Low-Light Image Enhancement Using Brightness and Signal-to-Noise Ratio Guided Transformer

The enhanced images generated by some existing low-light image enhancement methods have problems such as uneven brightness, poor denoising effect, and lack of detailed information. To solve these issues, this paper proposes a low-light image enhancement network based on brightness and signal-to-noise ratio guided Transformer. This network has the following advantages: a brightness and signal-to-noise ratio generation sub-network is designed to extract global illumination information and locate dark areas with missing information. The Transformer is guided by brightness and signal-to-noise ratio feature maps to extract long-distance features only from dark areas with missing information to reduce the calculation complexity. Meanwhile, the subsequent feature fusion module is guided to enrich the details of dark areas with the help of bright area information and achieve information sharing. A cross-fusion attention module is designed and introduced between the encoder and decoder, thereby the ability of network is improved to retain image details. Experimental results on four public datasets show that BSGFormer can achieve better enhancement effects than the popular methods in both subjective vision and objective evaluation. © 2025 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.