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A Cascaded Refinement GAN for Phase Contrast Microscopy Image Super Resolution

  • Liang Han
  • Zhaozheng YinEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11071)

Abstract

Phase contrast microscopy is a widely-used non-invasive technique for monitoring live cells over time. High-throughput biological experiments expect a wide-view (i.e., a low microscope magnification) to monitor the entire cell population and a high magnification on individual cell’s details, which is hard to achieve simultaneously. In this paper, we propose a cascaded refinement Generative Adversarial Network (GAN) for phase contrast microscopy image super-resolution. Our algorithm uses an optic-related data enhancement and super-resolves a phase contrast microscopy image in a coarse-to-fine fashion, with a new loss function consisting of a content loss and an adversarial loss. The proposed algorithm is both qualitatively and quantitatively evaluated on a dataset of 500 phase contrast microscopy images, showing its superior performance for super-resolving phase contrast microscopy images. The proposed algorithm provides a computational solution on achieving a high magnification on individual cell’s details and a wide-view on cell populations at the same time, which will benefit the microscopy community.

Notes

Acknowledgement

This project was supported by NSF CAREER award IIS-1351049 and NSF EPSCoR grant IIA-1355406.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceMissouri University of Science and TechnologyRollaUSA

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