The aim of this paper is to propose a mechanism to efficiently and explicitly
model image hierarchies in the global, regional, and local range for image
restoration. To achieve that, we start by analyzing two important properties of
natural images including cross-scale similarity and anisotropic image features.
Inspired by that, we propose the anchored stripe self-attention which achieves
a good balance between the space and time complexity of self-attention and the
modelling capacity beyond the regional range. Then we propose a new network
architecture dubbed GRL to explicitly model image hierarchies in the Global,
Regional, and Local range via anchored stripe self-attention, window
self-attention, and channel attention enhanced convolution. Finally, the
proposed network is applied to 7 image restoration types, covering both real
and synthetic settings. The proposed method sets the new state-of-the-art for
several of those. Code will be available at
this https URL

该论文提出了一种用于图像恢复的机制，通过锚定条纹自注意力机制，GRL 网络架构和其他技术能够明确地建模全局、区域和局部范围内的图像层次结构，并在 7 种图像恢复类型中取得了最新的研究成果。