无需配对数据，多尺度注意力提升暗光图像增强效果。
A Multi-Scale Spatial Attention-Based Zero-Shot Learning Framework for Low-Light Image Enhancement
- 用多尺度空间注意力融合细节与全局信息，增强图像结构。
- 在多个数据集上超越现有零样本方法，视觉质量显著提升。
- 适合移动端、监控等实时应用，兼顾效率与真实感。
低光照图像增强在缺乏成对训练数据的情况下仍具挑战性。本文提出LucentVisionNet，一种新颖的零样本学习框架，克服了传统及深度学习方法的局限。该方法结合多尺度空间注意力与深度曲线估计网络，实现细粒度增强并保持语义和感知保真度。为提升泛化能力，采用循环增强策略，并通过包含六个定制组件的复合损失函数优化模型，其中引入一种受人眼视觉感知启发的无参考图像质量损失。在配对与非配对基准数据集上的大量实验表明，LucentVisionNet在多种全参考与无参考指标上持续优于最先进的监督、无监督及零样本方法。本框架具备高视觉质量、结构一致性与计算高效性，适用于移动摄影、监控及自动驾驶等实际场景。
原文摘要 · Abstract (English)
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.
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