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InstructIR: High-Quality Image Restoration Following Human Instructions

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Computer Vision – ECCV 2024 (ECCV 2024)

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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.

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Notes

  1. 1.

    https://huggingface.co/TaylorAI/bge-micro-v2.

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Authors and Affiliations

  1. Computer Vision Lab, CAIDAS & IFI, University of Würzburg, Würzburg, Germany

    Marcos V. Conde, Gregor Geigle & Radu Timofte

  2. Sony PlayStation, FTG, Würzburg, Germany

    Marcos V. Conde

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  1. Marcos V. Conde
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  2. Gregor Geigle
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  3. Radu Timofte
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Editors and Affiliations

  1. University of Birmingham, Birmingham, UK

    Aleš Leonardis

  2. University of Trento, Trento, Italy

    Elisa Ricci

  3. Technical University of Darmstadt, Darmstadt, Germany

    Stefan Roth

  4. Princeton University, Princeton, NJ, USA

    Olga Russakovsky

  5. Czech Technical University in Prague, Prague, Czech Republic

    Torsten Sattler

  6. École des Ponts ParisTech, Marne-la-Vallée, France

    Gül Varol

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Conde, M.V., Geigle, G., Timofte, R. (2025). InstructIR: High-Quality Image Restoration Following Human Instructions. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15094. Springer, Cham. https://doi.org/10.1007/978-3-031-72764-1_1

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