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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    Titel :

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


    Beteiligte:
    Weng, Jiangwei (Autor:in) / Fan, Junkai (Autor:in) / Qian, Jianjun (Autor:in) / Zou, Haiyang (Autor:in) / Tai, Ying (Autor:in) / Yang, Jian (Autor:in) / Li, Jun (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.07.2025


    Format / Umfang :

    10102413 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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