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Blind Deconvolution Microscopy Using Cycle Consistent CNN with Explicit PSF Layer

  • Sungjun Lim
  • Jong Chul YeEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11905)

Abstract

Deconvolution microscopy has been extensively used to improve the resolution of the widefield fluorescent microscopy. Conventional approaches, which usually require the point spread function (PSF) measurement or blind estimation, are however computationally expensive. Recently, CNN based approaches have been explored as a fast and high performance alternative. In this paper, we present a novel unsupervised deep neural network for blind deconvolution based on cycle consistency and PSF modeling layers. In contrast to the recent CNN approaches for similar problem, the explicit PSF modeling layers improve the robustness of the algorithm. Experimental results confirm the efficacy of the algorithm.

Keywords

Microscopy Image reconstruction Machine learning 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.KAIST Institute for Artificial IntelligenceDaejeonRepublic of Korea
  2. 2.Department of Bio/Brain EngineeringKAISTDaejeonRepublic of Korea

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