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byTu 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 ourpaper for more details.

• Pytorch

• LOL-BlurLink
• Real LOL-BlurLink

Link

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• Download the weight and put it to the foldermodel_zoo/BOWNet_kernel_prediction_model_v10-5-512-ResUNet
• Check theoptions/train.yml file and modify appropriately.
• Run:python test.py

This project is licensed under the MIT License - see theLICENSE 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 contacttuvv@kc-ml2.com