Deep learning-based super-resolution in coherent imaging systems.

Journal: Scientific reports
Published Date: Mar 8, 2019

Abstract

We present a deep learning framework based on a generative adversarial network (GAN) to perform super-resolution in coherent imaging systems. We demonstrate that this framework can enhance the resolution of both pixel size-limited and diffraction-limited coherent imaging systems. The capabilities of this approach are experimentally validated by super-resolving complex-valued images acquired using a lensfree on-chip holographic microscope, the resolution of which was pixel size-limited. Using the same GAN-based approach, we also improved the resolution of a lens-based holographic imaging system that was limited in resolution by the numerical aperture of its objective lens. This deep learning-based super-resolution framework can be broadly applied to enhance the space-bandwidth product of coherent imaging systems using image data and convolutional neural networks, and provides a rapid, non-iterative method for solving inverse image reconstruction or enhancement problems in optics.

Authors

  • Tairan Liu
    Department of Mechanical Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA.
  • Kevin de Haan
    Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA.
  • Yair Rivenson
    Electrical and Computer Engineering Department, Bioengineering Department, University of California, Los Angeles, CA 90095 USA, and also with the California NanoSystems Institute, University of California, Los Angeles, CA 90095 USA.
  • Zhensong Wei
    Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USA.
  • Xin Zeng
    Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.
  • Yibo Zhang
    Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA.
  • Aydogan Ozcan
    Electrical and Computer Engineering Department, University of California, Los Angeles, California 90095, USA.

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