  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.
