereinafter, the distortion amount distribution D ″ (r) designated by the user is referred to as “designated distortion amount distribution D ″ (r)”.
As shown in FIG. 3C, the designated distortion amount distribution D ″ (r) represents the distortion amount distribution of the corrected image I ″ with reference to the undistorted image I _i . Therefore, the relationship between the original image I ′ and the corrected image I ″ should be understood based on the specified distortion amount distribution D ″ (r) and the above-described current distortion amount distribution D ′ (r).

Therefore, the computer 21 based on the current distortion amount distribution D ′ (r) and the specified distortion amount distribution D ″ (r), as shown in FIG. 3B, the original image I based on the corrected image I ″. A distortion amount distribution D ′ ^new (r) of “′” is calculated, and a coordinate conversion formula from the original image I ′ to the corrected image I ″ is created based on the distortion amount distribution D ′ ^new (r) (step S5). Details of step S5 will be described later.

Then, the computer 21 performs distortion correction processing on the original image I ′ using the coordinate conversion formula to obtain a corrected image I ″ (step S6), and displays the corrected image I ″ on the monitor 22a (step S6). Step S7).
Thereafter, when an instruction to save the corrected image I ″ is input from the user (step S8), the computer 21 creates an image file of the corrected image I ″ and stores the image file in the memory (step S9).

Therefore, the distortion remaining correction for the already corrected image (image I ′ or the like) is reliably performed by only one distortion correction process. Therefore, this distortion remaining correction is highly accurate with few missing information.
Moreover, in step S9, as shown in FIG. 4B, the computer 21 stores information on the distribution of the amount of distortion generated in the corrected image I ″ in the tag area At of the image file of the corrected image I ″, that is, the immediately preceding information. Information (values of coefficients A, B, and C) of the specified distortion amount distribution D ″ (r) specified in step S4 is written.

According to the operation of the computer 21 described above, it is also possible to repeatedly perform the distortion correction with high accuracy with little information loss on the same image (however, the total image loss increases by the number of repetitions).
Further, according to the operation of the computer 21 described above, the distortion suppression correction is performed only by the user setting the specified distortion amount distribution to zero (the values of the coefficients A, B, and C are set to 0) in step S4 described above. Is also possible.

[Details of Step S5]
Hereinafter, step S5 will be described in detail with reference to FIG. Here, as a representative example, a method of creating a coordinate conversion formula (FIG. 5A) from the image I to the image I ′ will be described. Coordinate conversion formulas for other images can be similarly created.
The coordinate conversion formula (FIG. 5 (1)) from the original image I to the corrected image I ′ is the same as the coordinate conversion formula (FIG. 5 (A)) from the original image I to the undistorted image I _i . _This is a combination of the coordinate conversion formula (FIG. 5B) from _i to the corrected image I ′.

First, consider the coordinate conversion formula (FIG. 5A) from the original image I to the undistorted image I _i .
As described above, the distortion amount distribution generated in the original image I is D (r), and the image height ratio r representing the distortion amount distribution D (r) is the image height ratio r on the undistorted image I _{i. i} . Therefore, here, the distortion amount distribution D (r) is represented as D (r _i ). In this case, coordinate conversion formula from the original image I to the non-distorted image I _i is the coordinate of V = on the original image I (x, y), the coordinates on the non-distorted image _{I i V i = (x i} , If y _i ), then it is expressed by equation (5).

Next, consider the coordinate conversion formula (FIG. 5B) from the undistorted image I _i to the corrected image I ′.
As described above, the distortion amount distribution to be left in the corrected image I ′ is D ′ (r), and the image height ratio r representing the distortion amount distribution D ′ (r) is on the undistorted image I _i . The image height ratio r _i . Therefore, here, the distortion amount distribution D ′ (r) is represented as D ′ (r _i ). In this case, the coordinate conversion formula from the undistorted image I _i to the corrected image I ′ is V = (x _i , y _i ) on the undistorted image I _i and V is the coordinate on the corrected image I ′. If '= (x', y ') is set, it is expressed by equation (6).

  Therefore, it can be seen that the coordinate conversion equation (FIG. 5 (1)) from the original image I to the corrected image I ′ is the equation (7) from the equations (5) and (6).

Note that D ^new (r _i ) in equation (7) represents the distortion amount distribution of the original image I with the corrected image I ′ as a reference, expressed as an image height ratio r _i on the undistorted image I _i. It is. This distortion amount distribution D ^new (r _i ) is also approximated by the same function type (equation (1)) as other distortion amount distributions.
In order to use this equation (7) as a coordinate conversion equation from the original image I to the corrected image I ′, the distortion amount distribution D ^new (r _i ) in the equation (7) is represented on the corrected image I ′. It is necessary to replace with the distortion amount distribution D ^new (r ′) represented by the image height ratio r ′ (see equation (8)).

Here, the replacement from the distortion amount distribution D ^new (r _i ) to the distortion amount distribution D ^new (r ′) is performed as follows.
First, the distortion amount distribution D ′ (r _i ) in the equation (7) is expressed as the following equation (9) from the definition of the distortion amount.

  Therefore, Expression (10) is established.

  Moreover, Formula (11) is formed from the definition in Formula (7).

As described above, the values of the coefficients A, B, and C that define the distortion amount distribution D (r _i ) and the values of the coefficients A, B, and C that define the distortion amount distribution D ′ (r _i ) are: Each is known.
Therefore, if a numerical value is substituted into r _i in the equations (10) and (11), the value of r _i , the value of r ′ corresponding thereto, and the value of D ^new (r _i ) corresponding thereto are respectively obtained. Become known. By repeating this while changing the value of r _i in various ways, a correspondence table between the value of r ′ and the value of D ^new (r _i ) is completed.

Then, by fitting the data of this correspondence table to the definition equation (12) of the distortion amount distribution D ^new (r ′) and determining the values of the coefficients A, B, and C of the definition equation (12), the distortion amount distribution is obtained. D ^new (r ′) is obtained.

Thereby, the replacement of the distortion amount distribution D ^new (r _i ) with the distortion amount distribution D ^new (r ′) is completed.
[Others]
In the above-described embodiment, the distortion amount distribution specified by the equations (1) to (4) or the user, and the above-described distortion amount distribution D ^new (r ′) are not limited to those described above. . For example, instead of the expression (1), another expression as shown in the expression (13) or (14) may be used, and a higher order term is included in the term of D ^new (r ′). It may be.

D (r) ≡Ar ³ + Br ² (13)
D (r) ≡Ar ³ + Br ² + Cr (14)
Incidentally, it is desirable to increase the number of terms in Equation (1) as the distortion pattern generated in the image is more complicated. Further, it is desirable to increase the number of terms in the equations (2) to (4) as the variation of the distortion pattern due to the lens positions d and f becomes more complicated.

  In the above-described embodiment, it is assumed that significant accompanying information (= information necessary to make the distortion amount distribution of the image known) is written in the image file of the image specified by the user. In fact, significant accompanying information may not be written. In this case, the computer 21 may perform the above-described operation by regarding the distortion amount distribution generated in the image as zero.

In the above-described embodiment, the distortion correction program is installed in the computer 21 via, for example, a recording medium such as a CD-ROM or the Internet.
In the above-described embodiment, it is desirable that the content of the distortion correction program (specifically, the lens information table) is updated every time a new type of photographing lens is released. The update information may be released by the manufacturer of the taking lens on the Internet, and the user may download it to the computer via the Internet.

  In the embodiment described above, some or all of the operations of the computer 21 may be executed by the digital camera 10 or may be executed by another device (printer, image storage) having an image editing function. Good.

FIG. 1 is a diagram illustrating the configuration of a camera system. FIG. 2 is an operation flowchart of the computer 21. It is a figure explaining the relationship of image I, I ', I ". It is a figure explaining the structure of the image file of image I ', I ". It is a figure explaining step S5.

Explanation of symbols

10 ... digital camera, 21 ... computer, 11 ... taking lens

Claims (12)

A recognition procedure for recognizing distortion of the captured image based on additional information of the captured image specified by the user;
An input procedure for allowing the user to input distortion to be left in the captured image after distortion correction;
A correction procedure for calculating a distortion correction to be performed on the captured image based on both the recognized distortion and the input distortion, and a correction procedure for performing distortion correction on the captured image using the correction formula. A characteristic distortion correction method. The distortion correction method according to claim 1,
The correction procedure further includes:
The distortion correction method, wherein the inputted distortion information is added to accompanying information of the captured image after the distortion correction. The distortion correction method according to claim 2,
Information added to the accompanying information is:
The distortion correction method, wherein the distortion information is expressed as a function of a position on an undistorted image. In the distortion correction method according to any one of claims 1 to 3,
In the recognition procedure,
When the photographed image designated by the user is an uncorrected image, the distortion of the photographed image is recognized based on the lens position of the photographing lens at the time of photographing the photographed image and the distortion aberration information of the photographing lens. A distortion correction method characterized by: In the distortion correction method according to any one of claims 1 to 4,
In the recognition procedure,
A distortion correction method, wherein, when there is no significant incidental information in a captured image designated by the user, distortion of the captured image is regarded as zero. A distortion correction program that causes a computer to execute the distortion correction method according to any one of claims 1 to 5. Recognizing means for recognizing distortion of the photographed image based on the incidental information of the photographed image designated by the user;
Input means for allowing the user to input distortion to be left in the captured image after distortion correction;
A correction means for calculating a distortion correction to be performed on the captured image based on both the recognized distortion and the input distortion, and correcting means for correcting the distortion on the captured image using the correction expression. Characteristic distortion correction device. The distortion correction apparatus according to claim 7,
The correction means further includes
The distortion correction apparatus, wherein the input distortion information is added to accompanying information of the captured image after the distortion correction. The distortion correction apparatus according to claim 8, wherein
Information added to the accompanying information is:
The distortion correction apparatus, wherein the distortion information is expressed by a function of a position on an undistorted image. In the distortion correction apparatus according to any one of claims 7 to 9,
The recognition means is
When the photographed image designated by the user is an uncorrected image, the distortion of the photographed image is recognized based on the lens position of the photographing lens at the time of photographing the photographed image and the distortion aberration information of the photographing lens. A distortion correction device characterized by that. In the distortion correction device according to any one of claims 7 to 10,
The recognition means is
The distortion correction apparatus according to claim 1, wherein if there is no significant accompanying information in the captured image designated by the user, the distortion of the captured image is regarded as zero. An imaging device for acquiring a captured image;
An image pickup apparatus comprising: the distortion correction apparatus according to any one of claims 7 to 11 which performs distortion correction on a captured image acquired by the image pickup apparatus.

Images