图像复原
计算机科学
图像(数学)
卷积(计算机科学)
航程(航空)
编码(集合论)
自相似性
相似性(几何)
人工智能
理论计算机科学
算法
图像处理
数学
人工神经网络
材料科学
几何学
集合(抽象数据类型)
复合材料
程序设计语言
作者
Yawei Li,Yuchen Fan,Xiaoyu Xiang,Denis Demandolx,Rakesh Ranjan,Radu Timofte,Luc Van Gool
标识
DOI:10.1109/cvpr52729.2023.01753
摘要
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.
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