Dual-Cycle: Self-Supervised Dual-View Fluorescence Microscopy Image Reconstruction using CycleGAN

Tomás Kerepecký; Jiaming Liu; Xue Wen Ng; David W. Piston; Ulugbek S. Kamilov

ICASSP 2023accepted0 citations

Dual-Cycle: Self-Supervised Dual-View Fluorescence Microscopy Image Reconstruction using CycleGAN

Tomás Kerepecký, Jiaming Liu, Xue Wen Ng, David W. Piston, Ulugbek S. Kamilov

Abstract

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.

BibTeX

@inproceedings{icassp2023_dualcycleselfsup,
  title = {Dual-Cycle: Self-Supervised Dual-View Fluorescence Microscopy Image Reconstruction using CycleGAN},
  author = {Tomás Kerepecký and Jiaming Liu and Xue Wen Ng and David W. Piston and Ulugbek S. Kamilov},
  booktitle = {ICASSP 2023},
  year = {2023}
}