Extreme low-light image enhancement using deep neural network

Ming Xu; Hongping Li; Jian Chen

doi:10.1117/12.2589358

27 January 2021 Extreme low-light image enhancement using deep neural network

Ming Xu, Hongping Li, Jian Chen

Proceedings Volume 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020); 1172015 (2021) https://doi.org/10.1117/12.2589358
Event: Twelfth International Conference on Graphics and Image Processing, 2020, Xi'an, China

Abstract

Low-light images are inappropriate for human observation and computer processing. Multiple enhancement techniques have been proposed but most of them are based on synthetic datasets. In this paper, an image enhancement method using deep convolutional neural network is introduced. The model is expert at merging multi-scale information and it is flexible for image of arbitrary size without pre- or post-processing. Different from previous work, we deliberately address those extreme low-light images which cannot be identified by human eyes. For doing so, a Real Extreme Low-Light Images (RELLI) dataset is collected to validate the method, and will be contributed to related research. We also analyze some factors that affect the model’s performance to achieve the best results. Furthermore, the generalization ability beyond RELLI dataset and the superiority over several state-of-the-art methods are also confirmed.

Citation Download Citation

Ming Xu, Hongping Li, and Jian Chen "Extreme low-light image enhancement using deep neural network", Proc. SPIE 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020), 1172015 (27 January 2021); https://doi.org/10.1117/12.2589358

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