Journal
SCIENTIFIC REPORTS
Volume 9, Issue -, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41598-019-40554-1
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Funding
- NSF Engineering Research Center (ERC, PATHS-UP)
- Army Research Office (ARO) [W911NF-13-1-0419, W911NF-13-1-0197]
- ARO Life Sciences Division
- National Science Foundation (NSF) CBET Division Biophotonics Program
- NSF Emerging Frontiers in Research and Innovation (EFRI) Award
- NSF INSPIRE Award
- NSF Partnerships for Innovation: Building Innovation Capacity (PFI:BIC) Program
- National Institutes of Health (NIH) [R21EB023115]
- Howard Hughes Medical Institute (HHMI)
- Vodafone Americas Foundation
- Mary Kay Foundation
- Steven & Alexandra Cohen Foundation
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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.
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