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Attention-based multi-scale recursive residual network for low-light image enhancement

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Abstract

Aiming at the problems of color distortion, low image processing efficiency, rich context information, spatial information imbalance in the current low-light image enhancement algorithm based on a convolutional neural network. In this paper, an Attention-based multi-scale recursive residual network for low-light image enhancement (AMR-Net) is proposed based on high-resolution, single-scale image processing. First, shallow features are extracted using convolution and channel attention. In the recursive residual unit, a parallel multi-scale residual block is constructed, and the image features are extracted from the three scales: original image resolution, 1/2 resolution, and 1/4 resolution. Then, the deep features and shallow features are connected by selective kernel feature fusion to obtain rich context information and spatial information. Finally, the residual image is obtained by convolution processing of the deep features, and the enhanced image is obtained by adding the original image to the residual image. The experimental results on LOL, LIME, DICM, MEF datasets show that the proposed method has achieved good results in multiple indicators, and reasonably restored the brightness, contrast, and details of the image, thereby intuitively improving the perceived quality of the image.

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Some or all data, models, or code generated or used during the study are available from the corresponding author by request.

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Funding

This work is supported by the National Natural Science Foundation of China Project No.62076199.

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Authors and Affiliations

  1. College of Faculty of Printing, Packaging Engineering and Digital Media Technology, Xi’an University of Technology, Xi’an, 710048, China

    Kaidi Wang, Yuanlin Zheng, Kaiyang Liao, Haiwen Liu & Bangyong Sun

Authors
  1. Kaidi Wang
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  2. Yuanlin Zheng
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  3. Kaiyang Liao
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  4. Haiwen Liu
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  5. Bangyong Sun
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Contributions

All authors made substantial contributions to the concept, design, and revision of the paper. KW and YZ wrote the main manuscript text. KL, HL and BS repared figured and table.

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Correspondence to Yuanlin Zheng.

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Wang, K., Zheng, Y., Liao, K. et al. Attention-based multi-scale recursive residual network for low-light image enhancement. SIViP 18, 2521–2531 (2024). https://doi.org/10.1007/s11760-023-02927-y

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  • DOI: https://doi.org/10.1007/s11760-023-02927-y

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