Partitioned bilateral total-variation regularization image noise elimination method

Abstract

The invention relates to a partitioned bilateral total-variation regularization image noise elimination method. The method comprises the steps that (1) a pollution image X<0> is acquired and is used to initialize a denoised image subjected to the first iteration, and then the step (2) is entered; (2) a partitioned bilateral structure similar distance matrix DW<t> of a denoised image subjected to the t(th) iteration is calculated, and then the step (3) is entered; (3) partitioned bilateral total-variation regular terms of the denoised image subjected to the t(th) iteration are constructed, and then the step (4) is entered; (4) an energy functional E<t> composed of fidelity terms and the partitioned bilateral total-variation regular terms is constructed, and the step (5) is entered; (5) a steepest descent method is adopted to solve a minimization problem of the energy functional E<t>, a denoised image subjected to the (t+1)(th) iteration is obtained, and the step (6) is entered; and (6) whether the number of the iterations is smaller than the maximum number N of iterations is judged, if the number of the iterations is smaller than the maximum number N of iterations, t is made to be equal to t+1, and the step (2) is entered, and otherwise the denoised image subjected to the (t+1)(th) iteration is output to end the operation.

Description

Technical field [0001] The invention belongs to the field of digital image processing, and specifically relates to a method for eliminating block-wise bilateral total variation and regularization of image noise, which can be used in the preprocessing process of digital images. Background technique [0002] In the process of acquiring and transmitting digital images, due to the interference of the circuit itself and external noise sources, degradation will inevitably occur, which will seriously affect the subsequent processing of feature extraction and analysis. [0003] Image denoising needs to balance the suppression of noise and the preservation of the original information of the image. In view of the problem of image denoising, researchers in this field have conducted a lot of exploration and research, and proposed a large number of noise suppression methods based on digital signal processing technology. [0004] The existing image noise suppression methods mainly include spatial...

AI Technical Summary

Problems solved by technology

However, in this method, the filtering weight only involves the spatial distance similarity, but ignores the brightness distance similarity, and the filtering weight is still determined by the brightness value of a single pixel. In terms of maintaining the local structural characteristics of the original image, it needs to be improved.

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