Weight-adaptive mixed-order total variation image denoising algorithm

Abstract

The invention discloses a weight-adaptive mixed-order total variation image denoising algorithm, which comprises the following steps of: preprocessing an image, converting a color image into a grayscale image, and adding Gaussian white noise to the image; constructing an image denoising model by taking the total variation model as a basic framework, and solving minimization under a constraint model; putting forward a high-order total variation model, fusing the high-order total variation model into an image denoising model, dividing an edge texture region and a flat region by the whole algorithm model through the structural information of a noisy image, constructing a weight function, combining the total variation model and the high-order total variation model, establishing a mixed-order total variation image denoising model, and obtaining a minimizedsolution. A gradient constraint term is proposed, a denoising model is introduced, the structural information of the image is ensured, a final weight adaptive mixed-order total variation image denoising model is established, and the solution is minimized. Compared with a traditional algorithm model, the algorithm provided by the invention has the advantages that the peak signal-to-noise ratio is improved by 8-13dB, and the numerical value of the structural similarity is superior to that of the previous algorithm.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a weight self-adaptive mixed order total variation image denoising algorithm. Background technique [0002] Today, with the high-quality development of modern industries and digital multimedia technology, images and videos have long become an indispensable part of people's lives, and image processing has become one of the most popular research fields today. As an indispensable part of the image processing field, image denoising has unique application value and broad development prospects in many aspects such as medical care, restoration of cultural relics, artificial intelligence and military industry. [0003] Many scholars at home and abroad have conducted in-depth research on image denoising, among which the effective denoising models are mainly image denoising methods based on partial differential equations (Partial Differential Equations, PDE) and non-local means ba...

AI Technical Summary

Problems solved by technology

Kumar et al. proposed an adaptive full variation regulator based on geometric moments, which can remove edge noise according to the geometric direction of the image edge, thereby improving the denoising performance, but for images with high noise, its denoising performance is limited

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