Image denoising method based on structural similarity and total variation hybrid model

Abstract

The invention discloses an image denoising method based on a structural similarity and total variation hybrid model. The method comprises (1), designing a functional E (u); (2), introducing a new auxiliary variable to the E (u) and turning the original model into two simple sub-models by using an alternate iterative method; (3), performing numerical solution on the two sub-models respectively by using a gradient descent method and a chambolle projection method to obtain a discrete mathematical model; (4), inputting a noisy image f; (5), performing iteration denoising on the f by using the discrete mathematical model; and (6), stopping until the iteration reaches set end conditions and outputting the denoised image. By means of the image denoising method, structural information of images can be well maintained while denoising is performed effectively, visual effects of the images are improved, and the method is applicable to denoising of natural images.

Description

technical field [0001] The invention relates to the field of image technology processing, in particular to an image denoising method based on a structural similarity and total variation mixed model, which is suitable for noise removal of natural images. Background technique [0002] In the process of image formation and transmission, the quality is degraded due to the interference of noise, which seriously affects people's correct understanding of the information conveyed by the image. Therefore, the image must be denoised before the subsequent processing of the image. [0003] There are many methods for image denoising, which can be roughly divided into two categories: spatial domain denoising methods and transform domain denoising methods. The spatial domain denoising method is to directly process the pixels, and the representative algorithms include the mean filter algorithm and the middle finger filter algorithm. The transform domain denoising method mainly uses the dif...

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Problems solved by technology

[0005] Although the ROF model can keep the edge of the image well when denoising, it also has two obvious disadvantages: one is easy to produce "staircase effect", and the other is not good at maintaining details such as image texture.

Therefore, with L 2 Norm is the loyalty item of the denoising model, the denoising result cannot be well consistent with the visual characteristics of the human eye, thus reducing the visual effect of the restored image

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