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Title: Dual-Cycle: Self-Supervised Dual-View Fluorescence Microscopy Image Reconstruction using CycleGAN
Three-dimensional fluorescence microscopy often suffers from anisotropy, where the resolution along the axial direction is lower than that within the lateral imaging plane. We address this issue by presenting Dual-Cycle, a new framework for joint deconvolution and fusion of dual-view fluorescence images. Inspired by the recent Neuroclear method, Dual-Cycle is designed as a cycle-consistent generative network trained in a self-supervised fashion by combining a dual-view generator and prior-guided degradation model. We validate Dual-Cycle on both synthetic and real data showing its state-of-the-art performance without any external training data.  more » « less
Award ID(s):
2043134
PAR ID:
10504931
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
IEEE
Date Published:
Journal Name:
Proceedings of the IEEE International Conference on Acoustics Speech and Signal Processing
ISSN:
1520-6149
ISBN:
978-1-7281-6327-7
Page Range / eLocation ID:
1 to 5
Format(s):
Medium: X
Location:
Rhodes Island, Greece
Sponsoring Org:
National Science Foundation
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