PromptIR: Prompting for All-in-One Blind Image Restoration

TL;DR

PromptIR introduces a prompt-based learning framework that enables a single model to effectively restore images degraded by various types and levels of corruption, eliminating the need for multiple specialized models.

Contribution

The paper proposes a novel prompt-based approach for all-in-one blind image restoration, enhancing generalization across multiple degradation types and levels.

Findings

Achieves state-of-the-art results on denoising, deraining, and dehazing.

Uses lightweight prompts to encode degradation information.

No prior knowledge of corruption types needed.

Abstract

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