In this paper, we present Uformer, an effective and efficient
Transformer-based architecture for image restoration, in which we build a
hierarchical encoder-decoder network using the Transformer block. In Uformer,
there are two core designs. First, we introduce a novel locally-enhanced window
(LeWin) Transformer block, which performs nonoverlapping window-based
self-attention instead of global self-attention. It significantly reduces the
computational complexity on high resolution feature map while capturing local
context. Second, we propose a learnable multi-scale restoration modulator in
the form of a multi-scale spatial bias to adjust features in multiple layers of
the Uformer decoder. Our modulator demonstrates superior capability for
restoring details for various image restoration tasks while introducing
marginal extra parameters and computational cost. Powered by these two designs,
Uformer enjoys a high capability for capturing both local and global
dependencies for image restoration. To evaluate our approach, extensive
experiments are conducted on several image restoration tasks, including image
denoising, motion deblurring, defocus deblurring and deraining. Without bells
and whistles, our Uformer achieves superior or comparable performance compared
with the state-of-the-art algorithms. The code and models are available at
this https URL

本文介绍了 Uformer - 一种基于 Transformer 的高效图像恢复架构，在其中构建了使用 Transformer 块的分层编码器 - 解码器网络。使用局部增强窗口 (LeWin) Transformer 块和多尺度恢复调制器，Uformer 具有高效捕获局部和全局图像依赖性的能力，可用于多种图像恢复任务，并在实验证明与最先进的算法相比具有卓越的性能。