论文标题
粗到细的：图像恢复由多尺度低级张量完成提升
Coarse to Fine: Image Restoration Boosted by Multi-Scale Low-Rank Tensor Completion
论文作者
论文摘要
现有的低量张量完成（LRTC）方法旨在通过对基础完整的张量施加全球低级限制来恢复部分观察到的张量。但是，这样的全球排名假设在恢复最初的详细信息零件和忽略潜在复杂的对象之间的权衡处于折衷，从而使双方的完成性能都不令人满意。为了解决这个问题，我们提出了一种新颖而实用的图像恢复策略，以粗到1（C2F）的方式恢复部分观察到的张量，该张量通过搜索适当的局部等级来为低级别和高级零件搜索适当的本地等级，从而消除了这种权衡。进行了广泛的实验，以证明拟议的C2F方案的优越性。这些代码可在以下网址提供：https：//github.com/ruilin0212/c2flrtc。
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.
