Video denoising based on matrix recovery with total variation priori
This article presents a novel scheme for video denoising based on improved matrix recovery strategy. The proposed scheme attempts to go beyond the conventional approaches that focus on the rank properties of the matrix by making use of a priori knowledge derived from the characteristics of video and noise. In this paper, we will first demonstrate that the conventional approach such as robust PCA (principal component analysis) is not effective when the video is corrupted by the mixture of impulse and Gaussian noises. The impulse noise can be considered sparse in the image domain and can be effectively filtered by matrix recovery. However, the dense Gaussian noise cannot be easily filtered because it is not sparse in either spatial or frequency domain. We shall show that this Gaussian noise corrupted video can be considered sparse in the 3D total variation domain. Based on this, we formulate the problem as a 3D total variation optimization and design an algorithm to solve this convex problem efficiently. Experimental results show that the proposed scheme achieves noticeable improvement over the state-of-the-art algorithm VBM3D [5].