Enhancing real nighttime images is a significant challenge due to the deterioration of visual quality caused by limited perceptibility under adverse illumination conditions, leading to loss of details and color deviation. In this paper, we propose a novel nighttime image enhancement framework using daytime-mixed non-aligned supervision. It aims to couple the information between non-aligned daytime and nighttime image pairs. Specifically, our framework consists of a simple yet effective daytime-mixed supervised learning phase and a Retinex-based reconstruction phase. In the first phase, we employ a multi-instance with adaptive information fusion (AIF) module integrated within a UNet enhancement network called MIFUNet, which is trained via a daytime-mixed supervised loss. In the second phase, the Retinex-based reconstruction employs both a light-effect estimation network and an illumination adjustment network to restore the nighttime image, guided by physical principles. To evaluate the effectiveness of our approach, we collect a real non-aligned day-night dataset named the NANE dataset, which contains 748 non-aligned image pairs and 100 nighttime images solely for testing. Extensive experiments demonstrate that our method achieves superior performance compared to state-of-the-art image enhancement methods.


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    Title :

    Daytime-Mixed Non-Aligned Learning for Real Nighttime Image Enhancement


    Contributors:
    Weng, Jiangwei (author) / Fan, Junkai (author) / Qian, Jianjun (author) / Zou, Haiyang (author) / Tai, Ying (author) / Yang, Jian (author) / Li, Jun (author)


    Publication date :

    2025-07-01


    Size :

    10102413 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





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