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Updated: Jan 7, 2026

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Point spread function decoupling in computational fluorescence microscopy.

Ziwei Wang^1,2, Wanyu Gu^1,2, Shaolei Xu^1,2

¹State Key Laboratory of Radio Frequency Heterogeneous Integration, Shenzhen University, Shenzhen, 518060, Guangdong, China.

Light, Science & Applications

|January 1, 2026

Summary

This summary is machine-generated.

A new computational imaging method accurately characterizes complex microscopy systems using a regular fluorescent sample. This point spread function (PSF) decoupling approach improves imaging performance for biological samples.

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Area of Science:

Optical microscopy
Computational imaging
Biophysics

Background:

Accurate characterization of computational fluorescence microscopy systems is crucial but challenging due to diverse optical modulations.
Conventional methods struggle with modeling complex systems and low signal-to-noise ratio measurements.

Purpose of the Study:

To develop a novel point spread function (PSF) decoupling method for accurate system characterization in computational microscopy.
To enable optimal demodulation and high-performance imaging compatible with advanced optical modulation techniques.

Main Methods:

Proposed a sample prior-based computational imaging strategy using a regular fluorescent sample as a system modulator.
Developed a PSF decoupling method for computationally optimizing system response without complex modeling.
Utilized a modulated sample prior to achieve accurate non-parametric system characterization.

Main Results:

Demonstrated accurate, non-parametric system characterization, overcoming limitations of traditional methods.
Achieved excellent volumetric imaging comparable to confocal microscopy across various biological tissues.
Enabled multicolor, large depth-of-field imaging under aperture modulation.

Conclusions:

The PSF decoupling method offers a robust mechanism for system characterization and computational demodulation.
This approach facilitates high-contrast, high-resolution imaging of cellular and subcellular structures.
The method holds promise for advancing live biological imaging and analysis.