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)


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.



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


  1. 1.
    Ba, J.L., et al.: Layer normalization. arXiv:1607.06450 (2016)
  2. 2.
    Chen, Q., Koltun, V.: Photographic image synthesis with cascaded refinement networks. In: ICCV (2017)Google Scholar
  3. 3.
    Dong, C., et al.: Image super-resolution using deep convolutional networks. TPAMI (2015)Google Scholar
  4. 4.
    Duchon, C.E.: Lanczos filtering in one and two dimensions. J. Appl. Meteorol. 18, 1016–1022 (1979)CrossRefGoogle Scholar
  5. 5.
    Freedman, G., Fattal, R.: Image and video upscaling from local self-examples. In: ACM TOG (2011)Google Scholar
  6. 6.
    Goodfellow, I., et al.: Generative adversarial nets. In: NIPS (2014)Google Scholar
  7. 7.
    Isola, P., et al.: Image-to-image translation with conditional adversarial networks. In: CVPR (2017)Google Scholar
  8. 8.
    Keys, R.: Cubic convolution interpolation for digital image processing. In: TASSP (1981)Google Scholar
  9. 9.
    Kim, J., Lee, J.K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In: CVPR (2016)Google Scholar
  10. 10.
    Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2015)Google Scholar
  11. 11.
    Lai, W.S., et al.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: CVPR (2017)Google Scholar
  12. 12.
    Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: CVPR (2017)Google Scholar
  13. 13.
    Maas, A.L., Hannun, A.Y., Ng, A.Y.: Rectifier nonlinearities improve neural network acoustic models. In: ICML (2013)Google Scholar
  14. 14.
    Odena, A., Dumoulin, V., Olah, C.: Deconvolution and checkerboard artifacts. In: Distill (2016)Google Scholar
  15. 15.
    Shi, W., et al.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: CVPR (2016)Google Scholar
  16. 16.
    Su, H., et al.: Cell segmentation in phase contrast microscopy images via semi-supervised classification over optics-related features. In: Medical Image Analysis (2013)Google Scholar
  17. 17.
    Timofte, R., De Smet, V., Van Gool, L.: Anchored neighborhood regression for fast example-based super-resolution. In: ICCV (2013)Google Scholar
  18. 18.
    Yang, J., Lin, Z., Cohen, S.: Fast image super-resolution based on in-place example regression. In: CVPR (2013)Google Scholar
  19. 19.
    Zernike, F.: How I discovered phase contrast. Science 121, 345–349 (1955)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

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

Personalised recommendations