TENSOR COMPLETION VIA GLOBAL LOW-TUBAL-RANKNESS AND NONLOCAL SELF-SIMILARITY

The recent popular tensor nuclear norm (TNN), a convex surrogate of tensor tubal rank, obtains promising results in tensor completion. Although the TNN-based model has shown its prominent capability of characterizing the global structure of tensors, it lacks the capability for preserving the abundant details of the target tensor. By integrating the global low-tubal-rankness and nonlocal self-similarity, we propose a novel tensor completion model, which recovers the global structural information by TNN regularizer while compensating for the details by plugging in a denoiser to express the nonlocal self-similarity prior. We design an alternating directional method of multipliers (ADMM)-based algorithm to solve the proposed model. Extensive experimental results on color images and fluorescence microscope images demonstrate the superiority of the proposed method over the compared ones.