Deep learning-based super-resolution in coherent imaging systems

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. We experimentally validated the capabilities of this deep learning-based coherent imaging approach by super-resolving complex 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.

arXiv comment18 pages, 9 figures, 3 tables

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• Deep learning-based color holographic microscopy
• Adaptive 3D descattering with a dynamic synthesis network
• Small-brain neural networks rapidly solve inverse problems with vortex Fourier encoders
• LensPlus: A High Space-bandwidth Optical Imaging Technique