CN110322485B - Rapid image registration method of heterogeneous multi-camera imaging system - Google Patents
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Abstract
The invention discloses a rapid image registration method of a heterogeneous multi-camera imaging system. The method comprises the following steps: determining internal parameters and distortion coefficients of each camera by using a Zhang-Yong calibration algorithm according to a plurality of groups of collected calibration plate images with different poses; selecting a reference camera coordinate system, and determining a spatial position transformation relation of each camera relative to a reference camera by using a stereo calibration algorithm; solving a coordinate mapping relation of each camera image plane relative to a reference camera image plane according to camera internal parameters, distortion coefficients, a space position transformation relation between cameras and a target distance; and according to the coordinate mapping relation lookup table, realizing rapid image registration. The method is suitable for the heterogeneous multi-camera system to realize real-time registration, and is simple, efficient and high in practical value.
Description
Technical Field
The present invention relates to the field of image processing, and more particularly, to a fast image registration method for heterogeneous multi-camera imaging systems.
Background
The image registration technology is a basic technology in the field of image processing, and is widely applied to tasks such as remote sensing data analysis, medical image analysis, computer vision and the like. Due to the large difference of image sources, factors such as different application scenes, different acquisition time points, different acquisition visual angles, different image sensors and the like need to be considered in the general image registration technology to select an applicable algorithm.
The image registration method based on features is most widely researched, and usually needs steps of feature detection, feature matching, transformation model estimation, resampling, transformation and the like, but has the defects of high computational complexity, long running time and difficulty in solving the registration problem of image pairs with large source difference, such as multispectral and polarized image registration and the like.
In recent years, with the development of heterogeneous multi-camera imaging systems, such as multi-spectral camera systems, multi-camera polarization imaging systems, and the like, new requirements for the accuracy and efficiency of image registration techniques are provided. Especially, the array camera system shows great application value and prospect in the fields of mobile phone camera systems, automatic driving, panoramic or stereo video, monitoring security, light field cameras, super-resolution reconstruction and the like, however, in these application scenes, due to the difference of intrinsic attributes of images, it is difficult to rapidly and accurately obtain the pixel level matching relationship to realize image registration, thereby influencing the development of subsequent algorithms and applications.
Disclosure of Invention
The invention aims to provide a quick image registration method of a heterogeneous multi-camera imaging system, which can realize real-time image registration of the heterogeneous multi-camera imaging system.
The invention provides a quick image registration method of a heterogeneous multi-camera imaging system, which comprises the following steps:
determining internal parameters and distortion coefficients of each camera by using a Zhang-Zhengyou calibration algorithm according to a plurality of groups of clear checkerboard calibration board images at different positions and postures acquired by a heterogeneous multi-camera system;
selecting a reference camera coordinate system, and sequentially determining the spatial position transformation relation of each camera coordinate system relative to the reference camera coordinate system by using a binocular vision stereo calibration algorithm;
solving the pixel-level coordinate transformation relation of each camera image plane relative to the reference camera image plane according to the camera internal parameters, the distortion coefficients, the space position transformation relation among the camera coordinate systems and the set target distance of the heterogeneous multi-camera system, and constructing a corresponding coordinate mapping relation lookup table;
and transforming the original image acquired by each camera to an image plane of a reference camera according to a coordinate mapping relation lookup table of each camera in the heterogeneous multi-camera system, thereby realizing rapid image registration.
Each camera of the heterogeneous multi-camera system is an imaging system consisting of an optical lens group and an image sensor.
Compared with the prior art, the invention has the remarkable advantages that:
(1) the rapid image registration method of the heterogeneous multi-camera imaging system is easy to implement, simple in calculation process and high in calculation efficiency, and real-time dynamic calculation can be achieved;
(2) the rapid image registration method is irrelevant to the intrinsic property of the image, is not influenced by the gray level and the characteristics of the image, and is suitable for various heterogeneous multi-camera imaging systems such as a multi-spectral array camera system, a multi-camera polarization system and the like;
(3) the rapid image registration method considers the distortion model of the heterogeneous multi-camera system and can finish the distortion correction work of the system at the same time, thereby improving the data precision and providing a good basis for the development of subsequent algorithms and applications.
Drawings
FIG. 1 is a schematic flow chart diagram of the fast image registration method of the present invention;
FIG. 2 is a schematic flow chart of the calculation of a coordinate mapping relationship lookup table in the fast image registration method of the present invention;
FIG. 3 is a schematic structural diagram of a heterogeneous multi-camera imaging system in an embodiment of the present invention;
FIG. 4 is a schematic diagram of an image registration scenario for a heterogeneous multispectral camera in an embodiment of the present invention;
in the figure: 310-a pre-filter array, 320-a pre-polarizer array, 330-an image sensor array unit and 340-a calculation processing unit; 410. 420, 430, 440-original non-registered 450nm, 550nm, 650nm, 700nm band images, 411, 421, 431, 441-registered 450nm, 550nm, 650nm, 700nm band images, 450, 451-original non-registered, registered multi-spectral band fusion images;
Detailed Description
The invention is further described with reference to the following figures and specific examples.
With reference to fig. 1, the present invention provides a fast image registration method for a heterogeneous multi-camera imaging system, which comprises the following specific steps:
it should be appreciated that the internal parameter matrix C for each camera in step 110 i Can be expressed as
Wherein f is x ＝f/d x And f y ＝f/d y Represents the focal length in pixels, d x And d y For the size of a single pixel of the image sensor, c x And c y Representing camera principal point coordinates; distortion coefficient matrix D of each camera i Can be expressed as [ k ] i1 ,k i2 ,k i3 ,p i1 ,p i2 ]Wherein [ k ] i1 ,k i2 ,k i3 ]Represents the radial distortion coefficient, [ p ] i1 ,p i2 ]Representing the tangential distortion coefficient.
it should be appreciated that the spatial positional transformation of each camera coordinate system relative to the reference camera coordinate system in step 120 includes the rotation matrix and R 0i And translation matrix T 0i Where the subscripts "0" and "i" represent the reference camera and the ith camera, respectively.
specifically, fig. 2 is a schematic diagram of a calculation process of the coordinate mapping relation lookup table in step 130, and the implementation process is as follows:
in conjunction with step 210 of FIG. 2, the ideal distortion-free point p in the plane coordinate system of the reference camera image is first determined 0 (u, v) projection onto a world coordinate system (physical coordinate system of a reference camera is typically chosen) P 0 (X 0 ，Y 0 ，Z 0 )：
Wherein Z is 0 Indicates the set target distance, C 0 Representing an internal reference matrix of a reference camera, superscripted with a "-1" tableShowing the inverse of the matrix, the superscript "T" showing the transpose of the matrix, [ u, v]The value range is within the image resolution range.
In conjunction with step 220 of FIG. 2, the world coordinates P of the target point are then determined 0 (X 0 ，Y 0 ，Z 0 ) Transforming to the physical coordinate systems of other cameras to obtain corresponding physical coordinate points P i (X i ，Y i ，Z i )：
[X i ，Y i ，Z i ，1] T ＝[R 0i | 0i ][X 0 ,Y 0 ,Z 0 ,1] T
Wherein [ R ] 0i |T 0i ]Representing a transformation matrix, R, between a reference camera coordinate system and an i-th camera coordinate system 0i Representing a rotation matrix, T 0i Representing a translation matrix.
In conjunction with step 230 of FIG. 2, the physical coordinate points P in each camera coordinate system are then compared i (X i ,Y i ,Z i ) Projecting the image plane coordinate system of the corresponding camera to obtain a pixel level coordinate p i (u i ，v i )：
[X′ i ,Y′ i ]＝[X i /Z i ,Y i /Z i ]
[u i ,v i ,1] T ＝C i [X″ i ,Y″ i ,1] T
Wherein, the first and the second end of the pipe are connected with each other,
[X′ i ,Y′ i ]is P i Normalized coordinates of [ X "] i ,Y″ i ]Is [ X' i ,Y′ i ]Distorted physical coordinates of (a); [ k ] A i1 ,k i2 ,k i3 ,p i1 ,p i2 ]Represents the distortion parameter of the i-th camera, [ k ] i1 ,k i2 ，k i3 ]Represents a radial distortion parameter, [ p ] i1 ,p i2 ]Representing the tangential distortion parameter, C i Representing the reference matrix of the ith camera.
In conjunction with step 240 of fig. 2, a pixel-level coordinate transformation relation of each camera image plane with respect to the reference camera image plane is finally constructed, i.e. a coordinate mapping relation lookup table MAP i ：
MAP i (u，v)＝[u i ，v i ] T
And 140, transforming the original image acquired by each camera to an image plane of a reference camera according to a coordinate mapping relation lookup table of each camera in the heterogeneous multi-camera system, thereby realizing rapid image registration.
Specifically, the method for implementing fast image registration in step 140 is directly based on the coordinate relation lookup table:
wherein the content of the first and second substances,
and
respectively representing the original image and the registered image acquired by the ith camera.
It should be noted that, steps 110 and 120 belong to a pre-calibration process, and the calibrated internal parameters of the camera, distortion coefficients, and spatial position transformation relationship between camera coordinate systems should be stored as constants. Steps 130 and 140 should be sequentially executed in the actual registration task of the heterogeneous multi-camera imaging system, that is, firstly, the stored calibration parameters are read and the target distance is set so as to calculate the coordinate mapping relation lookup table (step 130), and then the acquired original image is subjected to coordinate transformation so as to be rapidly registered to the reference camera image plane (step 140).
The invention provides a quick image registration method of a heterogeneous multi-camera imaging system, which is irrelevant to the intrinsic property of an image, is not influenced by the gray level and the characteristics of the image, has high calculation efficiency and wide applicability, can be directly applied to various heterogeneous multi-camera imaging systems such as a multi-spectral array camera system, a multi-camera polarization system and the like, and completes the real-time registration task.
Examples
The present invention is described in further detail by taking the task of image registration of a heterogeneous multi-camera imaging system as an example.
Referring to fig. 3, the heterogeneous multi-camera imaging system of the present embodiment includes a front optical lens group array, an image sensor array unit 330, and a calculation processing unit 340. The front optical lens group array can be a light filter array 310 or a polarizer array 320, and respectively forms a heterogeneous multispectral camera system and a heterogeneous multi-camera polarization system.
Specifically, the present embodiment takes a heterogeneous multispectral camera system as an example to perform image registration. The front filter array 310 of the heterogeneous multispectral camera system is composed of narrow-band filters of 6 different channels, the central wavelengths of the narrow-band filters are respectively 450nm, 500nm, 550nm, 600nm, 650nm and 700nm, the bandwidths of the narrow-band filters are about 20nm, and the narrow-band filters sequentially correspond to 0 th camera, 1 st camera, 2 nd camera, 3 rd camera, 4 th camera and 5 th camera; the image sensor array unit 330 is composed of 6 image sensors of the same type, the gray level image acquisition resolution is 1600x1300, and the bit width is 8 bit; the computing processing unit is an embedded computer system for performing computing tasks involved in image acquisition and registration.
The specific implementation flow of this embodiment is as follows:
with reference to step 110 and step 120 in fig. 1, a pre-calibration process of the heterogeneous multispectral camera system is performed: firstly, shooting a plurality of groups of clear checkerboard calibration board images with different positions and postures, and determining an internal parameter C of each camera by using a Zhang-Yongyou calibration algorithm i And distortion coefficient D i (ii) a Then selecting a camera No. 0 (corresponding to a 450nm wave band) as a reference camera coordinate system, and sequentially determining the spatial position transformation relation [ R ] of each camera coordinate system relative to the camera No. 0 coordinate system by using a binocular vision stereo calibration algorithm 0i |T 0i ]Wherein i is 0, 1,2,3, 4, 5.
It should be understood that any one of the heterogeneous multispectral camera systems may be selected as the reference camera coordinate system.
The parameters obtained in steps 110 and 120, including the internal parameters and distortion coefficients of each camera and the spatial position conversion relationship of each camera coordinate system with respect to the camera coordinate system No. 0, are stored in a calculation processing unit (340).
Combining step 130 in FIG. 1 with step 2 in FIG. 2, the camera intrinsic parameters C of the heterogeneous multispectral camera system i Distortion coefficient D i And the spatial position transformation relation between the camera coordinate systems [ R ] 0i |T 0i ]And a set target distance Z 0 Solving the pixel-level coordinate transformation relation of each camera image plane relative to the reference camera image plane and constructing a corresponding coordinate mapping relation lookup table MAP i ：
MAP i (u,v)＝[u i ,v i ] T
Wherein i is 0, 1,2,3, 4, 5. With reference to FIG. 2, (u, v) are ideal coordinate points of the reference camera image plane, (u i ,v i ) Is the coordinate point of the ith camera original image plane obtained through steps 210, 220 and 230.
Referring to step 140 of FIG. 1, a lookup table MAP is used to determine the coordinate mapping relationship for each camera in the heterogeneous multispectral camera system i The original multispectral image collected by each camera is processed
Transforming to the image plane of the reference camera to obtain all registered multispectral images
Wherein i is 0, 1,2,3, 4, 5.
More specifically, referring to fig. 4, the distance of the target scene collected by the heterogeneous multispectral camera system of the embodiment is Z 0 Taking 4 spectral bands, i.e. 450nm (corresponding to image 410), 550nm (corresponding to image 420), 650nm (corresponding to image 430), and 700nm (corresponding to image 440), as an example, 200M, the coordinate mapping relationship lookup table M constructed in step 130 is usedAP i Acts on the 4 band raw acquired images:
wherein u is more than 0 and less than 1600, v is more than 0 and less than 1300.
In turn corresponding to the raw unregistered images 410,420, 430,440 acquired by the image sensor,
the images 411, 421, 431 and 441 after the registration by the quick image registration method of the heterogeneous multi-camera imaging system are sequentially corresponded.
With reference to fig. 4, the multi-spectral-segment fusion result is used to more intuitively display the effect comparison after the misregistration and the registration, where the original misregistration fusion image 450 is a pseudo-color fusion result of three channels, i.e., 550nm (corresponding to image 420), 650nm (corresponding to image 430), and 700nm (corresponding to image 440), and the registered fusion image 451 is a pseudo-color fusion result of three channels, i.e., 550nm (corresponding to image 421), 650nm (corresponding to image 431), and 700nm (corresponding to image 441).
Therefore, the heterogeneous multispectral camera system of the embodiment realizes the registration task of the multispectral spectral image, and provides basic data support for the development of subsequent algorithms and applications. The rapid registration method is simple in calculation process and high in calculation efficiency, is easy to expand to various heterogeneous multi-camera imaging systems, and completes a real-time rapid registration task.
Claims (1)
1. A method for fast image registration for a heterogeneous multi-camera imaging system, comprising:
a pre-calibration process, comprising steps S1 and S2, wherein:
s1, determining internal parameters and distortion coefficients of each camera by using Zhangyingyou calibration algorithm according to a plurality of groups of clear checkerboard calibration board images of different positions and postures acquired by the heterogeneous multi-camera system;
s2, selecting a reference camera coordinate system, and sequentially determining the spatial position transformation relation of each camera coordinate system relative to the reference camera coordinate system by using a binocular vision stereo calibration algorithm;
the internal parameters and distortion coefficients of each camera determined in the pre-calibration process and the spatial position transformation relation of each determined camera coordinate system relative to the reference camera coordinate system are stored as constants;
when the fast image registration method of the heterogeneous multi-camera imaging system is performed, the constant stored in the pre-calibration process is firstly read, and the target distance is set, and then steps S3 and S4 are performed, wherein:
s3, solving the pixel-level coordinate transformation relation of each camera image plane relative to the reference camera image plane according to the camera internal parameters, distortion coefficients, the space position transformation relation among the camera coordinate systems and the set target distance of the heterogeneous multi-camera system, and constructing a corresponding coordinate mapping relation lookup table;
s4, converting the original image collected by each camera into the image plane of the reference camera according to the coordinate mapping relation lookup table of each camera in the heterogeneous multi-camera system, thereby realizing rapid image registration;
converting each camera into each image plane of the reference camera correspondingly to perform multi-spectral fusion display;
each camera of the heterogeneous multi-camera system is an imaging system consisting of an optical lens group and an image sensor, the optical lens group of each camera forms an optical lens group array, and the image sensor of each camera forms an image sensor array unit; when the optical lens group array selects the optical filter array, a heterogeneous multispectral camera system is formed, and the optical filter array is formed by narrow-band filters of different channels; the image sensor array unit is composed of image sensors of the same model;
internal parameter matrix C for each camera i Can be expressed as
Wherein f is x ＝f/d x And f y ＝f/d y Representing the focal length in pixels, f denotes the focal length of the camera, d x And d y For the size of a single pixel of the image sensor, c x And c y Representing camera principal point coordinates;
distortion coefficient matrix D for each camera i Can be expressed as [ k ] i1， k i2 ，k i3 ，p i1 ，p i2 ]Wherein [ k ] i1 ，k i2 ，k i3 ]Represents the radial distortion coefficient, [ p ] i1 ，p i2 ]Represents the tangential distortion coefficient;
the specific implementation process of the step S2 is as follows:
selecting a certain camera of the heterogeneous multi-camera system as a reference camera, and taking a coordinate system of the certain camera as a reference coordinate system;
sequentially calculating the spatial position transformation relation of each camera coordinate system relative to the reference camera coordinate system by using a binocular vision stereo calibration algorithm, wherein the spatial position transformation relation comprises a rotation matrix and R 0i And translation matrix T 0i Wherein subscripts "0" and "i" represent the reference camera and the ith camera, respectively;
the specific implementation process of the step S3 is as follows:
first, an ideal distortion-free point p in a reference camera image plane coordinate system is expressed according to the following formula 0 (u, v) projection onto the world coordinate System P 0 (X 0 ，Y 0 ，Z 0 )：
Wherein Z is 0 Indicates the set target distance, C 0 Denotes the reference matrix of the reference camera, the superscript "-1" denotes the inverse of the matrix, the superscript "T" denotes the transpose of the matrix, [ u, v]The value range is within the image resolution range;
next, the world coordinates P of the target point are calculated according to the following formula 0 (X 0 ，Y 0 ，Z 0 ) Physical seating of other individual cameras transformed to a heterogeneous multi-camera systemMarking the system to obtain a corresponding physical coordinate point P i (X i ，Y i ，Z i )：
[X i ，Y i ，Z i ，1] T ＝[R 0i |T 0i ][X 0 ，Y 0 ，Z 0 ，1] T
Wherein [ R ] 0i |T 0i ]Representing a transformation matrix between the reference camera coordinate system and the i-th camera coordinate system, R 0i Representing a rotation matrix, T 0i Representing a translation matrix;
then, the physical coordinate points P in each camera coordinate system are calculated according to the following formula i (X i ，Y i ，Z i ) Projecting the image plane coordinate system of the corresponding camera to obtain a pixel level coordinate p i (u i ，v i )：
[X′ i ，Y′ i ]＝[X i /Z i ，Y i /Z i ]
[u i ，v i ，1] T ＝C i [X″ i ，Y″ i ，1] T
Wherein the content of the first and second substances,
[X′ i ，Y′ i ]is P i Normalized coordinates of [ X "] i ，Y″ i ]Is [ X' i ，Y′ i ]Distorted physical coordinates of (a); [ k ] A i1 ，k i2 ，k i3 ，p i1 ，p i2 ]Represents the distortion parameter of the i-th camera, [ k ] i1 ，k i2 ，k i3 ]Represents a radial distortion parameter, [ p ] i1 ，p i2 ]Representing the tangential distortion parameter, C i An internal reference matrix representing the ith camera;
finally, each camera image plane is constructed relative to the reference cameraPixel-level coordinate transformation relations of image planes, i.e. coordinate mapping relation look-up tables MAP i It can be expressed as:
MAP i (u，v)＝[u i ，v i ] T ；
the step S4 of transforming the original image captured by each camera into the image plane of the reference camera may be expressed as:
wherein, the first and the second end of the pipe are connected with each other,
and
respectively representing the original image and the registered image acquired by the ith camera.
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