CN108062765A - Binocular image processing method, imaging device and electronic equipment - Google Patents
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Abstract
Imaging device and electronic equipment the present invention provides a kind of binocular image processing method and using the binocular image processing method.The binocular image processing method includes：The imaging unit being made of a pair of of Image Acquisition subelement gathers the first image and the second image；First image and the second image are sent to image processing unit and carry out Stereo matching, obtains the initial parallax of the first image and the second image；First image is divided into several pieces of subgraphs；The initial parallax of pixel in each subgraph is counted, determines to stablize pixel and unstable pixel；The parallax of unstable pixel is optimized, obtains final parallax.The present invention not only ensure that arithmetic speed, while improve the precision of Stereo matching disparity computation, have stronger practical application meaning.
Description
Technical Field
The invention relates to an image processing technology, in particular to a stereo matching method for image segmentation post-processing.
Background
With the continuous development of stereoscopic vision, especially the emergence of a double-camera mobile phone, the blurring of different depths of field like a single lens reflex camera can be realized easily. The principle of double-shot and human eyes is similar, when a person uses one eye, the distance of an object is difficult to accurately identify, namely, a single-shot mobile phone is difficult to obtain the scene depth, and when two eyes are used simultaneously, the brain reconstructs a real three-dimensional world by using images of two visual angles, thereby forming the most important information source, namely vision, of the human. The double-shot operation also works according to human eyes. For a certain scene in the real world, the two cameras are used for simultaneously acquiring and imaging data of the scene, so that a certain object point in the three-dimensional world is simultaneously recorded on the two images, namely two image points corresponding to the same object point. And one of the important steps of bi-shooting reconstruction of the three-dimensional world is stereo matching.
Stereo matching is to use the image points on the reference image (such as "left image") to find the corresponding matching points on the target image (such as "right image"). According to epipolar geometric knowledge, parallax can be obtained by using left and right matching points, and the depth of the same object point in the three-dimensional world corresponding to two image points can be calculated by combining a triangulation method. Therefore, the depth information of all object points in the common view field of the left image and the right image can be calculated. The stereo matching is the basis for obtaining the depth of an object, and the depth is the basis for realizing application such as background blurring, depth measurement, unmanned driving and the like. The accuracy of stereo matching also determines the accuracy of these applications.
However, the accuracy of stereo matching is affected by many factors, for example, when there is a large low texture in a scene or there is an occlusion of an object, the corresponding pixel points are called unstable points (unstable points), otherwise the pixel points are called stable points (stable points). Parallax calculation errors are easily caused for unstable point, resulting in depth errors. For these defects, various stereo matching methods also appear, such as methods of absolute error sum algorithm sad (sum of absolute differences), error sum of squares algorithm ssd (sum of Squared differences), normalized product correlation algorithm ncc (normalized Cross correlation), belief propagation bp (belief propagation), graph-cut (graph-cut), and the like. The methods have various advantages and disadvantages, such as simple SAD principle, but easily occurring matching error; compared with the former method, the graph-cut principle is complex, accuracy is high, but calculation amount is large, algorithm operation time is long, and the requirement for calculating depth in real time is difficult to achieve.
Disclosure of Invention
According to an embodiment of the present invention, there is provided a binocular image processing method for an electronic device including an imaging unit configured by a pair of image capturing subunits, the binocular image processing method including: an imaging unit composed of a pair of image acquisition subunits acquires a first image and a second image; sending the first image and the second image to an image processing unit for stereo matching to obtain initial parallax of the first image and the second image; dividing the first image into a number of block sub-images; counting the initial parallax of the pixel points in each sub-image, and determining stable pixel points and unstable pixel points; and optimizing the parallax of the unstable pixel points to obtain a final parallax image.
Further, according to the binocular image processing method provided by the embodiment of the invention, the stereo matching is calculated by adopting a global matching algorithm to obtain the initial parallax.
Further, according to the binocular image processing method of the embodiment of the invention, the global matching algorithm adopts a confidence coefficient propagation algorithm.
Further, according to the binocular image processing method of the embodiment of the present invention, the matching cost of the belief propagation algorithm includes, but is not limited to: the method comprises the steps of obtaining pixel points, neighborhood pixel point information of the pixel points and information transmitted to the neighborhood pixel points by the neighborhood pixel points.
Further, according to the binocular image processing method provided by the embodiment of the invention, the first image is subjected to image segmentation by adopting a K-means algorithm, and in the K-means algorithm, the cluster center with the closest distance is searched for each pixel point within a certain distance range.
Further, according to the binocular image processing method of the embodiment of the present invention, the finding of the calculation parameter of the cluster center closest to the distance includes: distance and color intensity.
Further, according to the binocular image processing method provided by the embodiment of the invention, the pixel points in each sub-image are judged through reliability measurement, and stable pixel points and unstable pixel points are determined.
Further, according to the binocular image processing method provided by the embodiment of the invention, the parallax of the unstable pixel point in each sub-image is optimized by using plane fitting.
According to another embodiment of the present invention, there is provided an electronic apparatus including: an imaging unit composed of a pair of image acquisition subunits, an image processing unit, and a memory. The imaging unit is used for acquiring a first image and a second image; the image processing unit is used for carrying out stereo matching on the first image and the second image to obtain the initial parallax of the first image and the second image; dividing the first image into a number of block sub-images; counting the initial parallax of the pixel points in each sub-image, and determining stable pixel points and unstable pixel points; optimizing the parallax of the unstable pixel points to obtain a final parallax image; and the memory is used for storing the program run by the image processing unit and the data required by the running program.
According to another embodiment of the present invention, there is provided a binocular image processing method for an imaging apparatus including an imaging unit constituted by a pair of image capturing subunits, the binocular image processing method including: an imaging unit composed of a pair of image acquisition subunits acquires a first image and a second image; sending the first image and the second image to an image processing unit for stereo matching to obtain initial parallax of the first image and the second image; dividing the first image into a number of block sub-images; counting the initial parallax of the pixel points in each sub-image, and determining stable pixel points and unstable pixel points; and optimizing the parallax of the unstable pixel points to obtain a final parallax image.
According to another embodiment of the present invention, there is provided an image forming apparatus including: an imaging unit composed of a pair of image acquisition subunits for acquiring a first image and a second image; the image processing unit is used for carrying out stereo matching on the first image and the second image to obtain the initial parallax of the first image and the second image; dividing the first image into a number of block sub-images; counting the initial parallax of the pixel points in each sub-image, and determining stable pixel points and unstable pixel points; and optimizing the parallax of the unstable pixel points to obtain a final parallax image.
According to the binocular image processing method, the imaging device and the electronic equipment using the binocular image processing method, not only is the operation speed guaranteed, but also the accuracy of stereo matching parallax calculation is improved, and the binocular image processing method has a strong practical application significance.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and are intended to provide further explanation of the claimed technology.
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FIG. 1 is a block diagram illustrating an electronic device in accordance with the present invention;
fig. 2 is a flowchart illustrating a binocular image processing method of the electronic device according to the present invention;
fig. 3 is a block diagram illustrating an imaging apparatus according to the present invention;
fig. 4 is a flowchart illustrating a binocular image processing method of the imaging apparatus according to the present invention.
Detailed Description
The present invention will be further explained by describing preferred embodiments of the present invention in detail with reference to the accompanying drawings.
First, an electronic apparatus according to an embodiment of the present invention will be described with reference to fig. 1, and the electronic apparatus of the present invention is preferably, for example: any one of a smart phone, a tablet computer, a digital camera, a notebook computer and the like, and other electronic equipment for realizing image acquisition by using double cameras.
Fig. 1 is a block diagram illustrating an electronic device according to an embodiment of the present invention. As shown in fig. 1, the electronic device 1 of the embodiment of the present invention has an imaging unit 11, an image processing unit 12, and a storage unit 13, and it is understood that only components closely related to the present invention are shown in fig. 1 for simplicity of description, and the electronic device 1 according to the embodiment of the present invention may further include other components such as a central processing unit, a communication unit, and an I/O unit.
Specifically, the imaging unit 11 includes a pair of image capturing subunits, in this embodiment, the pair of image capturing subunits is a pair of cameras 111 and 112, the cameras 111 and 112 shown in fig. 1 are arranged left and right, the arrangement is merely exemplary, and the cameras 111 and 112 may also be arranged up and down. In this embodiment, the cameras 111 and 112 are calibrated and each acquire an image of the physical world, and the obtained binocular image satisfies epipolar constraint, that is, corresponding pixel points are on the same line of the image, thereby saving a large amount of computation for a subsequent algorithm.
Specifically, the image processing unit 12 is configured to acquire a left eye image and a right eye image for the imaging unit 11 constituted by a pair of image acquisition subunits, i.e., the cameras 111 and 112. The image processing unit 12 may be configured by any of such as an image processing unit GPU, a digital signal processor DSP, an application specific integrated circuit ASIC.
Since the left eye head portrait is used as the reference image, in the present embodiment, the image processing unit 12 performs global matching, image segmentation, reliability measurement, plane fitting, and the like on the left eye image to obtain an accurate final disparity map.
Specifically, the storage unit 13 may be used to store programs processed and controlled in the image processing unit 12, and permanently or temporarily store image data and input or output data. The storage unit 13 may be a flash memory type storage medium, a hard disk type storage medium, a multimedia card micro storage medium, a random access memory, a read only memory, or the like. In addition, the electronic apparatus 1 may operate a network storage medium such as a cloud platform so as to perform the function of the storage unit 13 through network transmission. In one embodiment of the present invention, the image processing unit 12 stores the left eye image, the right eye image, and other relevant temporary data in the storage unit 13, and the image processing unit 12 reads various data from the storage unit 13.
As described above, in the electronic apparatus 1 according to the embodiment of the present invention, the final disparity map of the binocular view is obtained by performing one or more of global matching, image segmentation, reliability measurement, and plane fitting on the original image data obtained by the two cameras 111 and 112 arranged right and left such as cameras, so as to ensure the operation speed, improve the accuracy of the stereo matching disparity calculation, and have a strong practical significance.
The electronic apparatus 1 according to the embodiment of the present invention is described above with reference to fig. 1, and the binocular image processing method according to the embodiment of the present invention will be described below with reference to fig. 2.
Fig. 2 is a flowchart illustrating a binocular image processing method of an electronic device according to an embodiment of the present invention. As shown in fig. 2, the binocular image processing method of the electronic device according to the embodiment of the present invention includes the steps of:
in step S11, the left eye image and the right eye image are acquired by the cameras 111 and 112, and the left eye image and the right eye image are completely corrected; thereafter, the step processing proceeds to step S12.
In step S12, the left eye image and the right eye image are sent to the image processing unit 12 for stereo matching, and an initial parallax of the left eye image and the right eye image is obtained; specifically, in this embodiment, a global stereo matching algorithm is adopted: the Belief Propagation algorithm (Belief Propagation) performs disparity calculation:the formula is a matching cost (or matching error) cost calculation formula of any pixel point q, wherein Dq(fq) For the cost calculation of the pixel point q itself,and (5) calculating the matching cost transmitted to q by the adjacent pixel points of the time point q for the Tth iteration. Thereafter, the step processing proceeds to step S13.
In step S13, the left eye image is divided into a number of block sub-images; in particular, the left eye image is segmented by using K-means, and the K-means image segmentation is based on error flatSquare and minimization criterionWhere k is the number of blocks of the sub-picture, uiIs the central pixel point of the i-th block sub-image, ciIs any one sub-picture. According toAnd finding the central pixel point with the closest distance for each pixel point, and classifying the central pixel point into a corresponding cluster (cluster) to form k clusters. c. CiDenotes the ith cluster, uiAnd x is a central pixel point of the corresponding cluster and is an arbitrary point. In this embodiment, when the image is segmented by using K-means and the closest cluster center pixel point is found for each pixel point, the distance from any pixel point to all the cluster center pixel points is no longer calculated, and then judgment is performed, but the cluster center pixel points are found within a certain distance range, for example, the window size may be set to beN is the total number of pixels and K is the number of divided blocks. Further, when calculating the distance from the pixel point to each center, the distance and the color intensity are considered:wherein, thereafter, the step processing proceeds to step S14.
In step S14, the initial parallax of the pixel points in each sub-image is counted to determine stable pixel points and unstable pixel points; specifically, in this embodiment, a reliability measure (confidence measure) is used to determine whether a pixel point in the sub-image is a stable point or an unstable point, and the formula is as follows:wherein, C1And C2The minimum value and the second minimum value of the matching cost when the matching point is searched are respectively, when the ratio exceeds a preset threshold value, the pixel point can be judged as a stable point, otherwise, the pixel point is an unstable point. Thereafter, the step processing proceeds to step S15.
In step S15, the disparity of the unstable pixel is optimized to obtain a final disparity map. Specifically, in this embodiment, the disparity of the unstable point in each sub-image is optimized by using plane fitting (plane fitting), so as to obtain an accurate final disparity map.
The electronic device and the binocular image processing method thereof according to the embodiment of the present invention are described above with reference to fig. 1 to 2. Further, the present invention is also applicable to the imaging device 2.
As shown in fig. 3, the imaging apparatus 2 according to the embodiment of the present invention includes an imaging unit 21 and an image processing unit 22. Specifically, the imaging unit 21 is similar to the imaging unit 11 described with reference to fig. 1, and includes a pair of cameras 211 and 212, where the pair of cameras 211 and 212 are arranged left and right and are calibrated to acquire images of the physical world, respectively, and the obtained binocular images satisfy epipolar constraint, that is, corresponding pixel points are on the same line of the images, so as to save a large amount of computation for subsequent algorithms. The camera can be a color camera or a black and white camera. Specifically, the image processing unit 22 is configured to acquire a left eye image and a right eye image for the imaging unit 21 constituted by a pair of image acquisition subunits, i.e., the cameras 211 and 212. The image processing unit 22 may be configured by any of such as an image processing unit GPU, a digital signal processor DSP, an application specific integrated circuit ASIC.
Since the left eye head portrait is used as the reference image, in the present embodiment, the image processing unit 22 performs one or more of global matching, image segmentation, reliability measurement, plane fitting, and the like on the left eye image to obtain an accurate final disparity map.
As described above, in the imaging apparatus 2 according to the embodiment of the present invention, one or more of global matching, image segmentation, reliability measurement, plane fitting, and the like are performed on the raw image data acquired by the two cameras 211 and 212 arranged right and left such as cameras, so as to obtain an accurate final disparity map, thereby ensuring the operation speed, and improving the accuracy of the stereo matching disparity calculation, which has a strong practical significance.
As shown in fig. 4, the binocular image processing method of the imaging apparatus according to the embodiment of the present invention includes the steps of:
s21: the left eye image and the right eye image are acquired by the cameras 211 and 212 and are completely corrected;
s22: sending the left eye image and the right eye image to the image processing unit 22 for stereo matching to obtain an initial parallax of the left eye image and the right eye image;
s23: dividing the left eye image into a plurality of sub-images;
s24: counting the initial parallax of the pixel points in each sub-image, and determining stable pixel points and unstable pixel points;
s25: and optimizing the parallax of the unstable pixel points to obtain a final parallax image.
The specific image processing method is consistent with the binocular image processing method used for the electronic device according to the embodiment of the present invention, and details are not repeated here.
In the above, with reference to fig. 1 to 4, the image processing method, and the imaging device and the electronic device using the image processing method according to the embodiments of the present invention are described, by processing original image data acquired by, for example, two cameras, not only the operation speed is ensured, but also the accuracy of stereo matching parallax calculation is improved, and the method has a strong practical application meaning.
It should be noted that, in the present specification, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Finally, it should be noted that the series of processes described above includes not only processes performed in time series in the order described herein, but also processes performed in parallel or individually, rather than in time series.
Through the above description of the embodiments, those skilled in the art will clearly understand that the present invention may be implemented by software plus a necessary hardware platform, and may also be implemented by hardware entirely. With this understanding in mind, all or part of the technical solutions of the present invention that contribute to the background can be embodied in the form of a software product, which can be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., and includes instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods according to the embodiments or some parts of the embodiments.
Claims (11)
1. A binocular image processing method for an electronic apparatus including an imaging unit composed of a pair of image pickup sub-units, characterized in that:
an imaging unit formed by the pair of image acquisition subunits acquires a first image and a second image;
sending the first image and the second image to an image processing unit for stereo matching to obtain initial parallax of the first image and the second image;
partitioning the first image into a number of block sub-images;
counting the initial parallax of the pixel points in each sub-image, and determining stable pixel points and unstable pixel points;
and optimizing the parallax of the unstable pixel points to obtain a final parallax image.
2. The binocular image processing method of claim 1, wherein the stereo matching is calculated using a global matching algorithm to obtain an initial disparity.
3. The binocular image processing method of claim 2, wherein the global matching algorithm employs a belief propagation algorithm.
4. The binocular image processing method of claim 3, wherein the matching cost of the belief propagation algorithm includes, but is not limited to: the method comprises the steps of obtaining pixel points, neighborhood pixel point information of the pixel points and information transmitted to the neighborhood pixel points by the neighborhood pixel points.
5. The binocular image processing method of claim 1 or 2, wherein the first image is image-segmented using a K-means algorithm in which a cluster center closest to each pixel point is found within a certain distance range.
6. The binocular image processing method of claim 5, wherein the finding of the calculation parameter of the cluster center closest to the distance comprises: distance and color intensity.
7. The binocular image processing method of claim 1, wherein the determination of the pixels within each subimage is made by confidence measure to determine stable pixels and non-stable pixels.
8. The binocular image processing method of claim 1 or 7, wherein the disparity of the non-stationary pixels in each sub-image is optimized using plane fitting.
9. An electronic apparatus including an imaging unit constituted by a pair of image acquisition subunits, an image processing unit, and a memory,
the imaging unit is used for acquiring a first image and a second image;
the image processing unit is used for carrying out stereo matching on the first image and the second image to obtain the initial parallax of the first image and the second image; partitioning the first image into a number of block sub-images; counting the initial parallax of the pixel points in each sub-image, and determining stable pixel points and unstable pixel points; optimizing the parallax of the unstable pixel points to obtain a final parallax image;
the memory is used for storing the program run by the image processing unit and the data required by the running program.
10. A binocular image processing method for an imaging apparatus including an imaging unit composed of a pair of image pickup sub-units, the binocular image processing method comprising:
an imaging unit formed by the pair of image acquisition subunits acquires a first image and a second image;
sending the first image and the second image to an image processing unit for stereo matching to obtain initial parallax of the first image and the second image;
partitioning the first image into a number of block sub-images;
counting the initial parallax of the pixel points in each sub-image, and determining stable pixel points and unstable pixel points;
and optimizing the parallax of the unstable pixel points to obtain a final parallax image.
11. An image forming apparatus, comprising:
an imaging unit composed of a pair of image acquisition subunits for acquiring a first image and a second image;
the image processing unit is used for carrying out stereo matching on the first image and the second image to obtain the initial parallax of the first image and the second image; partitioning the first image into a number of block sub-images; counting the initial parallax of the pixel points in each sub-image, and determining stable pixel points and unstable pixel points; and optimizing the parallax of the unstable pixel points to obtain a final parallax image.
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Application publication date: 20180522 |