  Volumetric imaging by fluorescence microscopy is often limited by anisotropic
spatial resolution from inferior axial resolution compared to the lateral
resolution. To address this problem, here we present a deep-learning-enabled
unsupervised super-resolution technique that enhances anisotropic images in
volumetric fluorescence microscopy. In contrast to the existing deep learning
approaches that require matched high-resolution target volume images, our
method greatly reduces the effort to put into practice as the training of a
network requires as little as a single 3D image stack, without a priori
knowledge of the image formation process, registration of training data, or
separate acquisition of target data. This is achieved based on the optimal
transport driven cycle-consistent generative adversarial network that learns
from an unpaired matching between high-resolution 2D images in lateral image
plane and low-resolution 2D images in the other planes. Using fluorescence
confocal microscopy and light-sheet microscopy, we demonstrate that the trained
network not only enhances axial resolution, but also restores suppressed visual
details between the imaging planes and removes imaging artifacts.
