Robust Video Restoration by Joint Sparse and Low Rank Matrix Approximation

This paper presents a new video restoration scheme based on the joint sparse and lowrank matrix approximation. By grouping similar patches in the spatiotemporal domain, we formulate the video restoration problem as a joint sparse and low-rank matrix approximation problem. The resulted nuclear norm and `1 norm related minimization problem can also be efficiently solved by many recently developed numerical methods. The effectiveness of the proposed video restoration scheme is illustrated on two applications: video denoising in the presence of random-valued noise and video in-painting for archived films. The numerical experiments indicated the proposed video restoration method compares favorably against many existing algorithms.