Coarse to Fine: Image Restoration Boosted by Multi-Scale Low-Rank Tensor Completion
Abstract
Existing low-rank tensor completion (LRTC) approaches aim at restoring a partially observed tensor by imposing a global low-rank constraint on the underlying completed tensor. However, such a global rank assumption suffers the trade-off between restoring the originally details-lacking parts and neglecting the potentially complex objects, making the completion performance unsatisfactory on both sides. To address this problem, we propose a novel and practical strategy for image restoration that restores the partially observed tensor in a coarse-to-fine (C2F) manner, which gets rid of such trade-off by searching proper local ranks for both low- and high-rank parts. Extensive experiments are conducted to demonstrate the superiority of the proposed C2F scheme. The codes are available at: https://github.com/RuiLin0212/C2FLRTC.
Cite
@article{arxiv.2203.15189,
title = {Coarse to Fine: Image Restoration Boosted by Multi-Scale Low-Rank Tensor Completion},
author = {Rui Lin and Cong Chen and Ngai Wong},
journal= {arXiv preprint arXiv:2203.15189},
year = {2022}
}