Rethinking Multi-Focus Image Fusion: An Input Space Optimization View

Multi-focus image fusion (MFIF) addresses the challenge of partial focus by integrating multiple source images taken at different focal depths. Unlike most existing methods that rely on complex loss functions or large-scale synthetic datasets, this study approaches MFIF from a novel perspective: optimizing the input space. The core idea is to construct a high-quality MFIF input space in a cost-effective manner by using intermediate features from well-trained, non-MFIF networks. To this end, we propose a cascaded framework comprising two feature extractors, a Feature Distillation and Fusion Module (FDFM), and a focus segmentation network Y ${}^{U}$ Net. Based on our observation that discrepancy and edge…

• NN
  National Natural Science Foundation of China

  Awards: 62401097, 62521007, 62427819
• NS
  Natural Science Foundation of Heilongjiang Province

  Award: LH2023F016
• NS
  Natural Science Foundation of Liaoning Province

  Award: 2024-BS-028
• FR
  Fundamental Research Funds for the Central Universities

  Award: 0854-53