Neural Network-Based Processing and Reconstruction of Compromised Biophotonic Image Data

arXiv:2403.14324v1 Announce Type: cross
Abstract: The integration of deep learning techniques with biophotonic setups has opened new horizons in bioimaging. A compelling trend in this field involves deliberately compromising certain measurement metrics to engineer better bioimaging tools in terms of cost, speed, and form-factor, followed by compensating for the resulting defects through the utilization of deep learning models trained on a large amount of ideal, superior or alternative data. This strategic approach has found increasing popularity due to its potential …

abstract arxiv cost cs.cv cs.lg data deep learning deep learning techniques engineer form image image data integration measurement metrics network neural network physics.app-ph physics.optics processing speed terms tools trend type