GedankenNet: Self-supervised learning of hologram reconstruction using physics consistency. (arXiv:2209.08288v1 [cs.CV])

The past decade has witnessed transformative applications of deep learning in
various computational imaging, sensing and microscopy tasks. Due to the
supervised learning schemes employed, most of these methods depend on
large-scale, diverse, and labeled training data. The acquisition and
preparation of such training image datasets are often laborious and costly,
also leading to biased estimation and limited generalization to new types of
samples. Here, we report a self-supervised learning model, termed GedankenNet,
that eliminates the need for labeled or …

arxiv hologram physics self-supervised learning supervised learning