A Lightweight GAN-Based Image Fusion Algorithm for Visible and Infrared Images

TL;DR

This paper introduces a lightweight GAN-based image fusion method for visible and infrared images that balances high-quality fusion with computational efficiency, suitable for real-time embedded applications.

Contribution

It integrates CBAM and DSConv into a GAN to significantly reduce computational cost while maintaining or improving fusion quality, advancing resource-efficient image fusion techniques.

Findings

Outperforms existing methods in fusion quality on M3FD dataset

Reduces model parameters and inference latency

Validated for real-time embedded deployment

Abstract

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.