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Learning Optimal Phase-Coded Aperture for Depth of Field Extension

Tutkimustuotosvertaisarvioitu

Standard

Learning Optimal Phase-Coded Aperture for Depth of Field Extension. / Akpinar, Ugur; Sahin, Erdem; Gotchev, Atanas.

2019 IEEE International Conference on Image Processing (ICIP). IEEE, 2019. s. 4315-4319 (IEEE International Conference on Image Processing).

Tutkimustuotosvertaisarvioitu

Harvard

Akpinar, U, Sahin, E & Gotchev, A 2019, Learning Optimal Phase-Coded Aperture for Depth of Field Extension. julkaisussa 2019 IEEE International Conference on Image Processing (ICIP). IEEE International Conference on Image Processing, IEEE, Sivut 4315-4319, IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, 1/01/00. https://doi.org/10.1109/ICIP.2019.8803419

APA

Akpinar, U., Sahin, E., & Gotchev, A. (2019). Learning Optimal Phase-Coded Aperture for Depth of Field Extension. teoksessa 2019 IEEE International Conference on Image Processing (ICIP) (Sivut 4315-4319). (IEEE International Conference on Image Processing). IEEE. https://doi.org/10.1109/ICIP.2019.8803419

Vancouver

Akpinar U, Sahin E, Gotchev A. Learning Optimal Phase-Coded Aperture for Depth of Field Extension. julkaisussa 2019 IEEE International Conference on Image Processing (ICIP). IEEE. 2019. s. 4315-4319. (IEEE International Conference on Image Processing). https://doi.org/10.1109/ICIP.2019.8803419

Author

Akpinar, Ugur ; Sahin, Erdem ; Gotchev, Atanas. / Learning Optimal Phase-Coded Aperture for Depth of Field Extension. 2019 IEEE International Conference on Image Processing (ICIP). IEEE, 2019. Sivut 4315-4319 (IEEE International Conference on Image Processing).

Bibtex - Lataa

@inproceedings{02b5130a835946ba91ee5680601bea7f,
title = "Learning Optimal Phase-Coded Aperture for Depth of Field Extension",
abstract = "We present a learning-based optimization framework for depth of field extension, combining rigorous modeling of coded aperture imaging system and convolutional neural network based deblurring. The coded mask discretization is defined for desired depth range using wave optics based imaging model. Such approach significantly decreases the number of parameters to be optimized and increases the convergence speed of the network. We verify the proposed algorithm in different scenarios achieving superior or comparable performance with respect to existing methods.",
keywords = "Lenses, Apertures, Cameras, Convolution, Optimization, Optics, Computational imaging, Image deblurring, Neural network",
author = "Ugur Akpinar and Erdem Sahin and Atanas Gotchev",
year = "2019",
month = "9",
doi = "10.1109/ICIP.2019.8803419",
language = "English",
isbn = "978-1-5386-6250-2",
series = "IEEE International Conference on Image Processing",
publisher = "IEEE",
pages = "4315--4319",
booktitle = "2019 IEEE International Conference on Image Processing (ICIP)",

}

RIS (suitable for import to EndNote) - Lataa

TY - GEN

T1 - Learning Optimal Phase-Coded Aperture for Depth of Field Extension

AU - Akpinar, Ugur

AU - Sahin, Erdem

AU - Gotchev, Atanas

PY - 2019/9

Y1 - 2019/9

N2 - We present a learning-based optimization framework for depth of field extension, combining rigorous modeling of coded aperture imaging system and convolutional neural network based deblurring. The coded mask discretization is defined for desired depth range using wave optics based imaging model. Such approach significantly decreases the number of parameters to be optimized and increases the convergence speed of the network. We verify the proposed algorithm in different scenarios achieving superior or comparable performance with respect to existing methods.

AB - We present a learning-based optimization framework for depth of field extension, combining rigorous modeling of coded aperture imaging system and convolutional neural network based deblurring. The coded mask discretization is defined for desired depth range using wave optics based imaging model. Such approach significantly decreases the number of parameters to be optimized and increases the convergence speed of the network. We verify the proposed algorithm in different scenarios achieving superior or comparable performance with respect to existing methods.

KW - Lenses

KW - Apertures

KW - Cameras

KW - Convolution

KW - Optimization

KW - Optics

KW - Computational imaging

KW - Image deblurring

KW - Neural network

U2 - 10.1109/ICIP.2019.8803419

DO - 10.1109/ICIP.2019.8803419

M3 - Conference contribution

SN - 978-1-5386-6250-2

T3 - IEEE International Conference on Image Processing

SP - 4315

EP - 4319

BT - 2019 IEEE International Conference on Image Processing (ICIP)

PB - IEEE

ER -