Low-Light Enhancement via Encoder-Decoder Network with Illumination Guidance

• URL: http://arxiv.org/abs/2507.13360v1
• Date: Fri, 04 Jul 2025 09:35:00 GMT
• Title: Low-Light Enhancement via Encoder-Decoder Network with Illumination Guidance
• Authors: Le-Anh Tran, Chung Nguyen Tran, Ngoc-Luu Nguyen, Nhan Cach Dang, Jordi Carrabina, David Castells-Rufas, Minh Son Nguyen,
• Abstract summary: This paper introduces a novel deep learning framework for low-light image enhancement, named the.<n>the-Decoder Network with Illumination Guidance (EDNIG)<n>EDNIG integrates an illumination map, derived from Bright Channel Prior (BCP), as a guidance input.<n>It 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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