HyLFM-Net-stat

statically trained HyLFM-Net

This network was trained on light-field and corresponding light-sheet data acquired with a HyLFM microscope—a hybrid light-field light-sheet microscope. For a description of the implementation and information about the microscope please refer to "Deep learning-enhanced light-field imaging with continuous validation." at https://doi.org/10.1038/s41592-021-01136-0. For implementation details and how to train such networks please additionally refer to https://github.com/kreshuklab/hylfm-net.

This network is not expected to transfer to data acquired with a different microscope. Even if your light-field data recorded medaka heart nuclei with a 20x objective represented with $n_{num}=19$, this model might not yield faithful reconstructions.

With a HyLFM microscope continuous validation is enabled by the acquisition of groundtruth-like light-sheet planes. This network should only be used in such a hybrid optical setup to ensure its reconstruction bias toward Medaka heart nuclei is justified and the implicitly learned noise and scattering distributions are applicable.