thanks to marcosv ❤

README.md

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+---
+license: mit
+pipeline_tag: image-to-image
+tags:
+- photography
+- image restoration
+- image enhancement
+- computer vision
+- multimodal
+---
+
+# InstructIR ✏️🖼️
+
+[High-Quality Image Restoration Following Human Instructions](https://arxiv.org/abs/2401.16468) (arxiv version)
+
+[Marcos V. Conde](https://scholar.google.com/citations?user=NtB1kjYAAAAJ&hl=en), [Gregor Geigle](https://scholar.google.com/citations?user=uIlyqRwAAAAJ&hl=en), [Radu Timofte](https://scholar.google.com/citations?user=u3MwH5kAAAAJ&hl=en)
+
+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.
+
+**🚀 You can start with the [demo tutorial](https://github.com/mv-lab/InstructIR/blob/main/demo.ipynb)**
+
+<details>
+<summary> <b> Abstract</b> (click me to read)</summary>
+<p>
+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.
+</p>
+</details>
+
+### Contacts
+For any inquiries contact Marcos V. Conde: <a href="mailto:[email protected]">marcos.conde [at] uni-wuerzburg.de</a>
+
+
+### 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},
+}
+```

im_instructir-7d.pt

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lm_instructir-7d.pt

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