Blind convolutional motion fuzzy image restoration method

Abstract

The invention relates to a blind convolutional motion fuzzy image restoration method. The method comprises steps that S1, an observation image is acquired, derivative filtering of the observation image is carried out through utilizing a differential filter, a high frequency degeneration image is generated; S2, an intelligible image is updated; S3, a formula described as specifications is solved through utilizing an iteration least squares, and a point spread function is updated and solved; S4, if a dimension of the point spread function is greater than a set value, the dimension of the point diffusion function is enhanced, the process returns to the step S2, otherwise, iteration stops; and S5, according to the optimal point spread function acquired in the step S4 and the observation image, so a restoration image is solved through an original non-blind convolutional method. The method is advantaged in that the point spread function acquired through solution is stable, influence of noise on the restoration image is inhibited, and substantial fuzzy recovery effect, high automation degree, simple operation and rapid performing speed are realized.

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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