Abstract
Low-light image enhancement remains a challenging task, particularly in the absence of paired training data. In this study, we present LucentVisionNet, a novel zero-shot learning framework that addresses the limitations of traditional and deep learning-based enhancement methods. The proposed approach integrates multi-scale spatial attention with a deep curve estimation network, enabling fine-grained enhancement while preserving semantic and perceptual fidelity. To further improve generalization, we adopt a recurrent enhancement strategy and optimize the model using a composite loss function comprising six tailored components, including a novel no-reference image quality loss inspired by human visual perception. Extensive experiments on both paired and unpaired benchmark datasets demonstrate that LucentVisionNet consistently outperforms state-of-the-art supervised, unsupervised, and zero-shot methods across multiple full-reference and no-reference image quality metrics. Our framework achieves high visual quality, structural consistency, and computational efficiency, making it well-suited for deployment in real-world applications such as mobile photography, surveillance, and autonomous navigation.
Abstract (translated)
低光图像增强依然是一个挑战性的任务，尤其是在缺乏配对训练数据的情况下。本研究中，我们提出了一种新的零样本学习框架——LucentVisionNet，旨在解决传统方法和基于深度学习的方法的局限性。该提出的方案结合了多尺度空间注意力机制与深层曲线估计网络，使图像能够进行精细化增强，并同时保持语义和感知保真度。为了进一步提高泛化能力，我们采用了一种递归增强策略，并使用一个包含六个定制组件的复合损失函数来优化模型，其中包括一种受人类视觉感知启发的新颖无参考图像质量损失。在配对和非配对基准数据集上的大量实验表明，LucentVisionNet在多个全参考和无参考图像质量度量标准下始终优于现有的监督、非监督以及零样本方法。 我们的框架实现了高质量的视觉效果、结构一致性及计算效率，在移动摄影、监控与自主导航等实际应用场景中表现出良好的适用性。
URL
https://arxiv.org/abs/2506.18323
