High‐Fidelity Miniature Fluorescence Microscopy Using Zernike‐Based Point‐Spread‐Function Modeling and Hardware‐Assisted Calibration

Miniature fluorescence microscopy (Miniscope) enables critical observation of neural dynamics in freely behaving animals. However, its simplified optical design inherently limits spatial resolution and introduces significant background fluorescence, constraining image fidelity. To address these challenges, we present MiniZSV, a universal and practical image enhancement pipeline comprising background removal, Zernike‐polynomial‐based point‐spread‐function (PSF) modeling, and spatially‐varying deconvolution. Our pipeline leverages Zernike polynomial to represent an accurate spatially‐varying PSF map based on experimental data acquired by an open‐source Miniscope toolkit, ensuring high signal‐to‐noise ratio (SNR) reconstructions beyond conventional approaches. Applied to in vivo calcium imaging and angiography, MiniZSV uncovers low‐SNR neurons and overlapping vascular structures, significantly improving neuron extraction and hemodynamic analysis beyond the limits of raw Miniscope data. By providing higher‐fidelity imaging data, MiniZSV facilitates more accurate and versatile downstream analyses in neurobiology and advances the potential of Miniscope technology.