CN112991233B - Image correction method and device - Google Patents
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Abstract
The application discloses a method and a device for correcting an image, which are used for correcting errors caused by uneven visual angle of an LCD display screen in the image, wherein the method comprises the following steps: simulating all sample images in a target sample database through a preset elliptic paraboloid function to obtain correction parameters and a first correction image corresponding to the sample images; acquiring an optimal correction coefficient corresponding to each gray scale and correction parameter under the sample image group; adding the optimal correction coefficient corresponding to each gray scale into a preset correction model according to the sample image group to generate a target correction model; acquiring a correction image; determining a first gray scale corresponding to the corrected image; determining a target optimal correction coefficient corresponding to the first gray scale in a target correction model through the first gray scale; and correcting the corrected image by the target optimal correction coefficient according to a preset correction function to obtain a target corrected image.
Description
Technical Field
The present application relates to the field of image processing, and in particular, to a method and an apparatus for image correction.
Background
For intelligent devices with Liquid Crystal Displays (LCDs), such as mobile phones and ipads, when the LCD displays are viewed in a lighted state at different viewing angles, the observed Optical brightness and chromaticity are not uniform, and the Display itself almost provides Optical parameters at 0 viewing angle, if the LCD displays are photographed through an Optical system, due to the problem of the viewing angle non-uniformity, the photographed images are subject to errors, if the problem also exists in the photographs of the LCD displays by an Automatic Optical Inspection (AOI) system, a large systematic error is brought to Optical Mura Inspection and Mura compensation, and further the Inspection effect or the repair effect is not good.
In order to solve the problem of image errors caused by uneven visual angles of an LCD (liquid crystal display) of an optical system in shooting, most manufacturers of the optical system manually try correction coefficients with different intensities according to specific shooting conditions, and confirm compensation or check effects in a human-eye observation mode to provide a correction model.
Disclosure of Invention
The application provides an image correction method and device, which are used for correcting the phenomenon of nonuniformity in a shot image caused by different observation visual angles of a target display screen, so that the modeling accuracy is improved.
The application provides a method for image correction in a first aspect, comprising:
simulating all sample images in a target sample database by a preset elliptic paraboloid function to obtain correction parameters and a first correction image corresponding to the sample images, wherein the correction parameters comprise a first correction parameter and a second correction parameter, the target sample database comprises a plurality of sample image groups with different pictures, and the sample image groups comprise sample images with different gray scales;
acquiring optimal correction coefficients corresponding to the gray scales and the correction parameters under the sample image group, wherein the optimal correction coefficients comprise a first optimal correction coefficient and a second optimal correction coefficient;
adding the optimal correction coefficient corresponding to each gray scale into a preset correction model according to the sample image group to generate a target correction model;
acquiring a correction image, wherein the correction image is obtained by shooting a first target LCD display screen by a first target optical system;
determining a first gray scale corresponding to the corrected image;
determining a target optimal correction coefficient corresponding to the first gray scale in a target correction model through the first gray scale, wherein the target optimal correction coefficient comprises a first target optimal correction coefficient and a second target optimal correction coefficient;
and correcting the corrected image by the target optimal correction coefficient according to a preset correction function to obtain a target corrected image.
The simulating all sample images in the target sample database through the preset elliptic paraboloid function to obtain the correction parameters and the first correction image corresponding to the sample images comprises the following steps:
obtaining an untrained first sample graph group from the target sample database;
obtaining an untrained sample image from the first sample image group and determining a second gray scale of the sample image;
acquiring first residual data and second residual data of the sample image, wherein the first residual data is normalized residual data of the sample image obtained in the horizontal direction of the optical axis center, and the second residual data is normalized residual data of the sample image obtained in the vertical direction of the optical axis center;
obtaining a first correction parameter and a second correction parameter corresponding to the second gray scale through a preset elliptic paraboloid function according to the first residual data and the second residual data and generating a first correction image corresponding to the second gray scale;
judging whether an image which is not traversed exists in the first sample map group;
if yes, executing the step of obtaining an untrained sample image from the first sample image group and determining a second gray scale of the sample image;
if not, obtaining the corresponding optimal correction coefficient between each gray scale and the correction parameter under the sample image group.
Optionally, the obtaining an optimal correction coefficient corresponding to each gray scale and a correction parameter under the sample image group includes:
acquiring a first optimal correction coefficient between each gray scale and a corresponding first correction parameter, wherein the first optimal correction coefficient is a polynomial coefficient obtained by fitting each gray scale and the corresponding first correction parameter through a least square polynomial fitting method and storing the fitting;
and acquiring a second optimal correction coefficient between each gray scale and the corresponding second correction parameter, wherein the second optimal correction coefficient is a polynomial coefficient obtained by fitting each gray scale and the corresponding second correction parameter through a least square polynomial fitting method and storing the fitting.
Optionally, after obtaining the second optimal correction coefficients between each gray scale and the corresponding second correction parameter, the method further includes:
judging whether an unretraversed sample group exists in the target sample database;
if yes, executing the step of acquiring the first sample graph group from the target sample database;
and if not, executing the step of adding the optimal correction coefficient corresponding to each gray scale into a preset correction model according to the sample image group to generate a target correction model.
Optionally, the obtaining the first residual data and the second residual data of the sample image includes:
determining an optical axis position of the sample image;
acquiring first image data and second image data according to the optical axis position, wherein the first image data is image data in the horizontal direction with the optical axis position as the center, and the second image data is image data in the vertical direction with the optical axis position as the center;
determining the first residual data from the first image data;
determining the second residual data from the second image data.
Optionally, before simulating all sample images in the target sample database by using a preset elliptic paraboloid function to obtain the correction parameters and the first correction image corresponding to the sample images, the method further includes:
and creating a target sample database, wherein sample images in the target sample database are all obtained by shooting a second LCD display screen by a second target optical system in a specific scene.
Optionally, the creating a target sample database includes:
calibrating camera parameters of the second target optical system by a preset camera calibration method;
acquiring camera parameters, wherein the camera parameters comprise coordinates of an optical axis position of a camera on a shot image, a first focal length of the optical axis in an x direction and a second focal length of the optical axis in a y direction;
shooting the second target LCD display screen at different gray scale values corresponding to a white picture, a red picture, a green picture and a blue picture respectively through the second target optical system to obtain four groups of sample images, wherein the shooting is carried out in a specific environment, and the exposure time of the sample images is a preset value;
and grouping the sample images according to pictures, marking the gray scale of each sample image, and storing the sample images into the target sample database.
A second aspect of the present application provides an apparatus for image correction, comprising:
the system comprises a first execution unit, a second execution unit and a third execution unit, wherein the first execution unit is used for simulating all sample images in a target sample database through a preset elliptic paraboloid function to obtain correction parameters and a first correction image corresponding to the sample images, the correction parameters comprise first correction parameters and second correction parameters, the target database comprises a plurality of sample image groups with different pictures, and the sample image groups comprise sample images with different gray scales;
the first obtaining unit is used for obtaining optimal correction coefficients corresponding to the gray scales and the correction parameters under the sample image group, and the optimal correction coefficients comprise a first optimal correction coefficient and a second optimal correction coefficient;
the second execution unit is used for generating a target correction model after adding the optimal correction coefficient corresponding to each gray scale into a preset correction model according to the sample image group;
a second acquisition unit for acquiring a corrected image, which is obtained by photographing the first target LCD display screen by the first target optical system;
the first determining unit is used for determining a first gray scale corresponding to the corrected image;
the second determining unit is used for determining a target optimal correction coefficient corresponding to the first gray scale in a target correction model through the first gray scale, wherein the target optimal correction coefficient comprises a first target optimal correction coefficient and a second target optimal correction coefficient;
and the correcting unit is used for correcting the corrected image by the target optimal correction coefficient according to a preset correction function to obtain a target corrected image.
The first execution unit includes:
the first acquisition module is used for acquiring an untrained first sample group from the target sample database;
the second acquisition module is used for acquiring an untrained sample image from the first sample image group and determining a second gray scale of the sample image;
a third obtaining module, configured to obtain first residual data and second residual data of the sample image, where the first residual data is normalized residual data obtained by the sample image in a horizontal direction of an optical axis center, and the second residual data is normalized residual data obtained by the sample image in a vertical direction of the optical axis center;
the first execution module is used for obtaining a first correction parameter and a second correction parameter corresponding to the second gray scale through a preset elliptic paraboloid function according to the first residual data and the second residual data and generating a first correction image corresponding to the second gray scale;
and the first judging module is used for judging whether the first sample graph group has an image which is not traversed.
Optionally, the first obtaining unit includes:
the first fitting module is used for acquiring a first optimal correction coefficient between each gray scale and the corresponding first correction parameter, wherein the first optimal correction coefficient is a polynomial coefficient obtained by fitting each gray scale and the corresponding first correction parameter through a least square polynomial fitting method and storing the fitting;
and the second fitting module is used for acquiring a second optimal correction coefficient between each gray scale and the corresponding second correction parameter, wherein the second optimal correction coefficient is a polynomial coefficient obtained by fitting each gray scale and the corresponding second correction parameter through a least square polynomial fitting method and storing the fitting.
Optionally, the apparatus further comprises:
and the first judging unit is used for judging whether an unretraversed sample group exists in the target sample database.
Optionally, the third obtaining module includes:
the first determining submodule is used for determining the optical axis position of the sample image;
the first obtaining submodule is used for obtaining first image data and second image data according to the optical axis position, the first image data is image data which takes the optical axis position as the center and is in the horizontal direction, and the second image data is image data which takes the optical axis position as the center and is in the vertical direction;
a first determining sub-module for determining the first residual data from the first image data;
a second determining sub-module for determining the second residual data from the second image data.
Optionally, the apparatus further comprises:
and the creating unit is used for creating a target sample database, and sample images in the target sample database are all obtained by shooting a second LCD display screen by a second target optical system in a specific scene.
Optionally, the creating unit includes:
the calibration module is used for calibrating the camera parameters of the second target optical system by a preset camera calibration method;
the fourth acquisition module is used for acquiring camera parameters, wherein the camera parameters comprise coordinates of the optical axis position of the camera on a shot image, a first focal length of the optical axis in the x direction and a second focal length of the optical axis in the y direction;
the shooting module is used for shooting the second target LCD display screen at different corresponding gray scale values of a white picture, a red picture, a green picture and a blue picture through the second target optical system to obtain four groups of sample images, wherein the shooting is carried out in a specific environment, and the exposure time of the sample images is a preset value;
and the storage module is used for grouping the sample images according to pictures and marking the gray scale of each sample image and then storing the sample images into the target sample database.
The third aspect of the present application provides an image correction apparatus comprising:
the device comprises a processor, a memory, an input and output unit and a bus;
the processor is connected with the memory, the input and output unit and the bus;
the processor specifically performs the following operations:
simulating all sample images in a target sample database by a preset elliptic paraboloid function to obtain correction parameters and a first correction image corresponding to the sample images, wherein the correction parameters comprise a first correction parameter and a second correction parameter, the target sample database comprises a plurality of sample image groups with different pictures, and the sample image groups comprise sample images with different gray scales;
acquiring optimal correction coefficients corresponding to the gray scales and the correction parameters under the sample image group, wherein the optimal correction coefficients comprise a first optimal correction coefficient and a second optimal correction coefficient;
adding the optimal correction coefficient corresponding to each gray scale into a preset correction model according to the sample image group to generate a target correction model;
acquiring a correction image, wherein the correction image is obtained by shooting a first target LCD display screen by a first target optical system;
determining a first gray scale corresponding to the corrected image;
determining a target optimal correction coefficient corresponding to the first gray scale in a target correction model through the first gray scale, wherein the target optimal correction coefficient comprises a first target optimal correction coefficient and a second target optimal correction coefficient;
and correcting the corrected image by the target optimal correction coefficient according to a preset correction function to obtain a target corrected image.
A fourth aspect of embodiments of the present application provides a computer-readable storage medium having a program stored thereon, the program, when executed on a computer, performing the aforementioned method of image correction.
According to the technical scheme, the method and the device have the advantages that the visual angle unevenness phenomenon existing in the sample images under different gray scales of different pictures is simulated through the elliptic paraboloid correction model, the correction parameters corresponding to the gray scales are determined, the correction parameters and the gray scales are fitted to obtain the optimal correction coefficient, the finally obtained target model comprises the optimal correction coefficients corresponding to the different gray scales of different pictures, the optimal correction coefficient corresponding to the gray scale of each pixel can be rapidly determined through the target correction model when the corrected image is corrected, the target corrected image can be obtained through correction according to the optimal correction coefficient, the correction speed is high, and the errors existing in the target corrected image can be effectively reduced.
Drawings
FIG. 1 is a flow chart illustrating an embodiment of a method for image correction according to the present application;
FIGS. 2a and 2b are schematic flow charts of another embodiment of the image correction method of the present application;
FIG. 3 is a schematic structural diagram of an embodiment of an apparatus for image correction according to the present application;
fig. 4 is a schematic structural diagram of another embodiment of the image correction apparatus according to the present application.
Detailed Description
The embodiment of the application provides an image correction method and device, which are used for correcting errors caused by uneven visual angles of an LCD display screen in an image.
The technical solutions in the present application will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The method of the present application may be applied to a server, a terminal, or other devices with logic processing capability, and the present application is not limited thereto. For convenience of description, the following description will be given taking the execution body as an example.
Referring to fig. 1, an embodiment of an image correction method in the present application includes:
101. the method comprises the steps that a terminal simulates all sample images in a target sample database through a preset elliptic paraboloid function to obtain correction parameters and a first correction image corresponding to the sample images, wherein the correction parameters comprise a first correction parameter and a second correction parameter, the target sample database comprises a plurality of sample image groups with different pictures, and the sample image groups comprise sample images with different gray scales;
each pixel on the LCD screen is combined by red, green and blue (RGB) with different brightness levels to finally form different color points, the color change of each point on the screen is brought by the gray scale change of three RGB sub-pixels forming the point, in order to improve the accuracy and the effectiveness of a training model, a target database comprises sample images under different gray scales of a white picture, a red picture, a green picture and a blue picture group, and the elliptic paraboloid is used in the embodiment of the invention
When the sample image is simulated, the parameters a and b are determined first, and then a first correction image is generated.
102. The terminal obtains the optimal correction coefficient corresponding to each gray scale and the correction parameter under the sample image group, wherein the optimal correction coefficient comprises a first optimal correction coefficient and a second optimal correction coefficient;
after the correction parameters of all sample images are acquired, in order to further determine the corresponding relationship between the gray scales and the correction parameters, in the embodiment of the present application, the relationship between the correction parameters and the gray scale values in the same sample image group is determined by a least square polynomial fitting method, and a polynomial coefficient obtained by fitting is stored, where the polynomial coefficient is an optimal correction coefficient, and since the correction parameters include a first correction parameter and a second correction parameter, the corresponding optimal correction coefficient includes a first optimal correction coefficient and a second optimal correction coefficient.
103. The terminal adds the optimal correction coefficient corresponding to each gray scale to a preset correction model according to the sample image group to generate a target correction model;
the preset correction model does not contain any data before the optimal correction coefficient is not added, and the preset model is determined to be the target correction model after the optimal correction coefficients obtained in all the sample image groups are marked and added into the preset model.
104. The terminal acquires a correction image, and the correction image is obtained by shooting a first target LCD display screen by a first target optical system;
because the optical brightness and the chromaticity observed at different visual angles of the LCD display screen are uneven in the lighting state, the first target optical system can cause errors in the shot images when the LCD display screen is shot, if the Mura is checked and compensated under the condition, a large error exists, and in order to reduce the influence caused by uneven shooting visual angles in the picture shooting process, the terminal firstly obtains the pictures shot by the optical system and then carries out corresponding correction processing on the pictures.
105. The terminal determines a first gray scale corresponding to the corrected image;
since the image is composed of different pixel arrangements, in order to obtain a target correction image with high accuracy, the terminal needs to determine a first gray scale corresponding to the correction image.
106. The terminal determines a target optimal correction coefficient corresponding to the first gray scale in a target correction model through the first gray scale, wherein the target optimal correction coefficient comprises a first target optimal correction coefficient and a second target optimal correction coefficient;
the target correction model comprises optimal correction coefficients corresponding to different gray scales of different pictures, so that the corresponding optimal correction coefficients can be quickly determined in the target correction model according to the first gray scale, and the optimal correction coefficients are referred to as target optimal correction coefficients.
107. And the terminal corrects the corrected image by the target optimal correction coefficient according to a preset correction function to obtain a target corrected image.
The preset correction model is a preset polynomial calculation function, and after the terminal determines a target correction coefficient, the target correction coefficient is substituted into the preset correction model to be corrected point by point to obtain a target correction image.
The embodiment of the application provides a generation process of a target correction model and a process of correcting a corrected image through the target correction model, the target correction model can quickly determine the optimal correction coefficient of a target, the correction coefficient does not need to be set manually, the correction speed is high, and errors in the target corrected image can be effectively reduced.
Referring to fig. 2a and fig. 2b, another embodiment of the image correction method in the present application includes:
201. the terminal calibrates the camera parameters of the second target optical system by a preset camera calibration method;
in the embodiment of the application, the terminal calibrates the internal and external parameters of the target optical system by using a Zhangyingyou chessboard format calibration method, and may also use other image calibration methods, which are not limited herein.
202. The method comprises the steps that a terminal obtains camera parameters, wherein the camera parameters comprise coordinates of the position of an optical axis of a camera on a shot image, a first focal length of the optical axis in the x direction and a second focal length of the optical axis in the y direction;
the terminal acquires coordinates (c) of the optical axis position of the camera in the objective optical system on the imagex,cy) And a first focal length f in the X-axis (horizontal direction) and Y-axis (vertical direction)xAnd a second focal length fy。
203. The terminal shoots the second target LCD display screen at different corresponding gray scale values of a white picture, a red picture, a green picture and a blue picture through the second target optical system to obtain four groups of sample images, wherein the shooting is carried out in a specific environment, and the exposure time of the sample images is a preset value;
in order to improve the accuracy of the calibration model, in the embodiment of the present application, the display gray scales of a group of target display screens are uniformly set, for example, the display gray scale g = [0,5,10, 15.,. 255], a second target display screen is photographed by using a second target optical system after debugging under a white picture, a red picture, a green picture and a blue picture, respectively, to obtain a group of photographed images under each picture, and simultaneously, the exposure time setting used by a camera in the photographing process is recorded, with the unit being ms, and finally, the exposure times of all the photographed pictures are aligned to a preset value, where a specific numerical value is not limited here, it should be noted that in the process, the spatial position, the focal length, the aperture, and the like of the target optical system need to be kept the same as in the system working state, and simultaneously, the position of the target display screen remains unchanged and the optical uniformity of the first target LCD display screen is greater than the preset value, the details are not limited herein.
204. The terminal groups the sample images according to pictures and marks the gray scale of each sample image, and then stores the sample images into the target sample database;
in the embodiment of the application, the terminal classifies the shot images according to the pictures and marks gray scales, stores the shot images into the preset storage position, can store the shot images into local data and upload the shot images to the cloud database, and is not limited in the specific position.
205. The terminal acquires an untrained first sample group from the target sample database;
the target sample database comprises four groups of images of a white picture, a red picture, a green picture and a blue picture, each group of images comprises images with different gray scales, the terminal processes the images in sequence according to the groups, and the processing sequence is not limited here.
206. The terminal acquires an untrained sample image from the first sample image group and determines a second gray scale of the sample image;
since each sample image has a different gray scale, the second gray scale of the image needs to be determined by the mark when the sample image is acquired.
207. The terminal determines the position of an optical axis of the sample image;
the optical axis is the center line of the light beam, the light beam rotates around the optical axis without change of optical characteristics, and the terminal determines the coordinates (c) of the optical axis position on the image according to step 202x,cy）。
208. The terminal acquires first image data and second image data according to the optical axis position, wherein the first image data is image data in the horizontal direction with the optical axis position as the center, and the second image data is image data in the vertical direction with the optical axis position as the center;
the position of the terminal on the optical axis (c)x,cy) Taking a line of image data in the horizontal direction as a center to obtain first image data, denoted as V (c)yI) position of the terminal with the optical axis (c)x,cy) Taking a column of image data in the vertical direction as the center to obtain a second image data, and recording as V (j, c)x）。
209. The terminal determines the first residual data according to the first image data;
performing a predetermined operation on the first image data to obtain normalized first residual data D (c)yI), the specific calculation is as in formula (1), defined as follows:
Vg is the image gray level mean value of the part of the display screen with the central visual angle of the optical axis being less than 1 degree.
210. The terminal determines the second residual data according to the second image data;
performing a predetermined operation on the second image data to obtain normalized second residual data D (j, c)x) The specific calculation is as formula (2), and the following is defined:
211. The terminal obtains a first correction parameter and a second correction parameter corresponding to the second gray scale through a preset elliptic paraboloid function according to the first residual data and the second residual data and generates a first correction image corresponding to the second gray scale;
in the calculation process of executing the stepDetermining an elliptic paraboloid
The parameter variables x and y are specifically calculated in formula (3) and formula (4), and are defined as follows:
Since there may be a large number of 0 values in the central part of D (cy, i) and D (j, cx), z = k is used first to reduce the error of the discretization calculation of the systemxx2And z = kyy2For image data D (c) in the horizontal direction and the vertical direction, respectivelyyX) and D (y, c)x) Fitting to obtain fitting coefficients in the horizontal direction and the vertical direction, and a horizontal fitting coefficient kxThe specific calculation is as in formula (5), and the vertical fitting coefficient kyThe specific calculation is as in formula (6), and is defined as follows:
Wherein, Width is the Width of the calibration screen in the image, Height is the Height of the calibration screen in the image, and then the terminal is used for fitting the coefficient k according to the levelxAnd vertical fitting coefficient kyDetermining correction parameters a (first correction parameter) and b (second correction parameter) in the elliptic paraboloid function at the current display gray level, and specifically calculating as formula (7) and formula (8), which are defined as follows:
The terminal simulates the current gray-scale image according to the first correction parameter a and the second correction parameter b and generates a first correction image, which is specifically defined as formula (9) below;
212. The terminal judges whether an image which is not traversed exists in the first sample map group, if so, step 206 is executed, otherwise, step 213 is executed;
in the embodiment of the application, the terminal calculates and simulates all sample images in the current frame group, then calculates and simulates all images in the other frame group, and if the first frame group still has an image which is not traversed, the terminal returns to execute step 206 to obtain an untrained sample image for processing.
213. The method comprises the steps that a terminal obtains a first optimal correction coefficient between each gray scale and a corresponding first correction parameter, wherein the first optimal correction coefficient is a polynomial coefficient obtained by fitting each gray scale and the corresponding first correction parameter through a least square polynomial fitting method and storing the fitting;
when the terminal finishes acquiring the correction parameters of all the images in the first picture group, using a least square polynomial fitting method to carry out correction on each gray scale [ g ] under the current picture1,g2,...,gn]And a first correction parameter [ a1,a2,...,an]Fitting to obtain an n-order polynomial function expression relation between the gray scale g and the first correction parameter a, and storing n +1 polynomial coefficients obtained by fitting as a first optimal correction coefficient a corresponding to g under the current picturew：
Since the fitting method is a known calculation method, details are not described here.
214. The terminal obtains a second optimal correction coefficient between each gray scale and a corresponding second correction parameter, wherein the second optimal correction coefficient is a polynomial coefficient obtained by fitting each gray scale and the corresponding second correction parameter through a least square polynomial fitting method and storing the fitting;
using least square polynomial fitting method to each gray scale [ g ] under current picture1,g2,...,gn]And a second correction parameter [ b1,b2,...,bn]Fitting to obtain an n-order polynomial function expression relation between the gray scale g and the second correction parameter b, and storing n +1 polynomial coefficients obtained by fitting as a second optimal correction coefficient b corresponding to g under the current picturew：
215. The terminal judges whether an unretraversed sample graph group exists in the target sample database, if so, the step 205 is executed, and if not, the step 216 is executed;
after determining the first optimal correction coefficient and the second optimal correction coefficient in the current picture, the terminal processes the pictures of the next picture group until all sample picture groups are processed, and then executes step 216.
216. The terminal adds the optimal correction coefficient corresponding to each gray scale to a preset correction model according to the sample image group to generate a target correction model;
because a first optimal correction coefficient a is corresponding to one framewAnd a second optimum correction coefficient bw：
Therefore, the white picture, the red picture, the green picture and the blue picture contained in the target correction model obtain 8 x (n +1) viewing angle non-uniformity correction model parameters after obtaining the optimal correction coefficient, and when the target correction model is used, the target correction parameters can be obtained only by substituting the gray scale into the corresponding polynomial function for calculation.
217. The terminal acquires a correction image, and the correction image is obtained by shooting a first target LCD display screen by a first target optical system;
218. the terminal determines a first gray scale corresponding to the corrected image;
in the embodiment of the present application, steps 217 to 218 are similar to steps 104 to 105, and are not described herein again.
219. The terminal determines a target optimal correction coefficient corresponding to the first gray scale in a target correction model through the first gray scale, wherein the target optimal correction coefficient comprises a first target optimal correction coefficient and a second target optimal correction coefficient;
and determining a polynomial function corresponding to the first gray scale g through the target correction model, and substituting the gray scale g into the corresponding polynomial function to calculate target correction parameters a and b.
220. And the terminal corrects the corrected image by the target optimal correction coefficient according to a preset correction function to obtain a target corrected image.
Calculating the gray scale of the pixel to be corrected by the terminal and the corresponding optimal correction coefficient in the target correction model to obtain a target correction coefficient VgSpecific calculations are as in equation (10), defined as follows:
Wherein, Vg(i, j) is the image grey value of the pixel at coordinate position (i, j).
In the embodiment of the application, through designing the elliptic paraboloid correction model, the correction speed and the correction effect are greatly improved, the terminal acquires pictures of different display gray scales of the same picture, the phenomenon of uneven shooting visual angles under each gray scale is simulated through a corresponding calculation process to obtain correction parameters, in order to further improve the correction effect, the correction parameters are optimized to obtain optimal correction coefficients, the target correction model comprises the optimal correction coefficients of different gray scales, when the uneven shooting visual angles need to be corrected, the target correction coefficients are quickly determined from the target correction model through the gray scales, a target correction image can be obtained by correcting the image according to the target correction coefficients, human intervention factors are not involved in the correction process, and the stability and the accuracy of the uneven correction visual angles are greatly improved.
The method for image correction in the embodiment of the present application is described above, and the apparatus for image correction in the embodiment of the present application is described below:
referring to fig. 3, an embodiment of an image correction apparatus in the present application includes:
the first execution unit 301 is configured to simulate all sample images in a target sample database by using a preset elliptic paraboloid function to obtain correction parameters and a first correction image corresponding to the sample images, where the correction parameters include a first correction parameter and a second correction parameter, the target sample database includes a plurality of sample image groups with different pictures, and the sample image groups include sample images with different gray scales;
a first obtaining unit 302, configured to obtain optimal correction coefficients corresponding to respective gray scales and correction parameters under the sample image group, where the optimal correction coefficients include a first optimal correction coefficient and a second optimal correction coefficient;
a second executing unit 303, configured to generate a target correction model after adding the optimal correction coefficient corresponding to each gray scale to a preset correction model according to the sample image group;
a second acquiring unit 304 for acquiring a corrected image, which is captured by the first target optical system on the first target LCD display screen;
a first determining unit 305, configured to determine a first gray scale corresponding to the corrected image;
a second determining unit 306, configured to determine, in a target correction model, a target optimal correction coefficient corresponding to the first gray scale through the first gray scale, where the target optimal correction coefficient includes a first target optimal correction coefficient and a second target optimal correction coefficient;
a correcting unit 307, configured to correct the corrected image according to a preset correction function by using the target optimal correction coefficient to obtain a target corrected image;
a first determining unit 308, configured to determine whether an unexplored sample group exists in the target sample database;
the creating unit 309 is configured to create a target sample database, where sample images in the target sample database are all captured by the second target optical system on the second LCD display screen in a specific scene.
Specifically, the first execution unit 301 in the embodiment of the present application includes:
a first obtaining module 3011, configured to obtain an untrained first sample group from the target sample database;
a second obtaining module 3012, configured to obtain an untrained sample image from the first sample group and determine a second gray scale of the sample image;
a third obtaining module 3013, configured to obtain first residual data and second residual data of the sample image, where the first residual data is normalized residual data obtained by the sample image in a horizontal direction of an optical axis center, and the second residual data is normalized residual data obtained by the sample image in a vertical direction of the optical axis center;
a first executing module 3014, configured to obtain, according to the first residual data and the second residual data, a first correction parameter and a second correction parameter corresponding to the second gray scale through a preset elliptic paraboloid function, and generate a first corrected image corresponding to the second gray scale;
a first determining module 3015, configured to determine whether there is an unretraversed image in the first sample group.
Specifically, in this embodiment of the application, the third obtaining module 3013 includes:
a first determination sub-module 30131 for determining an optical axis position of the sample image;
a first obtaining sub-module 30132, configured to obtain, according to the optical axis position, first image data and second image data, where the first image data is image data in a horizontal direction with the optical axis position as a center, and the second image data is image data in a vertical direction with the optical axis position as a center;
a first determining sub-module 30133, configured to determine the first residual data according to the first image data;
a second determining sub-module 30134, configured to determine the second residual data according to the second image data.
Specifically, the first obtaining unit 302 in the embodiment of the present application includes:
a first fitting module 3021, configured to obtain a first optimal correction coefficient between each gray scale and a corresponding first correction parameter, where the first optimal correction coefficient is a polynomial coefficient obtained by fitting each gray scale and the corresponding first correction parameter by a least square polynomial fitting method and saving the fitting coefficient;
a second fitting module 3022, configured to obtain a second optimal correction coefficient between each gray scale and a corresponding second correction parameter, where the second optimal correction coefficient is a polynomial coefficient obtained by fitting each gray scale and the corresponding second correction parameter by a least square polynomial fitting method and saving the fitting polynomial coefficient.
Specifically, the creating unit 309 in the embodiment of the present application includes:
the calibration module 3091 is configured to perform camera parameter calibration on the second target optical system by using a preset camera calibration method;
a fourth obtaining module 3092, configured to obtain camera parameters, where the camera parameters include coordinates of an optical axis position of the camera on a captured image, and a first focal length of the optical axis in an x direction and a second focal length of the optical axis in a y direction;
the shooting module 3093 is used for shooting the second target LCD display screen at different gray scale values corresponding to a white picture, a red picture, a green picture and a blue picture respectively through the second target optical system to obtain four groups of sample images, wherein the shooting is carried out in a specific environment, and the exposure time of the sample images is a preset value;
the storage module 3094 is configured to group the sample images according to pictures and mark the gray scale of each sample image, and then store the sample images in the target sample database.
Referring to fig. 4, another embodiment of the image correction apparatus of the present application includes:
a processor 401, a memory 402, an input-output unit 403, a bus 404;
the processor 401 is connected to the memory 402, the input/output unit 403 and the bus 404;
the processor 401 specifically executes the following operations:
simulating all sample images in a target sample database by a preset elliptic paraboloid function to obtain correction parameters and a first correction image corresponding to the sample images, wherein the correction parameters comprise a first correction parameter and a second correction parameter, the target sample database comprises a plurality of sample image groups with different pictures, and the sample image groups comprise sample images with different gray scales;
acquiring optimal correction coefficients corresponding to the gray scales and the correction parameters under the sample image group, wherein the optimal correction coefficients comprise a first optimal correction coefficient and a second optimal correction coefficient;
adding the optimal correction coefficient corresponding to each gray scale into a preset correction model according to the sample image group to generate a target correction model;
acquiring a correction image, wherein the correction image is obtained by shooting a first target LCD display screen by a first target optical system;
determining a first gray scale corresponding to the corrected image;
determining a target optimal correction coefficient corresponding to the first gray scale in a target correction model through the first gray scale, wherein the target optimal correction coefficient comprises a first target optimal correction coefficient and a second target optimal correction coefficient;
and correcting the corrected image by the target optimal correction coefficient according to a preset correction function to obtain a target corrected image.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and the like.
Claims (8)
1. A method of image correction, comprising:
simulating all sample images in a target sample database by a preset elliptic paraboloid function to obtain correction parameters and a first correction image corresponding to the sample images, wherein the correction parameters comprise a first correction parameter and a second correction parameter, the target sample database comprises a plurality of sample image groups with different pictures, and the sample image groups comprise sample images with different gray scales;
acquiring optimal correction coefficients corresponding to the gray scales and the correction parameters under the sample image group, wherein the optimal correction coefficients comprise a first optimal correction coefficient and a second optimal correction coefficient;
adding the optimal correction coefficient corresponding to each gray scale into a preset correction model according to the sample image group to generate a target correction model;
acquiring a correction image, wherein the correction image is obtained by shooting a first target LCD display screen by a first target optical system;
determining a first gray scale corresponding to the corrected image;
determining a target optimal correction coefficient corresponding to the first gray scale in a target correction model through the first gray scale, wherein the target optimal correction coefficient comprises a first target optimal correction coefficient and a second target optimal correction coefficient;
correcting the corrected image by the target optimal correction coefficient according to a preset correction function to obtain a target corrected image;
the simulating all sample images in the target sample database through the preset elliptic paraboloid function to obtain the correction parameters and the first correction image corresponding to the sample images comprises the following steps:
obtaining an untrained first sample graph group from the target sample database;
obtaining an untrained sample image from the first sample image group and determining a second gray scale of the sample image;
acquiring first residual data and second residual data of the sample image, wherein the first residual data is normalized residual data of the sample image obtained in the horizontal direction of the optical axis center, and the second residual data is normalized residual data of the sample image obtained in the vertical direction of the optical axis center;
obtaining a first correction parameter and a second correction parameter corresponding to the second gray scale through a preset elliptic paraboloid function according to the first residual data and the second residual data and generating a first correction image corresponding to the second gray scale;
judging whether an image which is not traversed exists in the first sample map group;
if yes, executing the step of obtaining an untrained sample image from the first sample image group and determining a second gray scale of the sample image;
if not, obtaining the corresponding optimal correction coefficient between each gray scale and the correction parameter under the sample image group.
2. The method of claim 1, wherein said obtaining optimal correction coefficients corresponding to respective gray levels and correction parameters for the sample group of images comprises:
acquiring a first optimal correction coefficient between each gray scale and a corresponding first correction parameter, wherein the first optimal correction coefficient is a polynomial coefficient obtained by fitting each gray scale and the corresponding first correction parameter through a least square polynomial fitting method and storing the fitting;
and acquiring a second optimal correction coefficient between each gray scale and the corresponding second correction parameter, wherein the second optimal correction coefficient is a polynomial coefficient obtained by fitting each gray scale and the corresponding second correction parameter through a least square polynomial fitting method and storing the fitting.
3. The method according to claim 2, wherein after the obtaining of the second optimal correction coefficient between each gray scale and the corresponding second correction parameter, the method further comprises:
judging whether an unretraversed sample group exists in the target sample database;
if yes, executing the step of acquiring the first sample graph group from the target sample database;
and if not, executing the step of adding the optimal correction coefficient corresponding to each gray scale into a preset correction model according to the sample image group to generate a target correction model.
4. The method of any of claims 1 to 3, wherein said obtaining first and second residual data for the sample image comprises:
determining an optical axis position of the sample image;
acquiring first image data and second image data according to the optical axis position, wherein the first image data is image data in the horizontal direction with the optical axis position as the center, and the second image data is image data in the vertical direction with the optical axis position as the center;
determining the first residual data from the first image data;
determining the second residual data from the second image data.
5. The method according to any one of claims 1 to 3, wherein before simulating all sample images in a target sample database by using a preset elliptic paraboloid function to obtain the corresponding correction parameters and the first correction image of the sample images, the method further comprises:
and creating a target sample database, wherein sample images in the target sample database are all obtained by shooting a second LCD display screen by a second target optical system in a specific scene.
6. The method of claim 5, wherein creating a target sample database comprises:
calibrating camera parameters of the second target optical system by a preset camera calibration method;
acquiring camera parameters, wherein the camera parameters comprise coordinates of an optical axis position of a camera on a shot image, a first focal length of the optical axis in an x direction and a second focal length of the optical axis in a y direction;
shooting the second LCD display screen at different gray scale values corresponding to a white picture, a red picture, a green picture and a blue picture respectively through the second target optical system to obtain four groups of sample images, wherein the shooting is carried out in a specific environment, and the exposure time of the sample images is a preset value;
and grouping the sample images according to pictures, marking the gray scale of each sample image, and storing the sample images into the target sample database.
7. An apparatus for image correction, comprising:
the system comprises a first execution unit, a second execution unit and a third execution unit, wherein the first execution unit is used for simulating all sample images in a target sample database through a preset elliptic paraboloid function to obtain correction parameters and a first correction image corresponding to the sample images, the correction parameters comprise first correction parameters and second correction parameters, the target sample database comprises a plurality of sample image groups with different pictures, and the sample image groups comprise sample images with different gray scales;
the first obtaining unit is used for obtaining optimal correction coefficients corresponding to the gray scales and the correction parameters under the sample image group, and the optimal correction coefficients comprise a first optimal correction coefficient and a second optimal correction coefficient;
the second execution unit is used for generating a target correction model after adding the optimal correction coefficient corresponding to each gray scale into a preset correction model according to the sample image group;
a second acquisition unit for acquiring a corrected image, which is obtained by photographing the first target LCD display screen by the first target optical system;
the first determining unit is used for determining a first gray scale corresponding to the corrected image;
the second determining unit is used for determining a target optimal correction coefficient corresponding to the first gray scale in a target correction model through the first gray scale, wherein the target optimal correction coefficient comprises a first target optimal correction coefficient and a second target optimal correction coefficient;
the correcting unit is used for correcting the corrected image by the target optimal correction coefficient according to a preset correction function to obtain a target corrected image;
the first execution unit includes:
the first acquisition module is used for acquiring an untrained first sample group from the target sample database;
the second acquisition module is used for acquiring an untrained sample image from the first sample image group and determining a second gray scale of the sample image;
a third obtaining module, configured to obtain first residual data and second residual data of the sample image, where the first residual data is normalized residual data obtained by the sample image in a horizontal direction of an optical axis center, and the second residual data is normalized residual data obtained by the sample image in a vertical direction of the optical axis center;
the first execution module is used for obtaining a first correction parameter and a second correction parameter corresponding to the second gray scale through a preset elliptic paraboloid function according to the first residual data and the second residual data and generating a first correction image corresponding to the second gray scale;
and the first judging module is used for judging whether the first sample graph group has an image which is not traversed.
8. The apparatus of claim 7, wherein the first obtaining unit comprises:
the first fitting module is used for acquiring a first optimal correction coefficient between each gray scale and the corresponding first correction parameter, wherein the first optimal correction coefficient is a polynomial coefficient obtained by fitting each gray scale and the corresponding first correction parameter through a least square polynomial fitting method and storing the fitting;
and the second fitting module is used for acquiring a second optimal correction coefficient between each gray scale and the corresponding second correction parameter, wherein the second optimal correction coefficient is a polynomial coefficient obtained by fitting each gray scale and the corresponding second correction parameter through a least square polynomial fitting method and storing the fitting.
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