  Image denoising or artefact removal using deep learning is possible in the
availability of supervised training dataset acquired in real experiments or
synthesized using known noise models. Neither of the conditions can be
fulfilled for nanoscopy (super-resolution optical microscopy) images that are
generated from microscopy videos through statistical analysis techniques. Due
to several physical constraints, supervised dataset cannot be measured. Due to
non-linear spatio-temporal mixing of data and valuable statistics of
fluctuations from fluorescent molecules which compete with noise statistics,
noise or artefact models in nanoscopy images cannot be explicitly learnt.
Therefore, such problem poses unprecedented challenges to deep learning. Here,
we propose a robust and versatile simulation-supervised training approach of
deep learning auto-encoder architectures for the highly challenging nanoscopy
images of sub-cellular structures inside biological samples. We show the proof
of concept for one nanoscopy method and investigate the scope of
generalizability across structures, noise models, and nanoscopy algorithms not
included during simulation-supervised training. We also investigate a variety
of loss functions and learning models and discuss the limitation of existing
performance metrics for nanoscopy images. We generate valuable insights for
this highly challenging and unsolved problem in nanoscopy, and set the
foundation for application of deep learning problems in nanoscopy for life
sciences.
