Shuang Fu

This paper introduces LUNAR, a self-supervised neural-physics learning framework for 3D localization microscopy that improves robustness to optical aberrations and high-density molecule localization.

This paper introduces a method to remove defocus backgrounds in fluorescence microscopy using dark channels and dual frequency separation, significantly improving image quality.

This paper develops a high refractive index imaging buffer to improve 3D SMLM imaging depth in thick samples by reducing RI mismatch and enhancing image quality.

This paper introduces a GPU-based B-spline PSF modeling method for fast and accurate high-dimensional single molecule analysis.

This study evaluates how different PSF shapes affect 3D localization accuracy under varying sampling rates and noise conditions, identifying optimal pixel sizes for each PSF.