关键词: 交替方向乘子法(ADMM), 方向全变分(DTV)模型, 图像去噪, l^p(拟)范数图像去噪, ROF模型

Abstract: For the problem of texture image denoising, by analyzing the advantages and disadvantages of the total variation (TV) denoising model and the directional total variation (DTV) denoising model, we propose a robust denoising model based on l^p directional total variation. In the proposed model, in order to efficiently characterize the different structural features in the image, the exponential p in the edge adaptive directional total variation regularization term can be availably chosen in (0,2) based on the structure in the image. Since the proposed model is a non-smooth convex optimization with separable operator, it can be solved by using the alternating direction method of multipliers (ADMM). Then the convergence of the numerical method can be efficiently kept. Compared with other classic models, numerical implementations show that the proposed model can achieve higher peak signal-to-noise ratio and structural similarity, and can effectively retain image details while removing noise.

Key words: alternating direction method of multipliers (ADMM), direction total variation (DTV) model, image denoising, l^p-(quasi)norm image denoising, ROF model