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 https://github.com/ofsoundof/GRL-Image-Restoration.git.

翻译：为实现这一目标,我们首先分析自然图像的两种重要特性,包括跨比例的相似性和厌异性图像特征。为此,我们提议采用固定的条形自省,在自我关注的空间和时间复杂性与区域范围以外的建模能力之间实现良好的平衡。然后,我们提议一个新的网络结构,称为GRL,通过固定的条形自留、窗口自留和引导注意力增强共动,明确模拟全球、区域和地方范围的图像等级。最后,拟议的网络适用于7种图像恢复类型,涵盖真实和合成环境。拟议方法为其中若干类型的图像设定了新的艺术状态。代码将在https://github.com/soundof/GRL-Image-Restoration.git上查阅。</s>