{"ID":2898334,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.13360","arxiv_id":"2507.13360","title":"Low-Light Enhancement via Encoder-Decoder Network with Illumination Guidance","abstract":"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.","short_abstract":"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 guidanc...","url_abs":"https://arxiv.org/abs/2507.13360","url_pdf":"https://arxiv.org/pdf/2507.13360v1","authors":"[\"Le-Anh Tran\",\"Chung Nguyen Tran\",\"Ngoc-Luu Nguyen\",\"Nhan Cach Dang\",\"Jordi Carrabina\",\"David Castells-Rufas\",\"Minh Son Nguyen\"]","published":"2025-07-04T09:35:00Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false,"code_links":[{"ID":612403,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2898334,"paper_url":"https://arxiv.org/abs/2507.13360","paper_title":"Low-Light Enhancement via Encoder-Decoder Network with Illumination Guidance","repo_url":"https://github.com/tranleanh/ednig","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}