Robust video denoising using low rank matrix completion

Most existing video denoising algorithms assume a single statistical model of image noise, e.g. additive Gaussian white noise, which often is violated in practice. In this paper, we present a new patch-based video denoising algorithm capable of removing serious mixed noise from the video data. By grouping similar patches in both spatial and temporal domain, we formulate the problem of removing mixed noise as a low-rank matrix completion problem, which leads to a denoising scheme without strong assumptions on the statistical properties of noise. The resulting nuclear norm related minimization problem can be efficiently solved by many recent developed methods. The robustness and effectiveness of our proposed denoising algorithm on removing mixed noise, e.g. heavy Gaussian noise mixed with impulsive noise, is validated in the experiments and our proposed approach compares favorably against a few state-of-art denoising algorithms.

• Type: Conference
• Year: 2010
• Where: CVPR
• Authors: Hui Ji, Chaoqiang Liu, Zuowei Shen, Yuhong Xu
• Keywords: Computer Vision, CVPR 2010, Denoising Algorithms, Mixed Noise, Video Denoising Algorithm