PromptIR: Prompting for All-in-One Blind Image Restoration
Vaishnav Potlapalli, Syed Waqas Zamir, Salman Khan, Fahad Shahbaz Khan

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…
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Taxonomy
TopicsAdvanced Image Processing Techniques · Photoacoustic and Ultrasonic Imaging · Image Enhancement Techniques
