A Total Variational Image Denoising Model Coupled with a New Non-local Edge Extractor and a Horizontal Set Curvature Gradient

Abstract

To solve the problem of false edges in flat region of l₁ norm total variational TV model, an edge extractor based on non-local idea is proposed in this paper. The new edge extractor can effectively suppress the influence of noise and extract the edge information of the image. The new edge extractor is used as the adaptive function and the weighting function of the l_p norm variational model to control the noise reduction ability of the model, and a new model 1 is obtained. Considering that the new model 1 only uses the gradient mode as the image feature operator, which is insufficient to express the image texture information, a new level set curvature gradient variational model 2 combined with the edge extractor is proposed. The new model 2 uses the idea of minimum curvature of the level set of clear images to obtain noise reduction images. By coupling new model 1 and new model 2 to smooth the noise and protect more textures, a new NLSDM model (Non-local level set denoising model) for image noise reduction is obtained. The experimental results show that compared with the noise reduction model, the new model has significantly improved the peak signal-to-noise ratio and structural similarity, and the effect of noise reduction and edge preservation is better.