Web: http://arxiv.org/abs/2209.07911

Sept. 19, 2022, 1:14 a.m. | Kira Vinogradova, Eugene W. Myers

cs.CV updates on arXiv.org arxiv.org

The quality of microscopy images often suffers from optical aberrations.
These aberrations and their associated point spread functions have to be
quantitatively estimated to restore aberrated images. The recent
state-of-the-art method PhaseNet, based on a convolutional neural network, can
quantify aberrations accurately but is limited to images of point light
sources, e.g. fluorescent beads. In this research, we describe an extension of
PhaseNet enabling its use on 3D images of biological samples. To this end, our
method incorporates object-specific information …


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