Isotropic Reconstruction of 3D EM Images with Unsupervised Degradation Learning

The isotropic reconstruction of 3D electron microscopy (EM) images with low axial resolution is of great importance for biological analysis. Existing deep learning-based methods rely on handcrafted down-scaled training data, which does not model the real degradation accurately and thus leads to unsatisfying performance in practice. To address this problem, we propose a universal and unsupervised framework to simultaneously learn the real axial degradation and the isotropic reconstruction of 3D EM images. First, we train a degradation network using unpaired low-resolution (LR) and high-resolution (HR) slices, both of which are from real data, in an adversarial manner. Then, the degradation network is further used to generate realistic LR data from HR labels to form paired training data. In this way, the generated degraded data is consistent with the real axial degradation process, which guarantees the generalization ability of subsequent reconstruction networks to the real data. Our framework has the flexibility to work with different existing reconstruction methods. Experiments on both simulated and real anisotropic EM images validate the superiority of our framework.