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Low-light image enhancement network with decomposition and adaptive information fusion

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Abstract

High-quality clear image can not only bring a good subjective feeling, but also provide good performance guarantee for subsequent computer vision tasks in practical industrial applications. How to improve the low-light image quality and obtain clear image is a challenging task in computer vision. In order to ensure that clear images can be obtained under harsh lighting conditions, we propose a new low-light image enhancement network with decomposition and adaptive information fusion strategy. It firstly decomposes the image by decomposition network, which can obtain a reflection map with more details. Next, a brightness perception network is used to obtain the global and local brightness features of the input image. In addition, we employ an adaptive information fusion module (AIFM) to deal with the redundant information and noise in the multiple features. The experimental results show that the proposed network can not only restore the visually satisfactory image brightness, but also effectively remove the noise and get clear enhancement results. Specifically, the proposed method can achieve 22.20dB PSNR and 0.8380 SSIM gain on LOL dataset, which is the best performance and significantly improved compared with the state-of-the-art methods. We also illustrate the performance by NIQE scores with the proposed method and other comparable algorithms on several other real-world low-light benchmarks datasets including NPE, DICM and LIME, which also indicate that the proposed method has good generalization ability and superiority.

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Acknowledgements

This study was funded by the Natural Science Foundation of Liaoning Province(No. 2020–MS–080), the National Natural Science Foundation of China(No. 61772125)

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Authors and Affiliations

  1. College of Sciences, Northeastern University, Shenyang, 110819, China

    Hegui Zhu, Kai Wang, Ziwei Zhang & Yuelin Liu

  2. Beijing EyeCool Technology Co., Ltd, Beijing, 100085, China

    Wuming Jiang

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  1. Hegui Zhu
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  2. Kai Wang
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  3. Ziwei Zhang
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  4. Yuelin Liu
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  5. Wuming Jiang
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Correspondence to Hegui Zhu.

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Zhu, H., Wang, K., Zhang, Z. et al. Low-light image enhancement network with decomposition and adaptive information fusion. Neural Comput & Applic 34, 7733–7748 (2022). https://doi.org/10.1007/s00521-021-06836-4

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