  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 beyond the diffraction limit, but
also enhances suppressed visual details between the imaging planes and removes
imaging artifacts.
