Split Bregman weight iterative image blind restoration method based on non-convex high-order total variation model

Abstract

The invention provides a Split Bregman weight iteration image blind restoration method based on a non-convex higher-order total variation model, and belongs to the technical field of image processing. The method is characterized in that firstly, a non-convex higher-order total variation regularization blind restoration cost function is obtained by introducing image border sparse prior information meeting a hyper-Laplacian model and by combining a high-order filter bank capable of generating piecewise linear solutions; secondly, a weight iteration strategy is provided, a minimization problem of the non-convex higher-order total variation regularization blind restoration cost function is converted into a minimization problem of an approximate convexity cost function with the updated weight; thirdly, the minimization problem of the approximate convexity cost function with the updated weight is converted into a new constraint solving problem through an operator split technology, and the constraint solving problem is converted into a split cost function through the method of adding a penalty term; fourthly, the split cost function is solved through a Split Bregman iteration solving frame. According to the Split Bregman weight iteration image blind restoration method based on the non-convex higher-order total variation model, an image can be restored effectively and rapidly, the shortage that a staircase effect is generated in a traditional total variation regularization blind restoration method is overcome, and meanwhile a better restoration effect on manually degraded images and actually degraded images is achieved.

Description

technical field [0001] The invention belongs to the technical field of image processing. Background technique [0002] Image is one of the most important sources of information for people. However, in the process of image acquisition and transmission, due to the interference of various factors, the image will be degraded and degraded. The degradation of the image will cause a large amount of real information to be lost, which will not only reduce the scientific value of the image, but also bring huge economic losses. Therefore, we need to use image restoration technology to restore the original appearance from the degraded image. At present, image restoration technology has been applied to many fields of science and technology, such as astronomical observation, medical imaging, multimedia, criminal investigation, etc. Many image restoration methods require more prior information, or have disadvantages such as poor effect and high algorithm complexity. So far, developing a...

AI Technical Summary

Problems solved by technology

However, the traditional total variation method will produce a patch constant effect when restoring the image, which is not suitable for the subjective observation of the human eye.

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