photography image restoration image enhancement computer vision multimodal image-to-image arxiv:2401.16468 license:mit region:us
---
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:marcos.conde@uni-wuerzburg.de">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},
}
```