ProRes: Exploring Degradation-aware Visual Prompt for Universal Image
  Restoration
Jiaqi Ma Tianheng Cheng Guoli Wang Qian Zhang Xinggang Wang Lefei Zhang
Image restoration aims to reconstruct degraded images, e.g., denoising or
deblurring. Existing works focus on designing task-specific methods and there
are inadequate attempts at universal methods. However, simply unifying multiple
tasks into one universal architecture suffers from uncontrollable and undesired
predictions. To address those issues, we explore prompt learning in universal
architectures for image restoration tasks. In this paper, we present
Degradation-aware Visual Prompts, which encode various types of image
degradation, e.g., noise and blur, into unified visual prompts. These
degradation-aware prompts provide control over image processing and allow
weighted combinations for customized image restoration. We then leverage
degradation-aware visual prompts to establish a controllable and universal
model for image restoration, called ProRes, which is applicable to an extensive
range of image restoration tasks. ProRes leverages the vanilla Vision
Transformer (ViT) without any task-specific designs. Furthermore, the
pre-trained ProRes can easily adapt to new tasks through efficient prompt
tuning with only a few images. Without bells and whistles, ProRes achieves
competitive performance compared to task-specific methods and experiments can
demonstrate its ability for controllable restoration and adaptation for new
tasks. The code and models will be released in
\url{https://github.com/leonmakise/ProRes}.