A Blind Convolution Motion Blur Image Restoration Method

Abstract

The present invention is a blind convolution motion blur image restoration method. The method includes the following steps: S1: Collect the observation image, use a differential filter to perform derivative filtering on the observation image, and generate a high-frequency degraded image; S2: Update the clear image; S3: Use the iterative least squares method to solve the following equation to update and solve the point spread Function; S4: If the size of the point spread function is less than the set value, increase the size of the point spread function and return to S2. Otherwise, abort the iteration. S5: According to the optimal point spread function and the observation image obtained in S4, the restored image is obtained by solving the existing non-blind convolution method. The point spread function solved by the present invention is stable, thereby suppressing the impact of noise on the restored image, making the algorithm's fuzzy restoration effect remarkable, highly automated, simple to operate, and fast to execute.

Description

technical field [0001] The invention relates to signal processing technology in images, in particular to a blind convolution motion blurred image restoration method. Background technique [0002] When a video camera is used to capture images of a scene, if there is relative motion between the scene and the camera during the exposure of the camera, the resulting photos tend to be blurred. This kind of image blurring is called motion blur, and solving the problem of image restoration by establishing a restoration model of moving images is the main means of researching and solving motion blur. If the influence of noise is considered, the degradation model of motion blurred image can be described as a degradation function and an additive noise term n(x,y), processing an input image f(x,y) produces a degraded image g(x,y). [0003] [0004] Among them, h(x,y) is called the blur kernel or point spread function, Represents convolution, f(x,y) represents the original clear im...

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Problems solved by technology

[0011] However, there are some serious defects in the Richardson-Lucy algorithm method. The first problem is the noise amplification problem, which is also a common problem of this type of method (using the maximum likelihood solution).

The second problem is how many iterations are appropriate for different parts of the restored image

The calculation time of the Richardson-Lucy algorithm also increases significantly with the increase of the number of iterations, which is not conducive to the real-time recovery of motion blur

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