Deconvolution algorithms can reconstruct sharp images from out-of-focus microscopy data
Deconvolution algorithms are widely established computational tools used to reverse optical aberrations and effectively reconstruct sharp, high-resolution images from blurred microscopy data.
Multiple papers consistently demonstrate that deconvolution algorithms and related computational post-processing methods successfully correct optical aberrations, diffraction limits, and out-of-focus blur in microscopic imaging data.
Kohli A, Angelopoulos AN, McAllister D, Whang E, You S, Yanny K, Gasparoli FM, Chang BJ, Fiolka R, Waller L. Ring deconvolution microscopy: exploiting symmetry for efficient spatially varying aberration correction.. 2025. https://doi.org/10.1038/s41592-025-02684-5
Ring deconvolution microscopy uses aberration correction and deconvolution techniques to improve image quality and resolve spatial blurring across various microscope modalities.
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Sachuk A, Volkova E, Rakovskaya A, Chukanov V, Pchitskaya E. NeuroDecon: A Neural Network-Based Method for Three-Dimensional Deconvolution of Fluorescent Microscopic Images.. 2025. https://doi.org/10.3390/ijms26188770
NeuroDecon demonstrates that deconvolution techniques successfully revert optical aberrations and enhance spatial resolution in microscopic imaging.
Hou Y, Fu Y, Wang W, Cao R, Su X, Kim D, Li M, Xi P. Data-adaptive three-dimensional deconvolution and evaluation for volumetric fluorescence microscopy. 2026. https://doi.org/10.64898/2026.06.29.735443
Computational 3D deconvolution mitigates diffraction and resolution limits in volumetric fluorescence microscopy, restoring degraded optical data.
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