InstructIR ✏️🖼️
High-Quality Image Restoration Following Human Instructions (arxiv version)
Marcos V. Conde, Gregor Geigle, Radu Timofte
Computer Vision Lab, University of Wuerzburg | Sony PlayStation, FTG
TL;DR: quickstart
InstructIR takes as input an image and a human-written instruction for how to improve that image. The neural model performs all-in-one image restoration. InstructIR achieves state-of-the-art results on several restoration tasks including image denoising, deraining, deblurring, dehazing, and (low-light) image enhancement.
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Abstract (click me to read)
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.
Contacts
For any inquiries contact Marcos V. Conde: marcos.conde [at] uni-wuerzburg.de
Citation BibTeX
@misc{conde2024instructir,
title={High-Quality Image Restoration Following Human Instructions},
author={Marcos V. Conde, Gregor Geigle, Radu Timofte},
year={2024},
journal={arXiv preprint},
}
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