LightMFF: A Simple and Efficient Ultra-Lightweight Multi-Focus Image Fusion Network - Xinzhe Xie; Zijian Lin; Buyu Guo; Shuangyan He; Yanzhen Gu; Yefei Bai; Peiliang Li (Applied Sciences, 2025)
In recent years, deep learning-based multi-focus image fusion (MFF) methods have demonstrated remarkable performance. However, their reliance on complex network architectures often demands substantial computational resources, limiting practical applications. To address this, we propose LightMFF, an ultra-lightweight fusion network that achieves superior performance with minimal computational overhead. Our core insight is to reformulate the multi-focus fusion problem from a classification perspective to a refinement perspective, where coarse initial decision maps and explicit edge information are leveraged to guide the final decision map generation. This novel formulation enables a significantly simplified architecture, requiring only 0.02 M parameters while maintaining state-of-the-art fusion quality. Extensive experiments demonstrate that LightMFF achieves real-time performance at 0.02 s per image pair with merely 0.06 G FLOPs, representing a 98.05% reduction in computational cost compared to prior approaches. Crucially, LightMFF consistently surpasses existing methods across standard fusion quality metrics.