A Novel Completion Algorithm for Color Images and Videos Based on Tensor Train Rank

Abstract

Due to the inevitable data missing problem during visual data acquisition, the recovery of color images and videos from limited useful information has become an important topic, for which tensor completion has been proved to be a promising solution in previous studies. In this paper, we propose a novel completion scheme, which can effectively recover missing entries in color images and videos represented by tensors. We first employ a modified tensor train (TT) decomposition as tensor approximation scheme in the concept of TT rank to generate better-constructed and more balanced tensors which preserve only relatively significant informative data in tensors of visual data. Afterwards, we further introduce a TT rank-based weight scheme which can define the value of weights adaptively in tensor completion problem. Finally, we combine the two schemes with Simple Low Rank Tensor Completion via Tensor Train (SiLRTC-TT) to construct our completion algorithm, Low Rank Approximated Tensor Completion via Adaptive Tensor Train (LRATC-ATT). Experimental results validate that the proposed approach outperforms typical tensor completion algorithms in recovering tensors of visual data even with high missing ratios.