Deep learning-based super-resolution in coherent imaging systems

TL;DR

This paper introduces a GAN-based deep learning framework that significantly enhances the resolution of various coherent imaging systems, including lensfree holography and lens-based microscopy, by providing a rapid, non-iterative super-resolution method.

Contribution

The paper presents a novel GAN-based super-resolution approach applicable to both pixel-limited and diffraction-limited coherent imaging systems, improving resolution without iterative reconstruction.

Findings

Enhanced resolution of lensfree holographic images

Improved resolution of lens-based holographic systems

Applicable to various coherent imaging modalities

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.