Video Inpainting Based on Joint Gradient and Noise Minimization

Video inpainting is a process of filling in missing pixels or replacing undesirable pixels in a video. State-of-the-art approaches show limited efficiency to the videos in which the regions to be filled are large and irregular or the occlusions move slowly. Inpainted results on these videos are coarse and have small noise remained or have mosaic appearance. In this paper, we propose a new inpainting method to fix the problem. We rarefy the gradient fields of recovered frames along the horizontal and vertical direction and minimize noise remained after the initial inpainting while still considering the rank minimization of to-be-inpainted matrix. An algorithm which uses partial updates for the dual variables and approximates gradient fields based on Alternating Direction Minimizing strategy is designed to efficiently and effectively optimize the formulation. The experimental results on both simulated and real video data demonstrate the superior performance of the proposed method over state-of-the-arts in terms of preserving details as well as guaranteeing piecewise smoothness of recovered frames.