Unsupervised Deep Learning Methods for Biological Image Reconstruction and Enhancement [article]

Mehmet Akçakaya, Burhaneddin Yaman, Hyungjin Chung, Jong Chul Ye
2021 arXiv   pre-print
Recently, deep learning approaches have become the main research frontier for biological image reconstruction and enhancement problems thanks to their high performance, along with their ultra-fast inference times. However, due to the difficulty of obtaining matched reference data for supervised learning, there has been increasing interest in unsupervised learning approaches that do not need paired reference data. In particular, self-supervised learning and generative models have been
more » ... y used for various biological imaging applications. In this paper, we overview these approaches from a coherent perspective in the context of classical inverse problems, and discuss their applications to biological imaging, including electron, fluorescence and deconvolution microscopy, optical diffraction tomography and functional neuroimaging.
arXiv:2105.08040v2 fatcat:56gnjk7y45a7jifx4s6npb6zxy