High-Quality Image Restoration Following Human Instructions

Marcos V. Conde, Gregor Geigle, Radu Timofte

High-Quality Image Restoration Following Human Instructions: 14 upvotes on Hugging Face Daily Papers, #9 of 14 papers on 2024-01-31. Day-by-day upvote history.

Image restoration is a fundamental problem that involves recovering a high-quality clean image from its degraded observation. All-In-One image restoration models can effectively restore images from various types and levels of degradation using degradation-specific information as prompts to guide the restoration model. In this work, we present the first approach that uses human-written instructions to guide the image restoration model. Given natural language prompts, our model can recover high-quality images from their degraded counterparts, considering multiple degradation types. Our method, InstructIR, achieves state-of-the-art results on several restoration tasks including image denoising, deraining, deblurring, dehazing, and (low-light) image enhancement. InstructIR improves +1dB over previous all-in-one restoration methods. Moreover, our dataset and results represent a novel benchmark for new research on text-guided image restoration and enhancement. Our code, datasets and models are available at: https://github.com/mv-lab/InstructIR

Paper page on Hugging Face · arXiv

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