  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
