LACN A lightweight attention-guided ConvNeXt network for low-light image enhancement

Saijie Fan, Wei Liang, Derui Ding*, Hui Yu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Images captured under low-light conditions usually have poor visual quality, and hence greatly reduce the accuracy of subsequent tasks such as image segmentation and detection. In the low-light image enhancement task, noises in the dark areas are generally amplified while the images’ brightness is enhanced. It should be pointed out that many deep learning methods cannot effectively suppress the noise at this stage and capture important feature information. To address the above problem, this paper proposes a Lightweight Attention-guided ConvNeXt Network (LACN) for low-light image enhancement. A novel Attention ConvNeXt Module (ACM) is first proposed by introducing a parameter-free attention module (i.e. SimAM) into the ConvNeXt backbone network. Then, a nontrivial lightweight network LACN based on a multi-attention mechanism is established through stacking two ACMs and fusing their features. In what follows, an improved hybrid attention mechanism, Selective Kernel Attention Module (SKAM), is adopted to effectively extract both global and local information. Such a module realizes the evaluation of lighting conditions for the whole image and the adaptive adjustment of the receptive field. Finally, through the feature fusion module, the features of different stages are aggregated to improve the ability of network to retain color information. Numerous experiments on low-light image enhancement are implemented via comparison with other state-of-the-art methods. Experiments show that the proposed method significantly improves the brightness and contrast of low-illumination images, preserves color information, and suppresses the generation of noises after image brightening.

Original languageEnglish
Article number105632
Number of pages11
JournalEngineering Applications of Artificial Intelligence
Early online date22 Nov 2022
Publication statusPublished - 1 Jan 2023


  • ConvNeXt networks
  • feature fusion
  • low-light image enhancement
  • selective kernel attention modules


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