Low-Light Enhancement via Encoder-Decoder Network with Illumination Guidance
Fuente:
arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909694638948352 |
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| author | Tran, Le-Anh Tran, Chung Nguyen Nguyen, Ngoc-Luu Dang, Nhan Cach Carrabina, Jordi Castells-Rufas, David Nguyen, Minh Son |
| author_facet | Tran, Le-Anh Tran, Chung Nguyen Nguyen, Ngoc-Luu Dang, Nhan Cach Carrabina, Jordi Castells-Rufas, David Nguyen, Minh Son |
| contents | This paper introduces a novel deep learning framework for low-light image enhancement, named the Encoder-Decoder Network with Illumination Guidance (EDNIG). Building upon the U-Net architecture, EDNIG integrates an illumination map, derived from Bright Channel Prior (BCP), as a guidance input. This illumination guidance helps the network focus on underexposed regions, effectively steering the enhancement process. To further improve the model's representational power, a Spatial Pyramid Pooling (SPP) module is incorporated to extract multi-scale contextual features, enabling better handling of diverse lighting conditions. Additionally, the Swish activation function is employed to ensure smoother gradient propagation during training. EDNIG is optimized within a Generative Adversarial Network (GAN) framework using a composite loss function that combines adversarial loss, pixel-wise mean squared error (MSE), and perceptual loss. Experimental results show that EDNIG achieves competitive performance compared to state-of-the-art methods in quantitative metrics and visual quality, while maintaining lower model complexity, demonstrating its suitability for real-world applications. The source code for this work is available at https://github.com/tranleanh/ednig. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_13360 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Low-Light Enhancement via Encoder-Decoder Network with Illumination Guidance Tran, Le-Anh Tran, Chung Nguyen Nguyen, Ngoc-Luu Dang, Nhan Cach Carrabina, Jordi Castells-Rufas, David Nguyen, Minh Son Computer Vision and Pattern Recognition This paper introduces a novel deep learning framework for low-light image enhancement, named the Encoder-Decoder Network with Illumination Guidance (EDNIG). Building upon the U-Net architecture, EDNIG integrates an illumination map, derived from Bright Channel Prior (BCP), as a guidance input. This illumination guidance helps the network focus on underexposed regions, effectively steering the enhancement process. To further improve the model's representational power, a Spatial Pyramid Pooling (SPP) module is incorporated to extract multi-scale contextual features, enabling better handling of diverse lighting conditions. Additionally, the Swish activation function is employed to ensure smoother gradient propagation during training. EDNIG is optimized within a Generative Adversarial Network (GAN) framework using a composite loss function that combines adversarial loss, pixel-wise mean squared error (MSE), and perceptual loss. Experimental results show that EDNIG achieves competitive performance compared to state-of-the-art methods in quantitative metrics and visual quality, while maintaining lower model complexity, demonstrating its suitability for real-world applications. The source code for this work is available at https://github.com/tranleanh/ednig. |
| title | Low-Light Enhancement via Encoder-Decoder Network with Illumination Guidance |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.13360 |