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Deconvolution microscopy has been extensively used to improve the resolutionof the widefield fluorescent microscopy. Conventional approaches, which usuallyrequire the point spread function (PSF) measurement or blind estimation, arehowever computationally expensive. Recently, CNN based approaches have beenexplored as a fast and high performance alternative. In this paper, we presenta novel unsupervised deep neural network for blind deconvolution based on cycleconsistency and PSF modeling layers. In contrast to the recent CNN approachesfor similar problem, the explicit PSF modeling layers improve the robustness ofthe algorithm. Experimental results confirm the efficacy of the algorithm.

Blind Deconvolution Microscopy Using Cycle Consistent CNN with Explicit PSF Layer