CN111179333A - Defocus fuzzy kernel estimation method based on binocular stereo vision - Google Patents
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Abstract
The invention relates to a defocus blur kernel estimation method based on binocular stereo vision. The invention comprises the following steps: calculating an initial fuzzy core; obtaining equivalent fuzzy images; and (3) stereo matching calculation: integrating the gray information and the fuzzy kernel information as data items of an energy function of a global matching method, and performing stereo matching on the equivalent fuzzy images by optimizing the energy function by using the global matching method to obtain a dense disparity map; and (3) parallax post-processing: carrying out weighted mean filtering processing on the dense disparity map; and (3) calculating a final fuzzy core: and taking the disparity map subjected to disparity postprocessing as an initial disparity map, and performing blur kernel calculation again to obtain a final blur kernel. The method fully considers the relation between parallax and the fuzzy kernel in the binocular stereo vision, improves the accuracy of fuzzy kernel calculation by using the binocular image, and is suitable for the defocusing fuzzy kernel estimation problem based on the binocular stereo vision.
Description
Technical Field
The invention relates to a defocus blur kernel estimation method, in particular to a defocus blur kernel estimation problem based on binocular stereo vision.
Background
In the image acquisition process, the image degradation can occur to different degrees due to the imperfection of the imaging system and the interference of the external environment. Defocus blur is a common form of blur and refers to image blur due to depth of field reasons or focus errors of the imaging system. In addition, the image is affected by atmospheric turbulence in the case of long-distance imaging, which also results in the generation of defocus blur. Due to the reasons, clear images are difficult to acquire by an imaging system, defocusing fuzzy degradation of the binocular images at different degrees exists, and the precision of three-dimensional reconstruction is reduced. Whether the deblurring algorithm or the stereo matching algorithm aiming at the blurred image is adopted, the estimation of the blur kernel is always a key problem in the field of blurred images.
The existing fuzzy kernel estimation method mostly uses a single image or an image sequence shot from a single visual angle to perform calculation, calculates a fuzzy kernel corresponding to a pixel in an edge area by extracting edge texture information of the image, and then calculates a fuzzy kernel map of a whole image by a propagation algorithm. On the basis, Tang et al propose a method for calculating a blur kernel according to spectral contrast, and since defocus blur affects the spectral amplitude of an object edge region, a blur kernel map can be calculated according to the relationship between the blur degree of a secondary blurred image and a spectrum[2]. The method is only suitable for defocusing fuzzy kernel estimation of the monocular image, and can not effectively utilize more information provided by the binocular image to carry out fuzzy kernel estimation.
Disclosure of Invention
The invention provides a defocus blur kernel estimation method based on binocular stereo vision for overcoming the defects of the prior art and aiming at the defocus blur kernel estimation problem of a binocular degraded image.
The technical scheme of the invention is described in detail as follows:
the defocus blur kernel estimation method based on binocular stereo vision comprises the following five steps:
step one, calculating an initial fuzzy core. Performing stereo matching calculation on the binocular image to obtain an initial disparity map, and calculating an initial blur kernel of the binocular image by using disparity values corresponding to the edge pixels;
and step two, obtaining the equivalent blurred image. Calculating a relative fuzzy core according to the initial fuzzy core of the binocular image, and performing self-adaptive Gaussian filtering on the binocular image to obtain an equivalent fuzzy image;
and step three, calculating stereo matching. And integrating the gray information and the fuzzy kernel information as data items of an energy function of the global matching method. Performing stereo matching on the equivalent blurred images by using a global matching method through an optimized energy function to obtain a dense disparity map;
and step four, performing parallax post-processing. Carrying out weighted mean filtering processing on the dense disparity map obtained in the step;
and step five, finally calculating the fuzzy core. And taking the disparity map subjected to disparity postprocessing as an initial disparity map, and performing blur kernel calculation again to obtain a final blur kernel.
The initial fuzzy core calculating step specifically comprises the following steps:
calculating the average parallax value d of edge pixels in the binocular images by using a K-means clustering algorithm according to the initial parallax map of the binocular imageskWherein k is 1,2 represents the corresponding information of the left and right images respectively, according to the camera calibration parameter and the average parallax dkCalculating initial fuzzy kernel sigma corresponding to each pixel of binocular imagek。
The step of obtaining the equivalent blurred image specifically comprises the following steps:
calculating relative fuzzy core according to initial fuzzy core of each pixel in binocular image
In the formula sigma1(p)、σ2(q) represents initial blur kernels for arbitrary pixels p for the left and right images, respectively. According to the relative blur kernel delta sigma (p), Gaussian filtering is carried out on the region with smaller blur kernel in the corresponding region of the binocular image to obtain the same blur image, and the relative blur kernel delta sigma (p) is selected as the region of the Gaussian function filtering processAnd standard deviation parameters are adopted, so that equivalent blurred binocular images with similar blurring degrees of corresponding areas are obtained.
The global matching method specifically comprises the following steps:
the global matching method is a basic method in stereo matching, and stereo matching calculation is carried out by optimizing an energy function. The energy function of the global matching method consists of a data item and a smoothing item, the weighted sum of the relative blurring kernel information and the pixel gray scale information of the binocular image is used as the data item of the energy function, and the pixel gray scale information is used as the smoothing item of the energy function. And constructing a corresponding graph model according to the equivalent fuzzy binocular image and the energy function, processing the parallax value as a label, and selecting an optimal label for each pixel by calculating the minimum value of the global energy function so as to obtain a dense parallax graph.
The weighted mean filtering specifically comprises:
and selecting a support window from the disparity map, taking the difference of the fuzzy cores corresponding to each pixel and the central pixel in the window as a weight, and taking the weighted average of the disparities of all pixels in the window as the disparity value of the central pixel to obtain the final disparity map.
The invention has the following beneficial effects:
1. the method fully considers the relation between the fuzzy kernel and the parallax, and improves the accuracy of fuzzy kernel calculation through the parallax constraint of the binocular stereo vision. The method provided by the invention has clear principle and more accurate and reliable calculation result, and can provide a new idea for calculating the defocusing fuzzy core in the binocular stereo vision.
2. In the process of calculating the fuzzy core, the method uses the K-means algorithm and the weighted mean filtering post-processing algorithm, can select more accurate supporting points, neglects the interference of partial irrelevant points and improves the accuracy of calculating the fuzzy core.
Drawings
FIG. 1 is a flow chart of a defocus blur kernel estimation method based on binocular stereo vision according to the present invention;
FIG. 2 is a defocused binocular image used in an example of the present invention, wherein (a) is a left view and (b) is a right view;
fig. 3 shows the output results of the present invention, wherein (a), (b) are the left and right views of the equivalent blurred image obtained in step 2, and (c) is the defocus map obtained in the present invention.
Detailed Description
In order to make the technical scheme of the present invention clearer, the following will further describe a specific implementation method of the present invention with reference to the accompanying drawings.
The following explains a specific processing procedure of the defocus blur kernel estimation method based on binocular stereo vision, taking the defocus binocular images shown in fig. 2(a) and fig. 2(b) as an example:
1. an initial fuzzy core calculation step:
defocused binocular images shown in fig. 2(a) and 2(b) are respectively denoted as I1、I2Calculating a disparity map P of the binocular image by using a local matching algorithm based on SAD0Extracting edge pixels of the binocular image by using a Canny edge detection algorithm, and combining disparity values of the edge pixels into a set psikWhere k is 1 and 2 represents the corresponding information of the left and right images, respectively. Computing a set of edge pixels psi using a K-means clustering algorithmkAverage disparity value d ofkFurther calculating the initial fuzzy core corresponding to each pixel of the binocular image
And composing the initial fuzzy core map. In the formula, DkDenotes the diameter of the entrance pupil of the imaging system, B denotes the system baseline, d denotes the parallax value of the pixel in the initial parallax map, dkSetting a parameter D for the parallax value of the edge pixel according to the calibration information of the image1＝D2＝5.6,B＝3080.117。
2. Obtaining an equivalent blurred image:
calculating relative fuzzy core according to the initial fuzzy core of each pixel obtained in the above steps
In the formula sigma1(p)、σ2(q) shows the left and right diagrams respectivelyLike a blur kernel for the corresponding pixel. According to the relative fuzzy core, performing Gaussian filtering G on the area with smaller fuzzy core in the corresponding area of the binocular imageΔσObtaining an equivalent blurred image:
in the formula I1(p)、I2(p) respectively represents corresponding pixels of the left and right original images,
respectively representing corresponding pixels of equally blurred images, GΔσRepresents a gaussian point spread function with a standard deviation of delta sigma,
representing a convolution operation. For binocular images I as shown in fig. 3(a) and 3(b), respectively1、I2Carrying out adaptive fuzzy compensation to obtain equivalent fuzzy image
3. And stereo matching calculation:
using global matching method to optimize energy function to obtain equivalent fuzzy image
Performing stereo matching experiment to obtain dense parallax image P1。
In the global matching algorithm, a corresponding graph model can be constructed according to the image and energy function, and the parallax value is used as a label for processing. The network graph G is composed of a node set V and an edge set E, and a partition may partition the network graph G to include a source node s0Set S of (2) and containing sink node t0Set T of (2), capacity of edge to be cut into setAnd the sum is called the cut capacity, the minimum cut capacity is obtained by a method of calculating the maximum flow of the network, so that a global energy function is optimized, and an optimal label is selected for each pixel to obtain a dense disparity map.
The energy function of the global matching algorithm consists of a data item and a smooth item, and the fuzzy kernel information and the pixel gray level information of the binocular image are combined to be used as a data item E of the energy functiondata(f) Smoothing term E using pixel gray scale information as an energy functionsmoothness(f) Obtaining a disparity map P by calculating the minimum value of the energy function1。
Data item Edata(f) Can be defined as:
Edata(f)＝∑D(a)
where α is the scaling factor for adjusting the weight and T is the threshold for the data item.
Smoothing term Esmoothness(f) Can be defined as:
in the formula, λ is a grayscale threshold parameter.
in this embodiment, the algorithm parameters α is 0.15, T is 30, and λ is 5.
4. And (3) parallax post-processing:
using weighted mean filtering parallax post-processing method to obtain the parallax map P1And (6) carrying out optimization treatment. In the disparity map P1Selecting a window, and taking the difference of the fuzzy cores corresponding to each pixel and the central pixel in the window as a weight omegapq＝exp[-(σp-σq)2]Weighted average of all pixel disparities within a window
Obtaining a disparity map P as the disparity value of the central pixel2. Wherein σp、σqRespectively representing a central pixel and a support pixel, N (p) representing the support window of the central pixel p, dqRepresenting the disparity value of pixel q within the support window.
5. And a final fuzzy core calculation step:
a disparity map P obtained by post-processing the disparity2As an initial disparity map, the blur kernel calculation step in step 1 is repeated, the blur kernel is recalculated as a final blur kernel, and the obtained blur kernel map is shown in fig. 3 (c).
The present invention is described with reference to the following examples, which are provided as preferred embodiments to describe the steps of the present invention and not to limit the scope of the present invention. All equivalent changes made within the principles of the invention are intended to be included within the scope thereof.
Claims (1)
1. A defocus blur kernel estimation method based on binocular stereo vision is characterized by comprising the following five steps:
step one, calculating an initial fuzzy core: performing stereo matching calculation on the binocular image to obtain an initial disparity map, and calculating an initial blur kernel of the binocular image by using disparity values corresponding to the edge pixels;
step two, obtaining the equivalent blurred image: calculating a relative fuzzy core according to the initial fuzzy core of the binocular image, and performing self-adaptive Gaussian filtering on the binocular image to obtain an equivalent fuzzy image;
step three, stereo matching calculation: integrating the gray information and the fuzzy kernel information as data items of an energy function of a global matching method, and performing stereo matching on the equivalent fuzzy images by optimizing the energy function by using the global matching method to obtain a dense disparity map;
step four, parallax post-processing: carrying out weighted mean filtering processing on the dense disparity map obtained in the step;
step five, calculating a final fuzzy core: taking the disparity map subjected to disparity postprocessing as an initial disparity map, and performing fuzzy kernel calculation again to obtain a final fuzzy kernel;
the calculating of the initial blur kernel of the binocular image specifically comprises the following steps:
calculating the average parallax value d of edge pixels in the binocular images by using a K-means clustering algorithm according to the initial parallax map of the binocular imageskWherein k is 1,2 represents the corresponding information of the left and right images respectively, according to the camera calibration parameter and the average parallax dkCalculating initial fuzzy kernel sigma corresponding to each pixel of binocular imagek；
The step of obtaining the equivalent blurred image specifically comprises the following steps:
calculating relative fuzzy core according to initial fuzzy core of each pixel in binocular image
In the formula sigma1(p)、σ2(q) respectively representing initial fuzzy kernels of the left image and the right image corresponding to any pixel p, carrying out Gaussian filtering on a region with a smaller fuzzy kernel in a corresponding region of the binocular image according to a relative fuzzy kernel delta sigma (p) to obtain an equivalent fuzzy image, and selecting the relative fuzzy kernel delta sigma (p) as a standard deviation parameter of a Gaussian function filtering process to obtain the equivalent fuzzy binocular image with approximately the same fuzzy degree of the corresponding region;
the global matching method specifically comprises the following steps:
performing stereo matching calculation by optimizing an energy function, wherein the energy function of the global matching method consists of a data item and a smooth item, the weighted sum of relative fuzzy kernel information and pixel gray scale information of a binocular image is used as the data item of the energy function, the pixel gray scale information is used as the smooth item of the energy function, a corresponding image model is constructed according to the equivalent fuzzy binocular image and the energy function, a parallax value is used as a label for processing, and an optimal label is selected for each pixel by calculating the minimum value of the global energy function, so that a dense parallax image is obtained;
the weighted mean filtering process specifically includes:
and selecting a support window from the disparity map, taking the difference of the fuzzy cores corresponding to each pixel and the central pixel in the window as a weight, and taking the weighted average of the disparities of all pixels in the window as the disparity value of the central pixel to obtain the final disparity map.
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| CN113965742A (en) * | 2021-02-28 | 2022-01-21 | 北京中科慧眼科技有限公司 | Dense disparity map extraction method and system based on multi-sensor fusion and intelligent terminal |
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