EM-Based Simultaneous Registration, Restoration, and Interpolation of Super-resolved Images

Nathan A. Woods; Nikolas P. Galatsanos; Aggelos K Katsaggelos

EM-Based Simultaneous Registration, Restoration, and Interpolation of Super-resolved Images

Nathan A. Woods^*, Nikolas P. Galatsanos, Aggelos K Katsaggelos

^*Corresponding author for this work

Electrical and Computer Engineering

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

21 Scopus citations

Abstract

We present a maximum likelihood (ML) solution to the problem of obtaining high-resolution images from sequences of noisy, blurred, and low-resolution images. In our formulation, the registration parameters of the low-resolution images, the degrading blur, and noise variance are unknown. Our algorithm has the advantage that all unknown parameters are obtained simultaneously using all of the available data. An efficient implementation is presented in the frequency domain, based on the Expectation Maximization (EM) algorithm. Simulations demonstrate the effectiveness of the algorithm.

Original language	English (US)
Title of host publication	IEEE International Conference on Image Processing
Pages	303-306
Number of pages	4
Volume	2
State	Published - Dec 17 2003
Event	Proceedings: 2003 International Conference on Image Processing, ICIP-2003 - Barcelona, Spain Duration: Sep 14 2003 → Sep 17 2003

Other

Other	Proceedings: 2003 International Conference on Image Processing, ICIP-2003
Country/Territory	Spain
City	Barcelona
Period	9/14/03 → 9/17/03

ASJC Scopus subject areas

Hardware and Architecture
Computer Vision and Pattern Recognition
Electrical and Electronic Engineering

Cite this

@inproceedings{82d35b6db72847eeb82b06474a79ef89,

title = "EM-Based Simultaneous Registration, Restoration, and Interpolation of Super-resolved Images",

abstract = "We present a maximum likelihood (ML) solution to the problem of obtaining high-resolution images from sequences of noisy, blurred, and low-resolution images. In our formulation, the registration parameters of the low-resolution images, the degrading blur, and noise variance are unknown. Our algorithm has the advantage that all unknown parameters are obtained simultaneously using all of the available data. An efficient implementation is presented in the frequency domain, based on the Expectation Maximization (EM) algorithm. Simulations demonstrate the effectiveness of the algorithm.",

author = "Woods, {Nathan A.} and Galatsanos, {Nikolas P.} and Katsaggelos, {Aggelos K}",

year = "2003",

month = dec,

day = "17",

language = "English (US)",

volume = "2",

pages = "303--306",

booktitle = "IEEE International Conference on Image Processing",

note = "Proceedings: 2003 International Conference on Image Processing, ICIP-2003 ; Conference date: 14-09-2003 Through 17-09-2003",

}

TY - GEN

T1 - EM-Based Simultaneous Registration, Restoration, and Interpolation of Super-resolved Images

AU - Woods, Nathan A.

AU - Galatsanos, Nikolas P.

AU - Katsaggelos, Aggelos K

PY - 2003/12/17

Y1 - 2003/12/17

N2 - We present a maximum likelihood (ML) solution to the problem of obtaining high-resolution images from sequences of noisy, blurred, and low-resolution images. In our formulation, the registration parameters of the low-resolution images, the degrading blur, and noise variance are unknown. Our algorithm has the advantage that all unknown parameters are obtained simultaneously using all of the available data. An efficient implementation is presented in the frequency domain, based on the Expectation Maximization (EM) algorithm. Simulations demonstrate the effectiveness of the algorithm.

AB - We present a maximum likelihood (ML) solution to the problem of obtaining high-resolution images from sequences of noisy, blurred, and low-resolution images. In our formulation, the registration parameters of the low-resolution images, the degrading blur, and noise variance are unknown. Our algorithm has the advantage that all unknown parameters are obtained simultaneously using all of the available data. An efficient implementation is presented in the frequency domain, based on the Expectation Maximization (EM) algorithm. Simulations demonstrate the effectiveness of the algorithm.

UR - http://www.scopus.com/inward/record.url?scp=0345566162&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0345566162&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0345566162

VL - 2

SP - 303

EP - 306

BT - IEEE International Conference on Image Processing

T2 - Proceedings: 2003 International Conference on Image Processing, ICIP-2003

Y2 - 14 September 2003 through 17 September 2003

ER -

EM-Based Simultaneous Registration, Restoration, and Interpolation of Super-resolved Images

Abstract

Other

ASJC Scopus subject areas

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