Image restoration involves recovering a high-quality clean image from its degraded version. Deep learning-based methods have significantly improved image restoration performance, however, they have limited generalization ability to different degradation types and levels. This restricts their real-world application since it requires training individual models for each specific degradation and knowing the input degradation type to apply the relevant model. We present a prompt-based learning approach, PromptIR, for All-In-One image restoration that can effectively restore images from various types and levels of degradation. In particular, our method uses prompts to encode degradation-specific information, which is then used to dynamically guide the restoration network. This allows our method to generalize to different degradation types and levels, while still achieving state-of-the-art results on image denoising, deraining, and dehazing. Overall, PromptIR offers a generic and efficient plugin module with few lightweight prompts that can be used to restore images of various types and levels of degradation with no prior information on the corruptions present in the image. Our code and pretrained models are available here: https://github.com/va1shn9v/PromptIR

翻译：图像恢复旨在从退化版本中恢复出高质量干净图像。基于深度学习的方法显著提升了图像恢复性能，但面对不同退化类型和程度时泛化能力有限。这限制了其实际应用，因为需要针对每种特定退化训练独立模型，并预先知晓输入退化类型才能选用相应模型。我们提出一种基于提示学习的全合一图像恢复方法PromptIR，能够有效处理多种类型和程度的退化。具体而言，该方法利用提示编码退化特定信息，进而动态引导恢复网络。这使得我们的方法能够泛化至不同退化类型和程度，同时在图像去噪、去雨和去雾任务上仍达到最优结果。总体而言，PromptIR提供了一种通用且高效的即插即用模块，仅需少量轻量级提示，无需任何关于图像中退化类型的先验信息即可恢复多种类型和程度的退化图像。我们的代码与预训练模型已开源：https://github.com/va1shn9v/PromptIR