轻量GAN融合可见光与红外图像，兼顾画质与实时性。
A Lightweight GAN-Based Image Fusion Algorithm for Visible and Infrared Images
- 用CBAM增强特征关注，用深度可分离卷积降计算开销
- 参数量和延迟显著降低，融合质量优于同类方法
- 适合嵌入式设备部署，适用于复杂环境实时应用
本文提出一种专为可见光与红外图像融合设计的轻量级算法，重点平衡性能与效率。通过在生成器中引入卷积块注意力模块（CBAM）提升特征聚焦能力，并采用深度可分离卷积（DSConv）实现更高效的计算。这些改进显著降低了模型的计算成本，包括参数量和推理延迟，同时保持甚至提升了融合图像的质量。在M3FD数据集上的对比实验表明，该算法不仅在融合质量上优于同类方法，且更具资源效率，适合部署于嵌入式设备。通过大量消融实验验证了轻量化设计的有效性，证实其在复杂环境中的实时应用潜力。
原文摘要 · Abstract (English)
This paper presents a lightweight image fusion algorithm specifically designed for merging visible light and infrared images, with an emphasis on balancing performance and efficiency. The proposed method enhances the generator in a Generative Adversarial Network (GAN) by integrating the Convolutional Block Attention Module (CBAM) to improve feature focus and utilizing Depthwise Separable Convolution (DSConv) for more efficient computations. These innovations significantly reduce the model's computational cost, including the number of parameters and inference latency, while maintaining or even enhancing the quality of the fused images. Comparative experiments using the M3FD dataset demonstrate that the proposed algorithm not only outperforms similar image fusion methods in terms of fusion quality but also offers a more resource-efficient solution suitable for deployment on embedded devices. The effectiveness of the lightweight design is validated through extensive ablation studies, confirming its potential for real-time applications in complex environments.
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