Image multistep residual feedback iterative filtering method based on fractional order difference weighting

Abstract

The invention discloses an image multistep residual feedback iterative filtering method based on fractional order difference weighting. In the filtering method, a fractional order singularity index calculating unit, a fractional order weight matrix calculating unit and a multistep residual feedback filtering unit are adopted. The method comprises the following steps of: firstly, estimating a fractional order singularity index which corresponds to each pixel point; secondly, generating a fractional order weighting coefficient matrix according to the coefficient calculating way of a fractional order difference format; and lastly, performing multistep residual feedback filtering iteration, updating and generating a middle image to be denoised by using a fractional order weighting combination of a plurality of denoised residual images, performing total variation filtering on the middle image to be denoised to generate an iteration denoised image sequence, and iteratively converging the image into a finial denoised image. Due to the adoption of the method, an iteration sequence can be rapidly converged into a denoised image with a high peak signal noise ratio, the sensitivity and dependency degree of an iteration result on an iteration terminating condition are low, and detailed information such as the textures of images and the like can be well kept while image noise is effectively restrained.

Description

technical field [0001] The invention relates to a filtering technology for image noise suppression in the field of image processing, in particular to an image multi-step residual feedback iterative filtering method based on fractional difference weighting. Background technique [0002] In the process of image acquisition, transmission and display, noise pollution will inevitably be generated. Noise suppression is the preprocessing process of many image subsequent processes such as image segmentation, recognition and target detection. In some application fields, such as in medical and remote sensing image processing, the detection of strong and weak image edges and texture analysis are important image analysis methods, so it is very important to effectively maintain the structure of image edges and textures while suppressing noise . [0003] At present, there have been many researches on image detail preservation in the process of suppressing noise in the world. In 2...

AI Technical Summary

Problems solved by technology

In 2004, the fast projection iterative algorithm for total variation filtering proposed by Chamolle (Chambolle A. An algorithm for total variation minimization and applications. Journal of Mathematical Imaging and Vision, 2004, 20(1): 89-97) has a small amount of calculation, The convergence speed is fast, and the edge of the image can be well maintained, but it is prone to edge step effect, and the detail information such as texture is seriously lost.

However, because the method converges very fast, the iterative result is very sensitive to the selection of the iteration termination step, which greatly affects the stability of the method in practical applications.

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