Unsupervised deep learning enables low-dose extreme sparse view HAADF-STEM-EDX tomography reconstruction.
Thorough review on unsupervised deep learning methods including self-supervised methods and generative model-based methods for biological image reconstruction and enhancement is provided.
Unsupervised deep learning for simultaneous super-resolution and motion artifact removal of diffusion-weighted MRI scans is proposed.
Unsupervised missing-cone resolving method is proposed in the context of ODT.