CN114820289B - Fisheye image correction method based on radial symmetric projection model - Google Patents
Fisheye image correction method based on radial symmetric projection model Download PDFInfo
- Publication number
- CN114820289B CN114820289B CN202210488785.6A CN202210488785A CN114820289B CN 114820289 B CN114820289 B CN 114820289B CN 202210488785 A CN202210488785 A CN 202210488785A CN 114820289 B CN114820289 B CN 114820289B
- Authority
- CN
- China
- Prior art keywords
- image
- corrected
- projection
- pixel
- coordinates
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 31
- 238000003702 image correction Methods 0.000 title claims abstract description 13
- 238000012937 correction Methods 0.000 claims abstract description 36
- 239000011159 matrix material Substances 0.000 claims abstract description 18
- 238000011835 investigation Methods 0.000 claims abstract description 10
- 238000013507 mapping Methods 0.000 claims description 5
- 241000251468 Actinopterygii Species 0.000 claims description 4
- 230000000007 visual effect Effects 0.000 abstract description 10
- 230000000694 effects Effects 0.000 abstract description 9
- 238000011084 recovery Methods 0.000 abstract description 8
- 238000013178 mathematical model Methods 0.000 abstract description 6
- DMSMPAJRVJJAGA-UHFFFAOYSA-N benzo[d]isothiazol-3-one Chemical compound C1=CC=C2C(=O)NSC2=C1 DMSMPAJRVJJAGA-UHFFFAOYSA-N 0.000 description 4
- 238000012545 processing Methods 0.000 description 3
- 238000003384 imaging method Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 235000006538 Opuntia tuna Nutrition 0.000 description 1
- 244000237189 Opuntia tuna Species 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000003416 augmentation Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformations in the plane of the image
- G06T3/04—Context-preserving transformations, e.g. by using an importance map
- G06T3/047—Fisheye or wide-angle transformations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformations in the plane of the image
- G06T3/40—Scaling of whole images or parts thereof, e.g. expanding or contracting
- G06T3/4007—Scaling of whole images or parts thereof, e.g. expanding or contracting based on interpolation, e.g. bilinear interpolation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/80—Geometric correction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/80—Analysis of captured images to determine intrinsic or extrinsic camera parameters, i.e. camera calibration
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30244—Camera pose
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Image Processing (AREA)
Abstract
The invention discloses a fisheye image correction method based on a radial symmetric projection model, which comprises the following steps: acquiring an internal reference matrix and a radial distortion coefficient of a camera for shooting a fish-eye image to be corrected; calculating the width and height of a correction image generated in a projection coordinate system according to the preset box-shaped curved surface middle duty ratio, the projection point backward movement distance and the preset correction image resolution; traversing each pixel point from the initial position of the corrected image according to the internal reference matrix and the radial distortion coefficient, and calculating the pixel values of all the pixel points on the corrected image to obtain the corrected image; acquiring a manual investigation result of the correction image, and if the manual investigation result meets the requirement, finishing correction; if the requirements are not met, resetting the middle duty ratio of the box-shaped curved surface and the backward moving distance of the projection point; and performing circulation until the correction is completed. The method can accurately retain all information on the fisheye image, and has good straight line recovery effect and visual effect after correction, the constructed mathematical model is easy to analyze, and the parameter adjustment is simple and convenient.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to a fisheye image correction method based on a radial symmetric projection model.
Background
A fisheye lens is a lens with a large angle of view, and a camera using the fisheye lens can capture a wide range of visual information. However, the captured fisheye image has larger distortion, which not only affects the visual effect, but also brings great difficulty to the subsequent processing such as straight line detection.
There are various methods for correcting the current fisheye image, and they are roughly classified into an image-based method and a camera calibration-based method. The image-based method does not need to acquire information of shooting equipment, and is flexible to apply. However, the method can only reduce the distortion degree of the picture, cannot eliminate the distortion, and has an unsatisfactory effect on the straight line recovery. Based on the camera calibration method, an internal reference matrix and radial distortion parameters of a camera are required to be acquired, and a three-dimensional projection model is utilized to correct a picture. Zhu Huo Canada (Juho Kannala) and Sami S Bo Lant (Sami S. Brandt) propose a general camera model: kannala-Brandt model, a radially symmetric projection model.
Based on the model, the problem of correcting the fisheye image is converted into the problem of projecting the spherical image onto a plane. The simplest projection method is pinhole projection, and the correction method using pinhole projection has been integrated into Matlab vision toolkit and open source computer vision library OpenCV, and is the preferred method for correcting fisheye images. Pinhole projection, while capable of fully restoring straight lines, can only correct the middle portion of the fisheye image, resulting in a significant loss of edge information. In order to preserve all the information on the spherical image, the following researchers have proposed a number of projection methods: the projection method of Panini is proposed by Sharpless et al, which performs well in distortion correction of panoramic images at large viewing angles, but does not perform well in fisheye images; carroll et al propose a correction method based on minimizing line distortion, and achieve a very good effect on straight line restoration, but the method requires manual line drawing and marking on a fisheye image, and is complex to operate; chang et al propose two-step projection, the visual effect after correction is good, the straight line recovery is ideal, but a new distortion is introduced throughout the whole image, the mathematical model is complex, and the analysis is inconvenient.
Therefore, on the basis of the existing fisheye image correction method, how to provide a fisheye image correction method which has good straight line recovery effect and retains all information on the fisheye image becomes a problem to be solved by those skilled in the art.
Disclosure of Invention
In view of the above problems, the invention provides a fisheye image correction method based on a radial symmetric projection model, which at least solves part of the technical problems, can retain all information on the fisheye image, has good straight line recovery effect and visual effect after correction, and the constructed mathematical model is easy to analyze, and the parameter adjustment is simple and convenient.
The embodiment of the invention provides a fisheye image correction method based on a radial symmetric projection model, which comprises the following steps:
S1, acquiring an internal reference matrix and a radial distortion coefficient of a camera for shooting a fish-eye image to be corrected;
s2, calculating the width and height of the corrected image generated in the projection coordinate system according to the preset box-shaped curved surface middle duty ratio, the projection point backward movement distance and the preset corrected image resolution; traversing each pixel point from the initial position of the corrected image according to the internal reference matrix and the radial distortion coefficient, and calculating the pixel values of all the pixel points on the corrected image to obtain the corrected image;
s3, acquiring a manual investigation result of the correction image, and if the manual investigation result meets the requirement, finishing correction; if the requirements are not met, resetting the middle duty ratio of the box-shaped curved surface and the backward shift distance of the projection point;
s4, circularly executing the steps S2 to S3 until the correction is completed.
Further, the step S2 includes:
s21, calculating the width and height of a correction image generated in a projection coordinate system according to the preset box-shaped curved surface middle duty ratio, the projection point backward movement distance and the preset correction image resolution;
s22, starting from an initial position, determining pixel coordinate mapping between the corrected image and the fish-eye image to be corrected, and respectively calculating a projection azimuth angle alpha and a projection azimuth angle theta according to the radial distortion coefficient;
S23, calculating pixel coordinates of each pixel point on the corrected image mapped to the fish-eye image to be corrected according to the projection azimuth angle alpha, the projection azimuth angle theta and the internal reference matrix;
And S24, interpolating and calculating pixel values of all pixel points on the corrected image by using a bilinear interpolation method according to the pixel coordinates to obtain the corrected image.
Further, the width and height of the corrected image generated in the projection coordinate system are calculated by the following formulas, respectively:
In the above formula, w R represents the width of the corrected image; h R denotes the height of the rectified image; a c represents the middle duty ratio of the box-shaped curved surface; d represents the backward movement distance of the projection point.
Further, the step S22 includes:
s221, starting from an initial position, determining pixel coordinate mapping between the corrected image and the fish-eye image to be corrected, and calculating coordinates of each pixel point on the corrected image in the projection coordinate system;
S222, calculating the coordinates of each pixel point on the corrected image projected onto a box-shaped curved surface according to the coordinates of each pixel point on the corrected image in the projection coordinate system;
S223, calculating a projection azimuth angle alpha and a projection azimuth angle theta according to the coordinates of each pixel point on the corrected image projected onto the box-shaped curved surface and the radial distortion coefficient.
Further, the coordinates of each pixel point on the corrected image in the projection coordinate system are calculated by the following formula:
In the above formula, [ x 3;y3;z3 ] represents the coordinates of the pixel point on the corrected image in the projection coordinate system; Pixel coordinates representing pixel points on the rectified image; w R denotes the width of the corrected image; h R denotes the height of the rectified image; cols denotes the horizontal resolution of the corrected image; rows represents the vertical resolution of the rectified image; d represents the backward movement distance of the projection point.
Further, the coordinates of each pixel point on the corrected image projected onto the box-shaped curved surface are calculated by the following formula:
in the above formula, [ x 3;y3;z3 ] represents the coordinates of the pixel point on the corrected image in the projection coordinate system; a c represents the middle duty ratio of the box-shaped curved surface; [ x r;yr;zr ] represents the coordinates of the pixel points on the corrected image on the box-shaped curved surface; d represents the backward movement distance of the projection point.
Further, the projection azimuth α and the projection azimuth θ are calculated by the following formulas, respectively:
In the above formula, [ x r;yr;zr ] represents the coordinates of the pixel point on the corrected image on the box-shaped curved surface; [ D 1;d2;d3;d4 ] =d, representing the radial distortion coefficient.
Further, the step S23 includes:
S231, according to the projection azimuth angle alpha and the projection azimuth angle theta, calculating camera coordinates of each pixel point on the corrected image projected onto the fish-eye image to be corrected according to a radial symmetric projection model;
S232, calculating pixel coordinates of each pixel point on the corrected image mapped to the fish-eye image to be corrected according to the camera coordinates and the internal reference matrix.
Further, in the step S24, the pixel values of all the pixel points on the corrected image are interpolated by the following formula:
in the above formula, G (x, y) represents a pixel value of a pixel point on the corrected image; [ x f;yf ] represents the pixel coordinates; x d,yd represents the integer part of x f,yf, respectively; u, v represent the fractional parts of x f,yf, respectively; f (x d,yd) represents the pixel value of the pixel point with the coordinates of (x d,yd) on the fish eye image to be corrected; f (x d,yd +1) represents the pixel value of the pixel point with the coordinates of (x d,yd +1) on the fisheye image to be corrected; f (x d+1,yd) represents the pixel value of the pixel point with the coordinates of (x d+1,yd) on the fish eye image to be corrected; f (x d+1,yd +1) represents the pixel value of the pixel point with the coordinates of (x d+1,yd +1) on the fisheye image to be corrected.
The technical scheme provided by the embodiment of the invention has the beneficial effects that at least:
The fisheye image correction method based on the radial symmetric projection model provided by the embodiment of the invention comprises the following steps: acquiring an internal reference matrix and a radial distortion coefficient of a camera for shooting a fish-eye image to be corrected; calculating the width and height of a correction image generated in a projection coordinate system according to the preset box-shaped curved surface middle duty ratio, the projection point backward movement distance and the preset correction image resolution; traversing each pixel point from the initial position of the corrected image according to the internal reference matrix and the radial distortion coefficient, and calculating the pixel values of all the pixel points on the corrected image to obtain the corrected image; acquiring a manual investigation result of the correction image, and if the manual investigation result meets the requirement, finishing correction; if the requirements are not met, resetting the middle duty ratio of the box-shaped curved surface and the backward moving distance of the projection point; and performing circulation until the correction is completed. The method can accurately retain all information on the fisheye image, and has good straight line recovery effect and visual effect after correction, the constructed mathematical model is easy to analyze, and the parameter adjustment is simple and convenient.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
Fig. 1 is a flowchart of a fisheye image correction method based on a radially symmetric projection model according to an embodiment of the present invention;
fig. 2 is a fisheye image to be corrected according to an embodiment of the present invention;
FIG. 3 is an overall flow chart provided by an embodiment of the present invention;
Fig. 4 is a schematic view of box-shaped curved surface projection provided in an embodiment of the present invention;
FIG. 5 is a schematic view of a radially symmetric projection model according to an embodiment of the present invention;
FIG. 6 is an initial rectified image provided by an embodiment of the present invention;
FIG. 7 is a corrected image after adjustment of a c according to an embodiment of the present invention;
FIG. 8 is a graph showing the final correction result provided by the embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The embodiment of the invention provides a fisheye image correction method based on a radial symmetric projection model, which is shown by referring to fig. 1 and comprises the following steps:
S1, acquiring an internal reference matrix and a radial distortion coefficient of a camera for shooting a fish-eye image to be corrected;
S2, calculating the width and height of the corrected image generated in the projection coordinate system according to the preset box-shaped curved surface middle duty ratio, the projection point backward movement distance and the preset corrected image resolution; traversing each pixel point from the initial position of the corrected image according to the internal reference matrix and the radial distortion coefficient, and calculating the pixel values of all the pixel points on the corrected image to obtain the corrected image;
S3, acquiring a manual investigation result of the correction image, and if the manual investigation result meets the requirement, finishing correction; if the requirements are not met, resetting the middle duty ratio of the box-shaped curved surface and the backward moving distance of the projection point;
s4, circularly executing the steps S2 to S3 until the correction is completed.
The fisheye image correction method based on the radial symmetric projection model provided by the embodiment can accurately reserve all information on the fisheye image to be corrected, has good straight line recovery effect and good visual effect after correction, is easy to analyze in a mathematical model, and is simple and convenient in parameter adjustment.
The method provided in this embodiment will be described below with a practical application example:
collecting a fisheye image to be corrected through a monocular fisheye camera (the visual angle of the monocular fisheye camera in the vertical direction is not more than 165 degrees, and the visual angle of the monocular fisheye camera in the horizontal direction is not more than 180 degrees), wherein a USB1080P camera manufactured by Weiaoko technology limited company in Shenzhen city can be used for shooting the fisheye image together with a 180-degree fisheye lens, and the fisheye image to be corrected is collected as shown in reference to FIG. 2; and (5) conveying the collected fisheye images to a PC for processing. Referring to fig. 3, an overall flowchart of the method provided in this embodiment is shown.
Parameters of the camera are obtained in advance through calibration:
Internal reference matrix:
radial distortion coefficient: d= [ -0.02160;0.00237; -0.00372;0.00159];
the resolution (pixel resolution) of the output corrected image is set to be the same as that of the input fish-eye image to be corrected: cols x Rows = 1920 x 1080;
Setting calculation program parameters: setting the middle ratio a c＝0.5(0<ac < 1) of the box-shaped curved surface; the proxel back shift distance d=1 (0 < d < 5) is set.
Calculating the width of the corrected image in the projection coordinate system:
Computing the height of the rectified image in the projection coordinate system:
In the above formula, w R represents the width of the corrected image; h R denotes the height of the rectified image; a c represents the middle duty ratio of the box-shaped curved surface; d represents the distance by which the proxel is moved back.
Starting from the initial position (x=0, y=0), determining a pixel coordinate mapping between the corrected image G and the fish-eye image F, traversing each pixel point of the corrected image, and calculating pixel values of all pixel points of the corrected image, that is, calculating pixel values G (x, y) of pixel points with coordinates of (x, y) in the corrected image:
1) Starting from the initial position, calculating coordinates of each pixel point in the corrected image under a projection coordinate system: [ x 3;y3;z3 ]; Representing pixel coordinates of a pixel point on the rectified image;
That is to say,
In the above formula, [ x 3;y3;z3 ] represents the coordinates of the pixel point on the corrected image in the projection coordinate system; w R denotes the width of the corrected image; h R denotes the height of the rectified image; cols denotes the horizontal resolution of the corrected image; rows represents the vertical resolution of the rectified image; d represents the distance by which the proxel is moved back.
Calculating the width w c of the middle plane of the box-shaped curved surface:
the box-shaped curved surface related structure can be shown with reference to fig. 4.
2) Projecting the pixel points on the corrected image onto the box-shaped curved surface; according to the coordinates of each pixel point on the corrected image in the projection coordinate system, calculating the coordinates of each pixel point on the corrected image projected onto the box-shaped curved surface: [ x r;yr;zr ];
When (when) In the time-course of which the first and second contact surfaces,
When (when)In the time-course of which the first and second contact surfaces,
When (when)In the time-course of which the first and second contact surfaces,
3) According to the coordinates of each pixel point on the corrected image projected onto the box-shaped curved surface and the radial distortion coefficient D, referring to a radial symmetric projection model shown in FIG. 5, a projection azimuth angle alpha and a projection azimuth angle theta are respectively calculated in the following manner; in FIG. 4, α isAn included angle with the positive direction of the X axis, PQ [ T ] plane XOY; theta 1 isAnd the included angle theta with the positive direction of the Z axis is the angle theta 1 after distortion.
Projection azimuth α=arctan 2 (y r,xr);
wherein arctan2 (y ', x') is an arctangent function Is to calculate any vector in a plane rectangular coordinate system, wherein arctan2 (y ', x') is the generalization ofIncluded angle with positive direction of x-axis, range (-pi, pi);
and (3) carrying out distortion on the theta 1 to obtain a projection azimuth angle theta:
Wherein D= [ D 1;d2;d3;d4 ] is a radial distortion parameter of the lens;
4) According to the projection azimuth angle alpha and the projection azimuth angle theta, according to a radial symmetric projection model (equidistant projection model), calculating camera coordinates of each pixel point on the corrected image projected onto the fish-eye image to be corrected: [ x 2;y2; 1];
According to the radially symmetric projection model: Again according to r=θ,
5) According to the camera coordinates and the internal reference matrix K, calculating pixel coordinates of each pixel point on the corrected image, which are mapped onto the fish-eye image to be corrected
Wherein K is an internal reference matrix of the camera; recording device[ X f;yf; 1 is an augmentation of [ x f;yf ];
6) According to pixel coordinates, pixel values of all pixel points on the corrected image are calculated by using bilinear interpolation and pixel-by-pixel gray interpolation, specifically: wherein H is an interpolation function of the fisheye image F;
Taking bilinear interpolation as an example, let [ x f;yf]＝[xd+u;yd+v],[xf;yf ] denote pixel coordinates, x d,yd be the integer part of x f,yf, and u, v be the fractional part of x f,yf, respectively;
f (x ', y') is a pixel value of a pixel point with coordinates (x ', y') on the fisheye image, and includes three components of RGB:
H (x f,yf) is the interpolation result of the pixel point with the coordinates (x f,yf) on the fisheye image F;
order the γ＝[F(xd,yd),F(xd,yd+1),F(xd+1,yd),F(xd+1,yd+1)]
If (x f,yf) is within the boundary of the fisheye image F (within the index range of F):
wherein, beta= [ (1-u) (1-v), (1-u) v, u (1-v), uv ],
If (x f,yf) exceeds the boundary of the fisheye image (exceeds the index range of F):
That is to say,
Sequentially calculating pixel values of all pixel points on the corrected imageAnd obtaining a corrected image. That is, the corrected image is determined based on the coordinates and pixel values of the respective pixels on the corrected image.
Referring to fig. 6, a corrected image is shown. The checkerboard in the obtained corrected image is found to exceed the middle part of the box-shaped curved surface, the middle ratio a c of the box-shaped curved surface needs to be increased, the middle ratio of the box-shaped curved surface is adjusted, and a c =0.68 and d=1 are taken.
As a result of recalculation to obtain a corrected image, referring to fig. 7, it was found that the bottle on the left side was elongated, and it was necessary to increase the distance d by which the proxel was moved backward to compress the width on the left and right sides, taking d=1.75, and a c =0.68.
The corrected image is obtained through recalculation, the result is shown in fig. 8, the correction effect is good, and the final result is obtained after correction is completed.
In this embodiment, the known camera internal parameters and distortion coefficients are used to project the pixels of the corrected image onto the box-shaped curved surface one by one, then project the pixels of the corrected image onto the fisheye image from the box-shaped curved surface, and calculate the whole corrected image by using the pixel values of the fisheye image, so as to realize the correction of the fisheye image. The edge information of the fisheye image can be effectively reserved, and all information on the fisheye image is reserved; most of the straight lines are recovered, the straight lines after correction have good recovery effect and visual effect, the constructed mathematical model is easy to analyze, the calculation is simple, and the parameter adjustment is simple and convenient. The method is suitable for fisheye images shot by various fisheye cameras.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (1)
1. A fisheye image correction method based on a radial symmetric projection model is characterized by comprising the following steps:
S1, acquiring an internal reference matrix and a radial distortion coefficient of a camera for shooting a fish-eye image to be corrected;
s2, calculating the width and height of the corrected image generated in the projection coordinate system according to the preset box-shaped curved surface middle duty ratio, the projection point backward movement distance and the preset corrected image resolution; traversing each pixel point from the initial position of the corrected image according to the internal reference matrix and the radial distortion coefficient, and calculating the pixel values of all the pixel points on the corrected image to obtain the corrected image;
The step S2 includes:
s21, calculating the width and height of a correction image generated in a projection coordinate system according to the preset box-shaped curved surface middle duty ratio, the projection point backward movement distance and the preset correction image resolution;
the width and height of the rectified image generated in the projection coordinate system are calculated by the following formulas, respectively:
In the above formula, w R represents the width of the corrected image; h R denotes the height of the rectified image; a c represents the middle duty ratio of the box-shaped curved surface; d represents the backward movement distance of the projection point;
s22, starting from an initial position, determining pixel coordinate mapping between the corrected image and the fish-eye image to be corrected, and respectively calculating a projection azimuth angle alpha and a projection azimuth angle theta according to the radial distortion coefficient;
The step S22 includes:
s221, starting from an initial position, determining pixel coordinate mapping between the corrected image and the fish-eye image to be corrected, and calculating coordinates of each pixel point on the corrected image in the projection coordinate system;
the coordinates of each pixel point on the corrected image in the projection coordinate system are calculated by the following formula:
In the above formula, [ x 3;y3;z3 ] represents the coordinates of the pixel point on the corrected image in the projection coordinate system; Pixel coordinates representing pixel points on the rectified image; w R denotes the width of the corrected image; h R denotes the height of the rectified image; cols denotes the horizontal resolution of the corrected image; rows represents the vertical resolution of the rectified image; d represents the backward movement distance of the projection point;
S222, calculating the coordinates of each pixel point on the corrected image projected onto a box-shaped curved surface according to the coordinates of each pixel point on the corrected image in the projection coordinate system;
The coordinates of each pixel point on the corrected image projected onto the box-shaped curved surface are calculated by the following formula:
In the above formula, [ x 3;y3;z3 ] represents the coordinates of the pixel point on the corrected image in the projection coordinate system; a c represents the middle duty ratio of the box-shaped curved surface; [ x r;yr;zr ] represents the coordinates of the pixel points on the corrected image on the box-shaped curved surface; d represents the backward movement distance of the projection point;
s223, respectively calculating a projection azimuth angle alpha and a projection azimuth angle theta according to the coordinates of each pixel point on the corrected image projected onto the box-shaped curved surface and the radial distortion coefficient;
the projection azimuth α and the projection azimuth θ are calculated by the following formulas, respectively:
in the above formula, [ x r;yr;zr ] represents the coordinates of the pixel point on the corrected image on the box-shaped curved surface; [ D 1;d2;d3;d4 ] =d, representing the radial distortion coefficient;
S23, calculating pixel coordinates of each pixel point on the corrected image mapped to the fish-eye image to be corrected according to the projection azimuth angle alpha, the projection azimuth angle theta and the internal reference matrix;
the step S23 includes:
S231, according to the projection azimuth angle alpha and the projection azimuth angle theta, calculating camera coordinates of each pixel point on the corrected image projected onto the fish-eye image to be corrected according to a radial symmetric projection model;
S232, calculating pixel coordinates of each pixel point on the corrected image mapped to the fish-eye image to be corrected according to the camera coordinates and the internal reference matrix;
s24, interpolating and calculating pixel values of all pixel points on the corrected image by using a bilinear interpolation method according to the pixel coordinates to obtain the corrected image;
In the step S24, the pixel values of all the pixel points on the corrected image are interpolated by the following formula:
In the above formula, G (x, y) represents a pixel value of a pixel point on the corrected image; [ x f;yf ] represents the pixel coordinates; x d,yd represents the integer part of x f,yf, respectively; u, v represent the fractional parts of x f,yf, respectively; f (x d,yd) represents the pixel value of the pixel point with the coordinates of (x d,yd) on the fish eye image to be corrected; f (x d,yd +1) represents the pixel value of the pixel point with the coordinates of (x d,yd +1) on the fisheye image to be corrected; f (x d+1,yd) represents the pixel value of the pixel point with the coordinates of (x d+1,yd) on the fish eye image to be corrected; f (x d+1,yd +1) represents the pixel value of the pixel point with the coordinates of (x d+1,yd +1) on the fisheye image to be corrected;
s3, acquiring a manual investigation result of the correction image, and if the manual investigation result meets the requirement, finishing correction; if the requirements are not met, resetting the middle duty ratio of the box-shaped curved surface and the backward shift distance of the projection point;
s4, circularly executing the steps S2 to S3 until the correction is completed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210488785.6A CN114820289B (en) | 2022-05-06 | 2022-05-06 | Fisheye image correction method based on radial symmetric projection model |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210488785.6A CN114820289B (en) | 2022-05-06 | 2022-05-06 | Fisheye image correction method based on radial symmetric projection model |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114820289A CN114820289A (en) | 2022-07-29 |
| CN114820289B true CN114820289B (en) | 2024-08-02 |
Family
ID=82512541
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210488785.6A Active CN114820289B (en) | 2022-05-06 | 2022-05-06 | Fisheye image correction method based on radial symmetric projection model |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114820289B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109964245A (en) * | 2016-12-06 | 2019-07-02 | 深圳市大疆创新科技有限公司 | System and method for correcting wide angle picture |
| CN114331826A (en) * | 2022-01-19 | 2022-04-12 | 陕西科技大学 | Fisheye image rapid correction method based on distortion tension factor |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP6627352B2 (en) * | 2015-09-15 | 2020-01-08 | カシオ計算機株式会社 | Image display device, image display method, and program |
-
2022
- 2022-05-06 CN CN202210488785.6A patent/CN114820289B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109964245A (en) * | 2016-12-06 | 2019-07-02 | 深圳市大疆创新科技有限公司 | System and method for correcting wide angle picture |
| CN114331826A (en) * | 2022-01-19 | 2022-04-12 | 陕西科技大学 | Fisheye image rapid correction method based on distortion tension factor |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114820289A (en) | 2022-07-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Chen et al. | Camera lens super-resolution | |
| CN110276734B (en) | Image distortion correction method and device | |
| TWI622021B (en) | Method and apparatus for generating panoramic image with stitching process | |
| US9262807B2 (en) | Method and system for correcting a distorted input image | |
| CN109064404A (en) | It is a kind of based on polyphaser calibration panorama mosaic method, panoramic mosaic system | |
| CN110111250B (en) | Robust automatic panoramic unmanned aerial vehicle image splicing method and device | |
| JP5437311B2 (en) | Image correction method, image correction system, angle estimation method, and angle estimation device | |
| CN103839227B (en) | Fisheye image correcting method and device | |
| CN111199528A (en) | Fisheye image distortion correction method | |
| CN104994367B (en) | A kind of image correction method and camera | |
| CN106920210B (en) | A kind of flake video real time panoramic bearing calibration based on development of a sphere model | |
| CN109859137B (en) | Wide-angle camera irregular distortion global correction method | |
| CN110246161B (en) | Method for seamless splicing of 360-degree panoramic images | |
| CN116958437A (en) | Multi-view reconstruction method and system integrating attention mechanism | |
| JP2008512767A (en) | General two-dimensional spatial transformation expression system and method | |
| KR101140953B1 (en) | Method and apparatus for correcting distorted image | |
| CN116129059B (en) | Three-dimensional point cloud set generation and reinforcement method, device, equipment and storage medium | |
| CN111292278A (en) | Image fusion method and device, storage medium and terminal | |
| CN113905147A (en) | Method and device for removing jitter of marine monitoring video picture and storage medium | |
| CN114616586A (en) | Image annotation method and device, electronic equipment and computer-readable storage medium | |
| JP2012003503A (en) | Image processing device, method for controlling the same, and program | |
| US8571356B2 (en) | Image processing apparatus, image processing method, and image processing program | |
| CN114820289B (en) | Fisheye image correction method based on radial symmetric projection model | |
| CN113034365B (en) | Multi-picture splicing method and system based on super pixels | |
| CN114972025A (en) | Image fast splicing method based on YUV color space |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |