https://tristazeng.github.io/ZS-DeconvNet-page/

Zero-shot deconvolution network (ZS-DeconvNet) is a deep learning-based computational super-resolution approach, which fully exploits the powerful feature representation and noise suppression capabilities of deep neural networks in an unsupervised manner and can instantly enhance the resolution of microscope images by more than 1.5-fold over the diffraction limit with 10-fold lower fluorescence than ordinary SR imaging conditions without the need for any training data or data priors such as sparsity and temporal continuity.

https://www.nature.com/articles/s41467-024-48575-9