Total variation-based image denoising method

Abstract

The invention discloses a total variation-based image denoising method. The method comprises the steps of obtaining a noisy image consisting of an original image and alpha steady state noise; determining each pixel point in the noisy image, wherein the original image complies with Gibbs priori; obtaining an expression, equivalent to the minimization, of the original image according to the noisy image and each pixel point in the noisy image; obtaining a total variation denoising model under the alpha steady state noise according to the expression, equivalent to the minimization, of the original image; obtaining a convex total variation denoising model by combining the obtained total variation denoising model with a convexity penalty term; and solving the convex total variation denoising model by utilizing a primal-dual algorithm, and performing recovery according to an obtained solution to obtain the original image. According to the method, the alpha steady state noise can be well removed; the recovered image is clear; detail information of the image is well reserved; and the recovered image is also closest to the original image.

Description

Technical field [0001] The invention relates to an image denoising method, in particular to an image denoising method based on total variation, and belongs to the technical field of image processing. Background technique [0002] In the process of image collection, transmission and storage, the image will inevitably be contaminated by noise. There are many types and causes of noise. In many cases, it is necessary to denoise the image to make the processed image more suitable for analysis. And information extraction. Image denoising has always been the focus of research in the field of image processing, and it has been favored by more and more researchers in recent years. There are many methods for image denoising, such as wavelet denoising, linear filtering represented by Gaussian filtering, nonlinear filtering represented by median filtering, and nonlinear denoising methods based on partial differential equations. Among them, image denoising based on partial differential equat...

AI Technical Summary

Problems solved by technology

[0007] The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art, to provide an image denoising method based on total variation, and to solve the problem that the existing image denoising method cannot be based on the TV-based α steady-state noise environment. Denoising, and overcome the problem that the solution of the image denoising model may fall into a local minimum, and the model is sensitive to the initial value

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