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

翻译：图像复原是一个基础性问题，涉及从退化观测中恢复高质量清晰图像。全能型图像复原模型能够利用特定于退化的信息作为提示引导复原模型，有效处理多种类型和程度的退化。本研究首次提出采用人类书面指令引导图像复原模型的方法。通过自然语言提示，我们的模型可针对多种退化类型，从退化图像中恢复高质量图像。所提出的InstructIR方法在图像去噪、去雨、去模糊、去雾及（低光照）图像增强等多项复原任务中均达到当前最优性能。InstructIR相较先前全能型复原方法提升超过1dB。此外，我们的数据集与成果为基于文本引导的图像复原与增强研究提供了全新基准。相关代码、数据集及模型已发布于：https://github.com/mv-lab/InstructIR