根據人類指示進行高品質影像修復

摘要

圖像修復是一個基本問題，涉及從降質觀察中恢復高質量乾淨圖像。全能圖像修復模型可以有效地從各種類型和程度的降質中恢復圖像，利用特定於降質的信息作為提示來引導修復模型。在這項工作中，我們提出了第一種使用人類撰寫指令來引導圖像修復模型的方法。在給定自然語言提示的情況下，我們的模型可以從其降質對應物中恢復高質量圖像，考慮多種降質類型。我們的方法InstructIR 在包括圖像去噪、去雨、去模糊、去霧和（低光）圖像增強在內的多個修復任務上實現了最先進的結果。InstructIR 比先前的全能修復方法提高了 +1dB。此外，我們的數據集和結果為基於文本引導的圖像修復和增強的新研究建立了一個新的基準。我們的代碼、數據集和模型可在以下網址找到：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

根據人類指示進行高品質影像修復

High-Quality Image Restoration Following Human Instructions

摘要

Support