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

Authors

Yao Xiao, Youshen Xia, Zhenyu Lu, Changyin Sun, Jinjun Shan

Published in

IEEE transactions on neural networks and learning systems. Volume PP. Aug 26, 2026. Epub Aug 26, 2026.

Abstract

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.

PMID:
42647695
Bibliographic data and abstract were imported from PubMed on 27 Aug 2026.

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