遵循人类指令进行高质量图像恢复

摘要

图像恢复是一个基本问题，涉及从受损观测中恢复高质量干净图像。全能图像恢复模型可以有效地从各种类型和级别的退化中恢复图像，利用特定于退化的信息作为提示来指导恢复模型。在这项工作中，我们提出了第一种使用人类编写的指令来指导图像恢复模型的方法。在给定自然语言提示的情况下，我们的模型可以从其受损对应物中恢复高质量图像，考虑多种退化类型。我们的方法InstructIR 在包括图像去噪、去雨、去模糊、去雾和（低光）图像增强在内的多个恢复任务上实现了最先进的结果。InstructIR 比先前的全能恢复方法提高了 +1dB。此外，我们的数据集和结果为基于文本引导的图像恢复和增强的新研究建立了一个新的基准。我们的代码、数据集和模型可在以下网址获取：https://github.com/mv-lab/InstructIR

English

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

遵循人类指令进行高质量图像恢复

High-Quality Image Restoration Following Human Instructions

摘要

Support