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

Tu Vo, Chan Y. Park; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 2696-2705

Low-light and blurring issues are prevalent when capturing photos at night often due to the use of long exposure to address dim environments. Addressing these joint problems can be challenging and error-prone if an end-to-end model is trained without incorporating an appropriate physical model. In this paper 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.