提出轻量级低光图像增强模型，兼顾性能与计算效率
Rethinking Theoretical Illumination for Efficient Low-Light Image Enhancement
- 基于局部与全局光照理论设计注意力机制
- 轻量版降低超三分之二计算成本，强版平衡处理能力
- 适合边缘设备部署，适合资源受限场景
低光图像增强在计算机视觉中仍是关键挑战，尤其在边缘设备上需兼顾深度学习的计算需求与轻量化设计。本文提出改进版通道先验与伽马估计网络（CPGA-Net+），引入基于理论的局部与全局光照注意力机制。通过理论分析块结构设计，构建了超轻量与更强两种版本。轻量版利用局部分支作为辅助组件，计算成本降低超过三分之二；强版本则在局部与全局处理能力间实现优异平衡。实验表明，本方法相比近期轻量级方案更具有效性，在有限计算资源下提供更优性能与可扩展解决方案。
原文摘要 · Abstract (English)
Enhancing low-light images remains a critical challenge in computer vision, as does designing lightweight models for edge devices that can handle the computational demands of deep learning. This article introduces an extended version of the Channel-Prior and Gamma-Estimation Network (CPGA-Net), termed CPGA-Net+, incorporating the theoretically-based Attentions for illumination in local and global processing. Additionally, we assess our approach through a theoretical analysis of the block design by introducing both an ultra-lightweight and a stronger version, following the same design principles. The lightweight version significantly reduces computational costs by over two-thirds by utilizing the local branch as an auxiliary component. Meanwhile, the stronger version achieves an impressive balance by maximizing local and global processing capabilities. Our proposed methods have been validated as effective compared to recent lightweight approaches, offering superior performance and scalable solutions with limited computational resources.
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