Single-shot phase imaging with a coded aperture

Horisaki, Ryoichi; Ogura, Yusuke; Aino, Masahiko; Tanida, Jun

doi:10.1364/ol.39.006466

Cited by 107 publications

(45 citation statements)

References 20 publications

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“…Phase is recovered based on multiple images with some type of data diversity that translates phase information into intensity (e.g. defocus [5], illumination coding [31], pupil coding [32]). Here we adopt a pupil-coding scheme where the wavefront at the exit pupil [12] is differently aberrated for each measurement.…”

Section: Methodsmentioning

confidence: 99%

Deep phase decoder: self-calibrating phase microscopy with an untrained deep neural network

Bostan

Heckel

Chen

et al. 2020

Optica

131

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Deep neural networks have emerged as effective tools for computational imaging including quantitative phase microscopy of transparent samples. To reconstruct phase from intensity, current approaches rely on supervised learning with training examples; consequently, their performance is sensitive to a match of training and imaging settings. Here we propose a new approach to phase microscopy by using an untrained deep neural network for measurement formation, encapsulating the image prior and imaging physics. Our approach does not require any training data and simultaneously reconstructs the sought phase and pupil-plane aberrations by fitting the weights of the network to the captured images. To demonstrate experimentally, we reconstruct quantitative phase from through-focus images blindly (i.e. no explicit knowledge of the aberrations).

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Section: Methodsmentioning

confidence: 99%

Deep phase decoder: self-calibrating phase microscopy with an untrained deep neural network

Bostan

Heckel

Chen

et al. 2020

Optica

131

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“…A CA has been used to improve the reconstruction conditions because its Fourier spectrum is significantly extended compared with that of a conventional non-coded aperture. Using CAs, single-shot amplitude imaging with incoherent light and single-shot reference-free DH with coherent light have been demonstrated [18][19][20][21][22][23].…”

Section: 2 Methodsmentioning

confidence: 99%

Single-shot and lensless complex-amplitude imaging with incoherent light based on machine learning

Horisaki

Fujii

Tanida

2018

Opt Rev

Self Cite

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We present a single-shot incoherent light imaging method for simultaneously observing both amplitude and phase without any imaging optics, based on machine learning. In the proposed method, an object with a complex-amplitude field is illuminated with incoherent light and is captured by an image sensor with or without a coded aperture. The complex-amplitude field of the object is reconstructed from a single captured image using a state-of-the-art deep convolutional neural network, which is trained with a large number of input and output pairs. In experimental demonstrations, the proposed method was verified with a handwritten character database, and the effect of a coded aperture printed on an overhead projector film in the reconstruction was examined. Our method has advantages over conventional wavefront sensing techniques using incoherent light, namely simplification of the optical hardware and improved measurement speed. This study shows the importance and practical impact of machine learning techniques in various fields of optical sensing.

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“…Focusing is required to generate the diverging illumination for high magnification and resolution. Note that also for parallel beam propagation imaging it is extremely common to implicitly assume perfect plane wave illumination by performing the conventional flat-field correction [3,[7][8][9][10][11]. In previous studies, we have shown that under these conditions the commonly used standard flat-field correction, i.e.…”

Section: Introductionmentioning

confidence: 99%

Divide and update: towards single-shot object and probe retrieval for near-field holography

Hagemann

Salditt

2017

Opt. Express

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We present a phase reconstruction scheme for X-ray near-field holographic imaging based on a separability constraint for probe and object. In order to achieve this, we have devised an algorithm which requires only two measurements - with and without an object in the beam. This scheme is advantageous if the standard flat-field correction fails and a full ptychographic dataset can not be acquired, since either object or probe are dynamic. The scheme is validated by numerical simulations and by a proof-of-concept experiment using highly focused undulator radiation of the beamline ID16a of the European Synchrotron Radiation Facility (ESRF).

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Single-shot phase imaging with a coded aperture

Cited by 107 publications

References 20 publications

Deep phase decoder: self-calibrating phase microscopy with an untrained deep neural network

Deep phase decoder: self-calibrating phase microscopy with an untrained deep neural network

Single-shot and lensless complex-amplitude imaging with incoherent light based on machine learning

Divide and update: towards single-shot object and probe retrieval for near-field holography

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