Although there are many excellent solutions in image restoration, the fact
that they are specifically designed for a single image restoration task may
prevent them from being state-of-the-art (SOTA) in other types of image
restoration tasks. While some approaches require considering multiple image
restoration tasks, they are still not sufficient for the requirements of the
real world and may suffer from the task confusion issue. In this work, we focus
on designing a unified and effective solution for multiple image restoration
tasks including deraining, desnowing, defogging, deblurring, denoising, and
low-light enhancement. Based on the above purpose, we propose a Transformer
network Restorer with U-Net architecture. In order to effectively deal with
degraded information in multiple image restoration tasks, we need a more
comprehensive attention mechanism. Thus, we design all-axis attention (AAA)
through stereo embedding and 3D convolution, which can simultaneously model the
long-range dependencies in both spatial and channel dimensions, capturing
potential correlations among all axis. Moreover, we propose a Restorer based on
textual prompts. Compared to previous methods that employ learnable queries,
textual prompts bring explicit task priors to solve the task confusion issue
arising from learnable queries and introduce interactivity. Based on these
designs, Restorer demonstrates SOTA or comparable performance in multiple image
restoration tasks compared to universal image restoration frameworks and
methods specifically designed for these individual tasks. Meanwhile, Restorer
is faster during inference. The above results along with the real-world test
results show that Restorer has the potential to serve as a backbone for
multiple real-world image restoration tasks.

通过 Transformer 网络 Restorer，我们设计了一种综合有效的解决方案，能够在去雨、去雪、去雾、去模糊等多种图像修复任务中展现国际领先或可比较的性能，并且在推理时速度更快。