Single image blind deblurring method based on MAP method

A blind deblurring, single-image technology, applied in image enhancement, image data processing, instruments, etc., can solve problems such as large limitations and weak generalization ability of related algorithms, and achieve the effect of rapid convergence

Inactive Publication Date: 2019-06-07
KUNMING UNIV OF SCI & TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The problem to be solved by the present invention is that there are some deficiencies in the current correlation blind single image restoration method, for example, the correlation algorithm has weak generalization ability and particularly large limitations, and cannot be well used in other scenarios

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  • Single image blind deblurring method based on MAP method
  • Single image blind deblurring method based on MAP method
  • Single image blind deblurring method based on MAP method

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Embodiment 1

[0037] Example 1: Such as Figure 1-4 As shown, the experimental data of this embodiment includes 23 blurred images, in which there are low-illuminance images, human faces and natural images, and the blur kernels are all generated by the same method. The parameters are set as follows: α = 0.005, β = 0.0005, γ = 0.004, μ max = 2 3 , And ω max = 2 2 . Take one of the images as an example.

[0038] Specific steps are as follows:

[0039] Step1: First convert the image to be restored into a grayscale image, and then convert the obtained grayscale image into a double type image y to facilitate subsequent calculations.

[0040] Step2: Initialize the relevant parameters (k, x).

[0041] Step3: Solve u, g, d by x.

[0042] Step3.1 Substitute x into the equation: , And initialize μ←2α (assignment operation is only performed when initial value is assigned) to get the value of u.

[0043] Step3.1 Substitute x into the equation: And initialize (Assignment operation is only performed when init...

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Abstract

The invention relates to a single image blind deblurring method based on an MAP method, and belongs to the field of digital image restoration. The method comprises the following steps: converting a color image into a grayscale image; adding the priori knowledge such as dark channel, intensity and gradient priori to an objective function; solving a non-convex problem in the objective function by adopting a semi-quadratic separation method; and for the obtained blurred kernel, restoring a clear image by adopting a non-blind method. According to the method, the blind deblurring problem on the natural image is effectively solved by combining dark channel priori knowledge, intensity priori knowledge and gradient priori knowledge, and for the natural image, the dark channel priori knowledge hasa very good sparse effect. And for some edges of the natural image, the intensity priori can well retain the edges. Gradient prior can well restrain the artifact problem in the deblurring process. Therefore, the natural image can be well restored by combining the priori so as to obtain a better clear image.

Description

Technical field [0001] The invention relates to a blind deblurring method for a single image based on a MAP method, which belongs to the field of digital image restoration. Background technique [0002] Blind image deblurring is a classic problem in the field of image processing and computer vision. Its goal is to restore the hidden image in the blurred image. When the fuzzy shape satisfies the spatial invariance, the fuzzy process can be modeled in the following ways: [0003] y=x*k+n (15) [0004] Where * represents the convolution operator, y, x, k and n represent blurred images, clean images, blur kernels and noise, respectively. The problem in the above formula is ill-posed, because the kernel k is all unknown and there are infinitely many solutions. In order to solve this problem, additional constraints and prior knowledge about the blur kernel and the image are necessary. [0005] The success of recent deblurring methods mainly comes from the research progress of effective i...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00
Inventor 许煜刘辉尚振宏李润鑫罗静
Owner KUNMING UNIV OF SCI & TECH
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