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

Abstract

Image inpainting is a classical yet challenging inverse ill-posed problem. In this paper, we introduce the multi-filters guided low-rank tensor coding as a prior 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 procedure for updating the image and by low-rank pursuit procedure for updating the multi-view features. Besides, we explore an aggregation version of proposed method for further improving the inpainting performance. Experimental results demonstrate that the proposed algorithms can faithfully recover image and outperform the current state-of-the-art approaches in terms of visual inspection, the quantitative peak signal-to-noise ratio (PSNR), and structural similarity (SSIM).