update feb

.gitignore

@@ -2,3 +2,4 @@ __pycache__/
 *.pt
 *.gif
 *.pth
+flagged/

app.py

@@ -85,16 +85,17 @@ def process_img (image, prompt):
 
 
 title = "InstructIR ✏️🖼️ 🤗"
-description = ''' ## [High-Quality Image Restoration Following Human Instructions](https://github.com/mv-lab/InstructIR)
+description = ''' 
+## [High-Quality Image Restoration Following Human Instructions](https://arxiv.org/abs/2401.16468)
 
-[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)
+[Marcos V. Conde](https://mv-lab.github.io/), [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
+*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 (single) 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)** Check [our github](https://github.com/mv-lab/InstructIR) for more information
+**🚀 You can start with the [demo tutorial.](https://github.com/mv-lab/InstructIR/blob/main/demo.ipynb)** Check **[our github](https://github.com/mv-lab/InstructIR)** for more information
 
 <details>
 <summary> <b> Abstract</b> (click me to read)</summary>
@@ -109,10 +110,24 @@ You can also try general image enhancement prompts (e.g., "retouch this image",
 
 **Datasets:** We use these datasets BSD100, BSD68, Urban100, WED, Rain100, Aobe MIT5K, LOL, GoPro, SOTS (haze). This demo expects an image with some degradations (blur, noise, rain, low-light, haze).
 <br>
+```
+@article{conde2024high,
+  title={High-Quality Image Restoration Following Human Instructions},
+  author={Conde, Marcos V and Geigle, Gregor and Timofte, Radu},
+  journal={arXiv preprint arXiv:2401.16468},
+  year={2024}
+}
+
+```
+<br>
 '''
 
 
-article = "<p style='text-align: center'><a href='https://github.com/mv-lab/InstructIR' target='_blank'>High-Quality Image Restoration Following Human Instructions</a></p>"
+article = '''
+<p style='text-align: center'> Check our code, models and results at: <a href='https://github.com/mv-lab/InstructIR' target='_blank'>https://github.com/mv-lab/InstructIR</a></p>
+<p style='text-align: center'> Read the full paper at: <a href='https://arxiv.org/abs/2401.16468' target='_blank'>High-Quality Image Restoration Following Human Instructions</a></p>
+<p style='text-align: center'> Consider citing our work if you use it, or you find it insightful </p>
+'''
 
 #### Image,Prompts examples
 examples = [['images/a4960.jpg', "my colors are too off, make it pop so I can use it in instagram"],