by Tu Vo and Chan Y. Park
We introduce JUDE, a Deep Joint Unrolling for Deblurring and Low-Light Image Enhancement, inspired by the image physical model. Based on Retinex theory and the blurring model, the low-light blurry input is iteratively deblurred and decomposed, producing sharp low-light reflectance and illuminance through an unrolling mechanism. Additionally, we incorporate various modules to estimate the initial blur kernel, enhance brightness, and eliminate noise in the final image. Comprehensive experiments on LOL-Blur and Real-LOL-Blur demonstrate that our method outperforms existing techniques both quantitatively and qualitatively. Check our paper for more details.
- Pytorch
...tobeupdated...
- Download the weight and put it to the folder
model_zoo/BOWNet_kernel_prediction_model_v10-5-512-ResUNet
- Check the
options/train.yml
file and modify appropriately. - Run:
python test.py
Model Name | PSNR ↑ | SSIM ↑ | LPIPS ↓ |
---|---|---|---|
FourLLIE → FFTFormer | 18.433 | 0.705 | 0.305 |
LLFormer → FFTFormer | 20.290 | 0.792 | 0.212 |
RetinexFormer → FFTFormer | 16.452 | 0.702 | 0.324 |
MIMO → RetinexFormer | 17.024 | 0.770 | 0.271 |
FFTFormer → RetinexFormer | 16.712 | 0.728 | 0.325 |
FFTFormer | 19.889 | 0.858 | 0.139 |
RetinexFormer | 25.505 | 0.862 | 0.240 |
LEDNet | 25.740 | 0.850 | 0.224 |
FELI | 26.728 | 0.914 | 0.132 |
JUDE | 26.884 | 0.932 | 0.127 |
Model Name | ARNIQA ↑ | CONTRIQUE ↑ | LIQE ↑ | MUSIQ ↑ | CLIPIQA ↑ | DBCNN ↑ |
---|---|---|---|---|---|---|
FourLLIE → FFTFormer | 0.307 | 46.823 | 1.113 | 30.840 | 0.217 | 0.261 |
LLFormer → FFTFormer | 0.401 | 44.743 | 1.158 | 36.534 | 0.208 | 0.257 |
RetinexFormer → FFTFormer | 0.364 | 41.495 | 1.075 | 34.793 | 0.227 | 0.279 |
MIMO → RetinexFormer | 0.413 | 40.773 | 1.137 | 33.242 | 0.207 | 0.276 |
FFTFormer → RetinexFormer | 0.405 | 48.814 | 1.195 | 35.511 | 0.221 | 0.303 |
FFTFormer | 0.402 | 38.005 | 1.141 | 32.079 | 0.289 | 0.307 |
RetinexFormer | 0.418 | 43.410 | 1.074 | 31.782 | 0.187 | 0.232 |
LEDNet | 0.419 | 49.828 | 1.414 | 43.623 | 0.281 | 0.306 |
FELI | 0.429 | 42.354 | 1.155 | 33.669 | 0.207 | 0.239 |
JUDE | 0.437 | 50.207 | 1.454 | 44.732 | 0.299 | 0.313 |
This project is licensed under the MIT License - see the LICENSE file for details
@article{tvo_jude,
author = {Tu Vo and Chan Y. Park},
title = {Deep Joint Unrolling for Deblurring and Low-Light Image Enhancement (JUDE)},
booktitle = {The IEEE/CVF Winter Conference on Applications of Computer Vision},
year = {2025}
}
If you have any questions, please contact tuvv@kc-ml2.com