FIIF-Net: An end-to-end multi-focus image fusion framework for cosmic dust microscopic imaging

Résumé fourni par la source

Abstract Characterizing the microscopic morphology of cosmic dust particles is essential for understanding planetary formation and evolution; however, the shallow depth of field inherent in miniature imaging systems precludes single-shot all-in-focus acquisition. Multi-focus image fusion (MFIF) computationally extends the depth of field, yet existing methods degrade markedly under spatial misalignment and produce hard-boundary stitching artifacts at focus transition regions. We present FIIF-Net, an end-to-end framework designed for misaligned multi-focus microscopic dust images. In the alignment stage, a residual feature downsampling pyramid (RFDP) equipped with a learnable repair factor recovers fine morphological textures lost during conventional downsampling of low-contrast microscopic features, and a dynamic lookup operator (DLO) adaptively modulates the correlation search radius to accommodate vibration-induced, spatially non-uniform displacements. In the fusion stage, a dual-path interactive fusion strategy incorporating STA bidirectionally couples the image-content path with the focus-guidance path, enabling content features and continuous focus cues to mutually modulate each other and thereby replacing binary decision-map stitching with physically consistent soft transitions. Zero-shot super-resolution (ZSSR) is further integrated to compensate for the edge attenuation inherent in defocus-aware blending. A dedicated Multi-Focus Misaligned Cosmic Dust Particle (MFM-CDP) dataset, combining real focal stacks with physics-based rendered data, is constructed for training and evaluation. Experiments on both synthetic and real data demonstrate that FIIF-Net surpasses state-of-the-art MFIF methods quantitatively and visually, particularly in preserving edge details within focal transition regions.

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