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 this https URL.
Abstract (translated)
本文介绍了一种新的深度学习框架，用于低光图像增强，名为带有光照指导的编码器-解码器网络（EDNIG）。该框架基于U-Net架构构建，并集成了由亮通道先验（BCP）衍生出的光照图作为指导输入。这种光照引导帮助网络聚焦于曝光不足的区域，从而更有效地控制图像增强过程。 为了进一步提高模型的表现能力，文中还加入了一个空间金字塔池化（SPP）模块以提取多尺度上下文特征，使得模型能够更好地处理不同的照明条件。此外，在训练过程中使用Swish激活函数来确保平滑的梯度传播。 EDNIG在生成对抗网络（GAN）框架中进行了优化，并采用了一种结合了对抗损失、像素级均方误差（MSE）和感知损失的复合损失函数进行训练。 实验结果表明，与现有最先进的方法相比，EDNIG在量化指标和视觉质量方面表现出了竞争力，同时保持较低的模型复杂度，证明其适合于实际应用。该工作的源代码可以在[提供的URL]获取。
URL
https://arxiv.org/abs/2507.13360
