Restorer: Solving Multiple Image Restoration Tasks with One Set of Parameters

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.