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Open AccessJournal ArticleDOI

Learning approach to optical tomography

TLDR
A method for imaging 3D phase objects in a tomographic configuration implemented by training an artificial neural network to reproduce the complex amplitude of the experimentally measured scattered light is described.
Abstract
Optical tomography has been widely investigated for biomedical imaging applications. In recent years optical tomography has been combined with digital holography and has been employed to produce high-quality images of phase objects such as cells. In this paper we describe a method for imaging 3D phase objects in a tomographic configuration implemented by training an artificial neural network to reproduce the complex amplitude of the experimentally measured scattered light. The network is designed such that the voxel values of the refractive index of the 3D object are the variables that are adapted during the training process. We demonstrate the method experimentally by forming images of the 3D refractive index distribution of Hela cells.

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Citations
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Journal ArticleDOI

All-optical machine learning using diffractive deep neural networks

TL;DR: 3D-printed D2NNs are created that implement classification of images of handwritten digits and fashion products, as well as the function of an imaging lens at a terahertz spectrum.
Journal ArticleDOI

Quantitative phase imaging in biomedicine

TL;DR: This Review presents the main principles of operation and representative basic and clinical science applications of quantitative phase imaging, and aims to provide a critical and objective overview of this dynamic research field.
Journal ArticleDOI

Lensless computational imaging through deep learning

TL;DR: In this paper, the authors demonstrate that deep neural networks (DNNs) can be trained to solve end-to-end inverse problems in computational imaging, where a DNN was trained to recover phase objects given their propagated intensity diffraction patterns.
Journal ArticleDOI

On the use of deep learning for computational imaging

TL;DR: This paper relates the deep-learning-inspired solutions to the original computational imaging formulation and use the relationship to derive design insights, principles, and caveats of more general applicability, and explores how the machine learning process is aided by the physics of imaging when ill posedness and uncertainties become particularly severe.
References
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Journal ArticleDOI

Learning representations by back-propagating errors

TL;DR: Back-propagation repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector, which helps to represent important features of the task domain.
Book

Neural networks for pattern recognition

TL;DR: This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition, and is designed as a text, with over 100 exercises, to benefit anyone involved in the fields of neural computation and pattern recognition.
Journal ArticleDOI

Introduction to Fourier Optics

Joseph W. Goodman, +1 more
- 01 Apr 1969 - 
TL;DR: The second edition of this respected text considerably expands the original and reflects the tremendous advances made in the discipline since 1968 as discussed by the authors, with a special emphasis on applications to diffraction, imaging, optical data processing, and holography.
Book

Introduction to Fourier optics

TL;DR: The second edition of this respected text considerably expands the original and reflects the tremendous advances made in the discipline since 1968 as discussed by the authors, with a special emphasis on applications to diffraction, imaging, optical data processing, and holography.
Journal ArticleDOI

Sparse MRI: The application of compressed sensing for rapid MR imaging.

TL;DR: Practical incoherent undersampling schemes are developed and analyzed by means of their aliasing interference and demonstrate improved spatial resolution and accelerated acquisition for multislice fast spin‐echo brain imaging and 3D contrast enhanced angiography.
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