Learning optimal parameters for binary sensing image reconstruction algorithms

Renan A. Rojas; Wangyu Luo; Victor Murray; Yue M. Lu

doi:10.1109/ICIP.2017.8296791

Learning optimal parameters for binary sensing image reconstruction algorithms

Renan A. Rojas
, Wangyu Luo
, Victor Murray
, Yue M. Lu

University of Engineering and Technology UTEC
Harvard University
The University of New Mexico

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

13 Scopus citations

Abstract

A novel data-driven reconstruction algorithm for quantum image sensors is proposed. Binary observations are efficiently decoded by modeling the reconstruction structure as a two-layer neural network, where optimal coefficients are obtained via error backpropagation. Such a model encapsulates the structure of state-of-the-art algorithms, yet it presents a considerably faster alternative which adapts to input examples without a priori statistical information. Simulations on natural and synthetic datasets show accurate reconstructions with structural similarities consistent with the state of the art, while requiring approximately 5 times less computational cost.

Original language	English
Title of host publication	2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings
Publisher	IEEE Computer Society
Pages	2791-2795
Number of pages	5
ISBN (Electronic)	9781509021758
DOIs	https://doi.org/10.1109/ICIP.2017.8296791
State	Published - 2 Jul 2017
Externally published	Yes
Event	24th IEEE International Conference on Image Processing, ICIP 2017 - Beijing, China Duration: 17 Sep 2017 → 20 Sep 2017

Publication series

Name	Proceedings - International Conference on Image Processing, ICIP
Volume	2017-September
ISSN (Print)	1522-4880

Conference

Conference	24th IEEE International Conference on Image Processing, ICIP 2017
Country/Territory	China
City	Beijing
Period	17/09/17 → 20/09/17

Keywords

Anscombe transform
Error backpropagation
Image reconstruction
MLE
Quanta image sensors

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10.1109/ICIP.2017.8296791

Cite this

Rojas, R. A., Luo, W., Murray, V., & Lu, Y. M. (2017). Learning optimal parameters for binary sensing image reconstruction algorithms. In 2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings (pp. 2791-2795). (Proceedings - International Conference on Image Processing, ICIP; Vol. 2017-September). IEEE Computer Society. https://doi.org/10.1109/ICIP.2017.8296791

@inproceedings{2dd274d795894ead86bd2fc85ff8a460,

title = "Learning optimal parameters for binary sensing image reconstruction algorithms",

abstract = "A novel data-driven reconstruction algorithm for quantum image sensors is proposed. Binary observations are efficiently decoded by modeling the reconstruction structure as a two-layer neural network, where optimal coefficients are obtained via error backpropagation. Such a model encapsulates the structure of state-of-the-art algorithms, yet it presents a considerably faster alternative which adapts to input examples without a priori statistical information. Simulations on natural and synthetic datasets show accurate reconstructions with structural similarities consistent with the state of the art, while requiring approximately 5 times less computational cost.",

keywords = "Anscombe transform, Error backpropagation, Image reconstruction, MLE, Quanta image sensors",

author = "Rojas, \{Renan A.\} and Wangyu Luo and Victor Murray and Lu, \{Yue M.\}",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 24th IEEE International Conference on Image Processing, ICIP 2017 ; Conference date: 17-09-2017 Through 20-09-2017",

year = "2017",

month = jul,

day = "2",

doi = "10.1109/ICIP.2017.8296791",

language = "English",

series = "Proceedings - International Conference on Image Processing, ICIP",

publisher = "IEEE Computer Society",

pages = "2791--2795",

booktitle = "2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings",

}

Rojas, RA, Luo, W, Murray, V & Lu, YM 2017, Learning optimal parameters for binary sensing image reconstruction algorithms. in 2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings. Proceedings - International Conference on Image Processing, ICIP, vol. 2017-September, IEEE Computer Society, pp. 2791-2795, 24th IEEE International Conference on Image Processing, ICIP 2017, Beijing, China, 17/09/17. https://doi.org/10.1109/ICIP.2017.8296791

Learning optimal parameters for binary sensing image reconstruction algorithms. / Rojas, Renan A.; Luo, Wangyu; Murray, Victor et al.
2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings. IEEE Computer Society, 2017. p. 2791-2795 (Proceedings - International Conference on Image Processing, ICIP; Vol. 2017-September).

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

TY - GEN

T1 - Learning optimal parameters for binary sensing image reconstruction algorithms

AU - Rojas, Renan A.

AU - Luo, Wangyu

AU - Murray, Victor

AU - Lu, Yue M.

PY - 2017/7/2

Y1 - 2017/7/2

N2 - A novel data-driven reconstruction algorithm for quantum image sensors is proposed. Binary observations are efficiently decoded by modeling the reconstruction structure as a two-layer neural network, where optimal coefficients are obtained via error backpropagation. Such a model encapsulates the structure of state-of-the-art algorithms, yet it presents a considerably faster alternative which adapts to input examples without a priori statistical information. Simulations on natural and synthetic datasets show accurate reconstructions with structural similarities consistent with the state of the art, while requiring approximately 5 times less computational cost.

AB - A novel data-driven reconstruction algorithm for quantum image sensors is proposed. Binary observations are efficiently decoded by modeling the reconstruction structure as a two-layer neural network, where optimal coefficients are obtained via error backpropagation. Such a model encapsulates the structure of state-of-the-art algorithms, yet it presents a considerably faster alternative which adapts to input examples without a priori statistical information. Simulations on natural and synthetic datasets show accurate reconstructions with structural similarities consistent with the state of the art, while requiring approximately 5 times less computational cost.

KW - Anscombe transform

KW - Error backpropagation

KW - Image reconstruction

KW - MLE

KW - Quanta image sensors

UR - https://www.scopus.com/pages/publications/85045333872

U2 - 10.1109/ICIP.2017.8296791

DO - 10.1109/ICIP.2017.8296791

M3 - Conference contribution

AN - SCOPUS:85045333872

T3 - Proceedings - International Conference on Image Processing, ICIP

SP - 2791

EP - 2795

BT - 2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings

PB - IEEE Computer Society

T2 - 24th IEEE International Conference on Image Processing, ICIP 2017

Y2 - 17 September 2017 through 20 September 2017

ER -

Learning optimal parameters for binary sensing image reconstruction algorithms

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Keywords

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