  Three-dimensional (3D) subcellular imaging is essential for biomedical
research, but the diffraction limit of optical microscopy compromises axial
resolution, hindering accurate 3D structural analysis. This challenge is
particularly pronounced in label-free imaging of thick, heterogeneous tissues,
where assumptions about data distribution (e.g. sparsity, label-specific
distribution, and lateral-axial similarity) and system priors (e.g. independent
and identically distributed (i.i.d.) noise and linear shift-invariant (LSI)
point-spread functions (PSFs)) are often invalid. Here, we introduce SSAI-3D, a
weakly physics-informed, domain-shift-resistant framework for robust isotropic
3D imaging. SSAI-3D enables robust axial deblurring by generating a
PSF-flexible, noise-resilient, sample-informed training dataset and sparsely
fine-tuning a large pre-trained blind deblurring network. SSAI-3D was applied
to label-free nonlinear imaging of living organoids, freshly excised human
endometrium tissue, and mouse whisker pads, and further validated in publicly
available ground-truth-paired experimental datasets of 3D heterogeneous
biological tissues with unknown blurring and noise across different microscopy
systems.
