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JUDE Deep Joint Unrolling for Deblurring and Low-Light Image Enhancement

by Tu Vo and Chan Y. Park

Introduction

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.

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Prerequisites

  • Pytorch

Datasets

Pretrained Models

Link

Running

Training

...tobeupdated...

Testing

  • 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

Result

Benchmarking the LOL-Blur Dataset.

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

Benchmarking the Real-Blur Dataset.

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

License

This project is licensed under the MIT License - see the LICENSE file for details

Citation

@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}
}

Contact

If you have any questions, please contact tuvv@kc-ml2.com