Multi-filters guided low-rank tensor coding for image inpainting

Image inpainting is a classical inverse ill-posed problem. In this paper, we introduce a multi-filters guided low-rank tensor coding as a priori information to tackle it. The key innovation is to formulate multiple feature-domain tensors by convoluting the target image with multi-filters. Furthermore, by exploring a low-rank tensor coding, it can reduce the redundancy between sparse feature vectors at neighboring locations and improve the efficiency of the overall representation. The resulting non-convex model is iteratively tackled by gradient descent and low-rank pursuit procedure. The experimental results demonstrate that the proposed algorithm can faithfully recover image and outperform the current state-of-the-art approach.