Bilateral enhancement network with signal-to-noise ratio fusion for lightweight generalizable low-light image enhancement
Abstract Low-light image enhancement aims to enhance the visibility and contrast of low-light images while eliminating complex degradation issues such as noise, artifacts, and color distortions. Most existing low-light image enhancement methods either focus on quality while neglecting computational...
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Nature Portfolio
2024-11-01
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| Online Access: | https://doi.org/10.1038/s41598-024-81706-2 |
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| author | Junfeng Wang Shenghui Huang Zhanqiang Huo Shan Zhao Yingxu Qiao |
| author_facet | Junfeng Wang Shenghui Huang Zhanqiang Huo Shan Zhao Yingxu Qiao |
| author_sort | Junfeng Wang |
| collection | DOAJ |
| description | Abstract Low-light image enhancement aims to enhance the visibility and contrast of low-light images while eliminating complex degradation issues such as noise, artifacts, and color distortions. Most existing low-light image enhancement methods either focus on quality while neglecting computational efficiency or have limited learning and generalization capabilities. To address these issues, we propose a Bilateral Enhancement Network with signal-to-noise ratio fusion, called BiEnNet, for lightweight and generalizable low-light image enhancement. Specifically, we design a lightweight Bilateral enhancement module with SNR (Signal-to-Noise Ratio) Fusion (BSF), which serves the SNR map of the input low-light image as the interpolation weights to dynamically fuse global brightness features and local detail features extracted from a bilateral network and achieve differentiated enhancement across different regions. To improve the network’s generalization ability, we propose a Luminance Normalization (LNM) module for preprocessing and a Dual-Exposure Processing (DEP) module for post-processing. LNM divides the channels of input features into luminance-related channels and luminance independent channels, and reduces the inconsistency of the degradation distribution of input low-light images by only normalizing the luminance-related channels. DEP learns overexposure and underexposure corrections simultaneously by employing the ReLU activation function, inverting operation, and residual network, which can improve the robustness of enhancement effects under different exposure conditions while reducing network parameters. Experiments on the LOL-V1 dataset shows BiEnNet significantly increased PSNR by 8.6 $$\%$$ and SSIM by 3.6 $$\%$$ compared to FLW-Net, reduced parameters by 98.78 $$\%$$ , and improved computational speed by 52.64 $$\%$$ compared to the classical KIND. |
| format | Article |
| id | doaj-art-403ed3a3af2d4fc98b1b99273cdde8af |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-403ed3a3af2d4fc98b1b99273cdde8af2025-08-20T02:08:24ZengNature PortfolioScientific Reports2045-23222024-11-0114111610.1038/s41598-024-81706-2Bilateral enhancement network with signal-to-noise ratio fusion for lightweight generalizable low-light image enhancementJunfeng Wang0Shenghui Huang1Zhanqiang Huo2Shan Zhao3Yingxu Qiao4School of Software, Henan Polytechnic UniversitySchool of Software, Henan Polytechnic UniversitySchool of Software, Henan Polytechnic UniversitySchool of Software, Henan Polytechnic UniversitySchool of Computer Science and Technology, Henan Polytechnic UniversityAbstract Low-light image enhancement aims to enhance the visibility and contrast of low-light images while eliminating complex degradation issues such as noise, artifacts, and color distortions. Most existing low-light image enhancement methods either focus on quality while neglecting computational efficiency or have limited learning and generalization capabilities. To address these issues, we propose a Bilateral Enhancement Network with signal-to-noise ratio fusion, called BiEnNet, for lightweight and generalizable low-light image enhancement. Specifically, we design a lightweight Bilateral enhancement module with SNR (Signal-to-Noise Ratio) Fusion (BSF), which serves the SNR map of the input low-light image as the interpolation weights to dynamically fuse global brightness features and local detail features extracted from a bilateral network and achieve differentiated enhancement across different regions. To improve the network’s generalization ability, we propose a Luminance Normalization (LNM) module for preprocessing and a Dual-Exposure Processing (DEP) module for post-processing. LNM divides the channels of input features into luminance-related channels and luminance independent channels, and reduces the inconsistency of the degradation distribution of input low-light images by only normalizing the luminance-related channels. DEP learns overexposure and underexposure corrections simultaneously by employing the ReLU activation function, inverting operation, and residual network, which can improve the robustness of enhancement effects under different exposure conditions while reducing network parameters. Experiments on the LOL-V1 dataset shows BiEnNet significantly increased PSNR by 8.6 $$\%$$ and SSIM by 3.6 $$\%$$ compared to FLW-Net, reduced parameters by 98.78 $$\%$$ , and improved computational speed by 52.64 $$\%$$ compared to the classical KIND.https://doi.org/10.1038/s41598-024-81706-2Low-light Image Enhancement, Lightweight, Generalization ability, SNR Fusion, Normalization |
| spellingShingle | Junfeng Wang Shenghui Huang Zhanqiang Huo Shan Zhao Yingxu Qiao Bilateral enhancement network with signal-to-noise ratio fusion for lightweight generalizable low-light image enhancement Scientific Reports Low-light Image Enhancement, Lightweight, Generalization ability, SNR Fusion, Normalization |
| title | Bilateral enhancement network with signal-to-noise ratio fusion for lightweight generalizable low-light image enhancement |
| title_full | Bilateral enhancement network with signal-to-noise ratio fusion for lightweight generalizable low-light image enhancement |
| title_fullStr | Bilateral enhancement network with signal-to-noise ratio fusion for lightweight generalizable low-light image enhancement |
| title_full_unstemmed | Bilateral enhancement network with signal-to-noise ratio fusion for lightweight generalizable low-light image enhancement |
| title_short | Bilateral enhancement network with signal-to-noise ratio fusion for lightweight generalizable low-light image enhancement |
| title_sort | bilateral enhancement network with signal to noise ratio fusion for lightweight generalizable low light image enhancement |
| topic | Low-light Image Enhancement, Lightweight, Generalization ability, SNR Fusion, Normalization |
| url | https://doi.org/10.1038/s41598-024-81706-2 |
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