A Lightweight GAN-Based Image Fusion Algorithm for Visible and Infrared Images
This work addresses the need for efficient image fusion for deployment on embedded devices, but it is incremental as it builds upon existing GAN methods with specific enhancements.
The paper tackled the problem of merging visible and infrared images by proposing a lightweight GAN-based algorithm that integrates CBAM and DSConv, resulting in reduced computational cost and improved fusion quality on the M3FD dataset.
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.