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RCFNC: a resolution and contrast fusion network with ConvLSTM for low-light image enhancement

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Abstract

Low-light image enhancement based on deep learning has achieved breakthroughs recently. However, the current methods based on deep learning have problems with inadequate resolution enhancement or inadequate contrast. To address these problems, this paper proposes a resolution and contrast fusion network with ConvLSTM (RCFNC) for low-light image enhancement. The network is mainly constructed by four parts, including resolution enhancement branch, contrast enhancement branch, multi-scale feature fusion block (MFFB), and convolution long short-time memory block (ConvLSTM). Specifically, to improve the resolution of the low-light image, a resolution enhancement branch consisting of multi-scale differential feature blocks is proposed, using residual features at different scales to enhance the spatial details of image. To enhance the contrast of the image, a contrast enhancement branch consisting of adaptive convolution residual blocks is introduced to learn the mapping relationship between global and local features in the image. In addition, a weighted fusion is performed using MFFB to better balance the resolution and contrast features obtained from the above branches. Finally, to improve the learning capability of the model, ConvLSTM is added to filter redundant information. Experiments on the LOL, MIT5K, and five benchmark datasets show that RCFNC outperforms current state-of-the-art methods.

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Data availibility

The datasets used during and/or analyzed during the current study are publicly available. The corresponding papers are cited accordingly.

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Acknowledgements

The work was supported in part by the Science and Technology Planning Project of Henan Province under Grant 212102210097.

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Authors and Affiliations

  1. School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, China

    Canlin Li, Shun Song, Xinyue Wang, Yan Liu & Lihua Bi

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  1. Canlin Li
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Correspondence to Canlin Li.

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Li, C., Song, S., Wang, X. et al. RCFNC: a resolution and contrast fusion network with ConvLSTM for low-light image enhancement. Vis Comput 40, 2793–2806 (2024). https://doi.org/10.1007/s00371-023-02986-9

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