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Effective low-light image enhancement with multiscale and context learning network

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Abstract

Convolutional Neural Network (CNN) has been widely used in low-light image enhancement task, and has achieved good enhancement results. However, the enhancement results not only are limited by the convolution kernel, but also are affected by the different shapes and sizes of low-light regions. CNN can only capture local dependencies. It is difficult to obtain long-distance dependencies and multiscale features from images, resulting in over/under enhancement. To alleviate these problems, we propose a Multiscale and Context Learning Network (MCLNet) for adaptive low-light enhancement by multiscale feature extraction and global relationships learning. Concretely, in order to obtain discriminative representation in diverse low-light regions, we design an Attentive Residual Multiscale Block (ARMB) to captures valuable multiscale features through spatial attention mechanisms at different scales. Further, we propose a Bottleblock of Scale Aggregation Module (BSAM) to learn hierarchical discriminative features based on th ARBM. Finally, to further adaptive enhancement from globel view, we present a Context Encoding Module (CEM) to model long-distance dependencies by Transformer. Experimental results show that our proposed MCLNet achieves superior performance of low-light images enhancement than some state-of-the-art methods.

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Data Availability

The dataset analyzed in the current study is available from the corresponding author upon reasonable request. The models and code analyzed in the current study will soon be publicly available at https://github.com/hlinqiao/MCLNet.git.

Notes

  1. https://github.com/hlinqiao/MCLNet.git

  2. https://dragon.larc.nasa.gov/retinex/pao/news/

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Funding

This work was supported in part by the National Natural Science Foundation of China under grant 62072169 and 62172156, and Natural Science Foundation, and Natural Key R&D Program of China under Grant No.2020YFB1713003,and Scientific Research Project of Hunan Provincial Education Department No.19A286.

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Authors and Affiliations

  1. Hunan University, ChangSha, China

    Qiao Li, Bin Jiang & Chao Yang

  2. Beijing Institute of Radiation Medicine, Beijing, China

    Xiaochen Bo

  3. Shenzhen University, Shenzhen, China

    Xu Wu

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  1. Qiao Li
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  2. Bin Jiang
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Correspondence to Bin Jiang.

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Li, Q., Jiang, B., Bo, X. et al. Effective low-light image enhancement with multiscale and context learning network. Multimed Tools Appl 82, 15271–15286 (2023). https://doi.org/10.1007/s11042-022-13830-y

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