Dual-Cycle: Self-Supervised Dual-View Fluorescence Microscopy Image Reconstruction using CycleGAN
This addresses resolution anisotropy in microscopy for biomedical imaging, but it is incremental as it builds on prior methods like Neuroclear.
The paper tackles the problem of anisotropic resolution in 3D fluorescence microscopy by proposing Dual-Cycle, a self-supervised framework for joint deconvolution and fusion of dual-view images, achieving state-of-the-art performance on synthetic and real data without external training data.
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.