Sparsity based super-resolution in optical measurements

Yoav Shechtman; Alexander Szameit; Eliyahu Osherovich; Pavel Sidorenko; Oren Cohen; Yonina C. Eldar; Mordechai Segev

doi:10.1109/CAMSAP.2013.6714043

Sparsity based super-resolution in optical measurements

Yoav Shechtman, Alexander Szameit, Eliyahu Osherovich, Pavel Sidorenko, Oren Cohen, Yonina C. Eldar, Mordechai Segev

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

We propose and experimentally demonstrate a method of exploiting prior knowledge of a signal's sparsity to perform super-resolution in various optical measurements, including: single-shot sub-wavelength Coherent Diffractive Imaging (CDI), i.e. algorithmic object reconstruction from Fourier amplitude measurements, and ultra-fast pulse measurement, i.e. exceeding the temporal resolution imposed by the rise time of the photodiode. The prior knowledge of the signal's sparsity compensates for the loss of phase information and the loss of high spatial frequencies in the case of CDI, and for the loss of temporal frequencies accompanying the photodiode measurement process.

Original language	English
Title of host publication	2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013
Pages	204-207
Number of pages	4
DOIs	https://doi.org/10.1109/CAMSAP.2013.6714043
State	Published - 2013
Externally published	Yes
Event	2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013 - Saint Martin, France Duration: 15 Dec 2013 → 18 Dec 2013

Publication series

Name	2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013

Conference

Conference	2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013
Country/Territory	France
City	Saint Martin
Period	15/12/13 → 18/12/13

Access to Document

10.1109/CAMSAP.2013.6714043

Cite this

Shechtman, Y., Szameit, A., Osherovich, E., Sidorenko, P., Cohen, O., Eldar, Y. C., & Segev, M. (2013). Sparsity based super-resolution in optical measurements. In 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013 (pp. 204-207). [6714043] (2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013). https://doi.org/10.1109/CAMSAP.2013.6714043

Shechtman, Yoav ; Szameit, Alexander ; Osherovich, Eliyahu et al. / Sparsity based super-resolution in optical measurements. 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013. 2013. pp. 204-207 (2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013).

@inproceedings{437749253c7a497a8c85612117f0b637,

title = "Sparsity based super-resolution in optical measurements",

abstract = "We propose and experimentally demonstrate a method of exploiting prior knowledge of a signal's sparsity to perform super-resolution in various optical measurements, including: single-shot sub-wavelength Coherent Diffractive Imaging (CDI), i.e. algorithmic object reconstruction from Fourier amplitude measurements, and ultra-fast pulse measurement, i.e. exceeding the temporal resolution imposed by the rise time of the photodiode. The prior knowledge of the signal's sparsity compensates for the loss of phase information and the loss of high spatial frequencies in the case of CDI, and for the loss of temporal frequencies accompanying the photodiode measurement process.",

author = "Yoav Shechtman and Alexander Szameit and Eliyahu Osherovich and Pavel Sidorenko and Oren Cohen and Eldar, {Yonina C.} and Mordechai Segev",

year = "2013",

doi = "10.1109/CAMSAP.2013.6714043",

language = "英语",

isbn = "9781467331463",

series = "2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013",

pages = "204--207",

booktitle = "2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013",

note = "null ; Conference date: 15-12-2013 Through 18-12-2013",

}

Shechtman, Y, Szameit, A, Osherovich, E, Sidorenko, P, Cohen, O, Eldar, YC & Segev, M 2013, Sparsity based super-resolution in optical measurements. in 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013., 6714043, 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013, pp. 204-207, 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013, Saint Martin, France, 15/12/13. https://doi.org/10.1109/CAMSAP.2013.6714043

Sparsity based super-resolution in optical measurements. / Shechtman, Yoav; Szameit, Alexander; Osherovich, Eliyahu et al.

2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013. 2013. p. 204-207 6714043 (2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Sparsity based super-resolution in optical measurements

AU - Shechtman, Yoav

AU - Szameit, Alexander

AU - Osherovich, Eliyahu

AU - Sidorenko, Pavel

AU - Cohen, Oren

AU - Eldar, Yonina C.

AU - Segev, Mordechai

PY - 2013

Y1 - 2013

N2 - We propose and experimentally demonstrate a method of exploiting prior knowledge of a signal's sparsity to perform super-resolution in various optical measurements, including: single-shot sub-wavelength Coherent Diffractive Imaging (CDI), i.e. algorithmic object reconstruction from Fourier amplitude measurements, and ultra-fast pulse measurement, i.e. exceeding the temporal resolution imposed by the rise time of the photodiode. The prior knowledge of the signal's sparsity compensates for the loss of phase information and the loss of high spatial frequencies in the case of CDI, and for the loss of temporal frequencies accompanying the photodiode measurement process.

AB - We propose and experimentally demonstrate a method of exploiting prior knowledge of a signal's sparsity to perform super-resolution in various optical measurements, including: single-shot sub-wavelength Coherent Diffractive Imaging (CDI), i.e. algorithmic object reconstruction from Fourier amplitude measurements, and ultra-fast pulse measurement, i.e. exceeding the temporal resolution imposed by the rise time of the photodiode. The prior knowledge of the signal's sparsity compensates for the loss of phase information and the loss of high spatial frequencies in the case of CDI, and for the loss of temporal frequencies accompanying the photodiode measurement process.

UR - http://www.scopus.com/inward/record.url?scp=84894157831&partnerID=8YFLogxK

U2 - 10.1109/CAMSAP.2013.6714043

DO - 10.1109/CAMSAP.2013.6714043

M3 - 会议稿件

AN - SCOPUS:84894157831

SN - 9781467331463

T3 - 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013

SP - 204

EP - 207

BT - 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013

Y2 - 15 December 2013 through 18 December 2013

ER -

Shechtman Y, Szameit A, Osherovich E, Sidorenko P, Cohen O, Eldar YC et al. Sparsity based super-resolution in optical measurements. In 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013. 2013. p. 204-207. 6714043. (2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013). doi: 10.1109/CAMSAP.2013.6714043

Sparsity based super-resolution in optical measurements

Abstract

Publication series

Conference

Access to Document

Other files and links

Fingerprint

Cite this