基于局部算子不同形式的TV(total variation)模型用于彩色图像的噪声去除时往往存在边缘模糊、纹理模糊、阶梯效应、Mosaic效应等问题。因此,将传统局部的Tikhonov模型、TV模型、MTV(multi-channel total variation)模型、CTV(color total variation)模型推广到基于非局部算子概念的NL-CT(non-local color Tikhonov)模型、NL-LTV(non-local layered total variation)模型、NL-MTV(non-local multi-channel total variation)模型、NL-CTV(non-local color total variation)模型,并通过引入辅助变量和Bregman迭代参数设计了相应的快速Split Bregman算法。实验结果表明,所提出的非局部TV模型都很好地解决了局部模型中出现的问题,在纹理、边缘、光滑度等特征保持方面取得了良好特性,其中NL-CTV处理效果最好,但是计算效率较低。

The traditional total variation (TV) models based on local operators for color image denoising has in some problems, such as smeared edges, smeared textures, staircase effects, and mosaic effects. In this paper, the Tikhonov model, TV model, multi-channel total variation (MTV) model, color total variation (CTV)model based on local operators are extended to the non-local color Tikhonov (NL-CT)model, non-local layered total variation (NL-LTV)model, non-local multi-channel total variation (NL-MTV)model, non-local color total variation(NL-CTV)model via non-local operators for color texture image denoising. Using auxiliary variables and the Bregman iterartive parameters, we design their fast Split-Bregman algorithms. Experiments show that all of them solve the above mentioned effects and demonstrate excellent properties of edge preserving, texture preserving, and smoothness preserving. NL-CTV has the best result,but its computational efficiency is low.