Toward a Thinking Microscope: Deep Learning in Optical Microscopy and Image Reconstruction

05/23/2018
by   Yair Rivenson, et al.
2

We discuss recently emerging applications of the state-of-art deep learning methods on optical microscopy and microscopic image reconstruction, which enable new transformations among different modes and modalities of microscopic imaging, driven entirely by image data. We believe that deep learning will fundamentally change both the hardware and image reconstruction methods used in optical microscopy in a holistic manner.

READ FULL TEXT

page 8

page 9

page 10

page 11

research
05/12/2017

Deep Learning Microscopy

We demonstrate that a deep neural network can significantly improve opti...
research
02/14/2019

On instabilities of deep learning in image reconstruction - Does AI come at a cost?

Deep learning, due to its unprecedented success in tasks such as image c...
research
06/29/2014

PAINTER: a spatio-spectral image reconstruction algorithm for optical interferometry

Astronomical optical interferometers sample the Fourier transform of the...
research
07/14/2017

Spatially variant PSF modeling in confocal macroscopy

Point spread function (PSF) plays an essential role in image reconstruct...
research
05/25/2021

A Geometry-Informed Deep Learning Framework for Ultra-Sparse 3D Tomographic Image Reconstruction

Deep learning affords enormous opportunities to augment the armamentariu...
research
12/30/2014

Holistic random encoding for imaging through multimode fibers

The input numerical aperture (NA) of multimode fiber (MMF) can be effect...
research
05/18/2021

A parameter refinement method for Ptychography based on Deep Learning concepts

X-ray Ptychography is an advanced computational microscopy technique whi...

Please sign up or login with your details

Forgot password? Click here to reset