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arXiv · 2401.16468

InstructIR: High-Quality Image Restoration Following Human Instructions

Marcos V. Conde·Gregor Geigle·Radu Timofte
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Abstract

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

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Marcos V. Conde, Gregor Geigle, Radu Timofte. 2024-01-29. InstructIR: High-Quality Image Restoration Following Human Instructions. https://arxiv.org/abs/2401.16468

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