The Study of Image Completion Technique Using Low-Rank Concept in Matrix and Tensor Domain

Abstract

In this paper, our interest is to solve the problem of image completion from degraded or noisy image with lost samples utilizing the concept of low-rank-based matrix completion. Low-rank restoration or completion aims at exploring low-rank properties which are present in natural images in order to make use of it for various tasks. The main idea behind this is that the redundant structures make the pixels highly content dependent, and thus, it can be interpreted as rank minimization problem. Solution to this problem is attempted using Alternating Direction Method of Multipliers (ADMM). Experimental results show the comparison between tensor and matrix completion.