Non-local-restriction-based total variation image deblurring method

Abstract

The invention discloses a non-local-restriction-based total variation image deblurring method, which mainly solves the problems that the edge of an image cannot be sharpened and the high-frequency details of the image cannot be recovered in the process of deblurring the image in the prior art. The method comprises the following steps of: (1) initiating a deblurring result image by a Wiener filtering method; (2) calculating the non-local weight coefficient matrix of the deblurring result image; (3) updating the deblurring result image by using a non-local-restriction-based threshold value iterative formula; (4) performing noise suppression on the deblurring result image by a total-variation-model-based denoising method; and (5) judging whether a stopping condition is met or not, if the stopping condition is met, acquiring a final deblurring result image, and if the stopping condition is not met, returning to the step (2) until the stopping condition is met. In the process of deblurring the image by the method, the edge of the image can be sharpened, and the high-frequency details of the image can be recovered; and the method can be used for deblurring various known blurred types of blurred images.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a method for deblurring blurred images, which can be used for deblurring blurred images of various known blurring types. Background technique [0002] Image deblurring refers to removing or alleviating the phenomenon of image quality degradation in the process of acquiring digital images, which is an important and challenging research content in image processing. For the image deblurring problem, researchers have proposed many methods. [0003] The traditional deblurring methods include inverse filtering, Wiener filtering, Kalman filtering and generalized inverse singular value decomposition, etc. These methods have been widely used in image deblurring, but these methods require blurred images with high information quality. Noise ratio, methods such as inverse filtering are only suitable for images with high signal-to-noise ratio, which limits the practical application ...

AI Technical Summary

Problems solved by technology

The convergence speed of this method is higher than that of the general threshold iteration method. At the same time, in their code example, J.Bioucas-Dias et al. converted the noise coefficient into the fully variable domain for suppression, and removed the ringing effect. However, this method tends to produce a staircase effect in the smooth area of ​​the image, and cannot restore the high-frequency details of the image very well.

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