Deep learning-based super-resolution in coherent imaging systems

by   Tairan Liu, et al.

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.



There are no comments yet.


page 3

page 4

page 7

page 11

page 12

page 13

page 14

page 15


MRI Super-Resolution with Ensemble Learning and Complementary Priors

Magnetic resonance imaging (MRI) is a widely used medical imaging modali...

A deep learning framework for morphologic detail beyond the diffraction limit in infrared spectroscopic imaging

Infrared (IR) microscopes measure spectral information that quantifies m...

Accurate and Robust Deep Learning Framework for Solving Wave-Based Inverse Problems in the Super-Resolution Regime

We propose an end-to-end deep learning framework that comprehensively so...

Deep learning-based color holographic microscopy

We report a framework based on a generative adversarial network (GAN) th...

SPI-GAN: Towards Single-Pixel Imaging through Generative Adversarial Network

Single-pixel imaging is a novel imaging scheme that has gained popularit...

A self-adapting super-resolution structures framework for automatic design of GAN

With the development of deep learning, the single super-resolution image...

Super-Resolution Image Reconstruction Based on Self-Calibrated Convolutional GAN

With the effective application of deep learning in computer vision, brea...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.