A Model-Driven Deep Neural Network for Simultaneous Low-Light Image Enhancement and Deblurring

Images captured at night are often degraded by both low-light conditions and motion blur, primarily caused by insufficient illumination and relative motion during exposure. Existing methods typically address only a single type of degradation or rely heavily on data-driven deep neural networks, which limits interpretability and generalization capability. To overcome these limitations, we propose a model-driven deep neural network to effectively handle the joint degradation of low light and blur. By integrating image restoration and Retinex-based modeling, we formulate a unified optimization framework and employ a proximal gradient descent (PGD)-based deep unfolding strategy to recover reflectance and illumination components from low-light blurry images. Furthermore, we design an illumination enhancement module (IEM) and a reflectance refinement module (RRM) to improve brightness, restore fine details, and suppress noise. Extensive experiments on both synthetic and real-world datasets demonstrate that the proposed method outperforms state-of-the-art approaches. Code is available at https://github.com/cassiopeia-yxx/MDNet.