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

TL;DR

Dual-Cycle is a self-supervised cycle-consistent generative network that enhances 3D fluorescence microscopy by jointly deconvolving and fusing dual-view images, improving axial resolution without external data.

Contribution

It introduces a novel self-supervised dual-view reconstruction framework using cycleGAN architecture for fluorescence microscopy.

Findings

Achieves state-of-the-art performance on synthetic and real data.

Operates without external training data.

Effectively improves axial resolution in fluorescence images.

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.