Point spread function decoupling in computational fluorescence microscopy

Computational fluorescence microscopy constantly breaks through imaging performance through advanced optical modulation technologies; however, conventional theoretical modeling and experimental measurement approaches are challenging to meet the demand for accurate system characterization of diverse modulations. To this end, we propose a point spread function (PSF) decoupling method that is intrinsically compatible with the optimal demodulation in computational microscopic imaging modality. The critical core lies in designing a sample prior-based computational imaging strategy, in which a regular fluorescent sample instead of generally used sub-diffraction limited particles acts as a system modulator to demodulate the system response. PSF consequently can be computationally optimized through the strong support from the modulated sample prior, achieving accurate non-parametric system characterization and thereby avoiding the modeling difficulty and the low signal-to-noise ratio measurement errors of the system specificity. Experimental results across various biological tissues demonstrated and verified that the proposed PSF decoupling method enables excellent volumetric imaging comparable to confocal microscopy and multicolor, large depth-of-field imaging under aperture modulation. It provides a promising mechanism of system characterization and computational demodulation for high-contrast and high-resolution imaging of cellular and subcellular biological structures and life activities. Sample priors enable point spread function decoupling in computational fluorescence microscopy, improving system characterization and imaging quality.