Super-exponential method for blur identification and image restoration

Thomas J. Kostas; Laurent M. Mugnier; Aggelos K Katsaggelos; Alan Varteres Sahakian

Super-exponential method for blur identification and image restoration

Thomas J. Kostas^*, Laurent M. Mugnier, Aggelos K Katsaggelos, Alan Varteres Sahakian

^*Corresponding author for this work

Electrical and Computer Engineering

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

4 Scopus citations

Abstract

This paper examines a super-exponential method for blind deconvolution. Possibly non- minimal phase point spread functions (PSFs) are identified. The PSF is assumed to be low pass in nature. No other prior knowledge of the PSF or the original image is necessary to assure convergence of the algorithm. Results are shown using synthetically degraded satellite images in order to demonstrate the accuracy of the PSF estimates. In addition, radiographic images are restored with no knowledge of the PSF of the x-ray imaging system. These experiments suggest a promising application of this algorithm to a variety of blur identification problems.

Original language	English (US)
Title of host publication	Proceedings of SPIE - The International Society for Optical Engineering
Publisher	Society of Photo-Optical Instrumentation Engineers
Pages	921-929
Number of pages	9
Volume	2308
Edition	p 2
ISBN (Print)	081941638X
State	Published - Dec 1 1994
Event	Visual Communications and Image Processing '94 - Chicago, IL, USA Duration: Sep 25 1994 → Sep 29 1994

Other

Other	Visual Communications and Image Processing '94
City	Chicago, IL, USA
Period	9/25/94 → 9/29/94

ASJC Scopus subject areas

Electrical and Electronic Engineering
Condensed Matter Physics

Cite this

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title = "Super-exponential method for blur identification and image restoration",

abstract = "This paper examines a super-exponential method for blind deconvolution. Possibly non- minimal phase point spread functions (PSFs) are identified. The PSF is assumed to be low pass in nature. No other prior knowledge of the PSF or the original image is necessary to assure convergence of the algorithm. Results are shown using synthetically degraded satellite images in order to demonstrate the accuracy of the PSF estimates. In addition, radiographic images are restored with no knowledge of the PSF of the x-ray imaging system. These experiments suggest a promising application of this algorithm to a variety of blur identification problems.",

author = "Kostas, {Thomas J.} and Mugnier, {Laurent M.} and Katsaggelos, {Aggelos K} and Sahakian, {Alan Varteres}",

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publisher = "Society of Photo-Optical Instrumentation Engineers",

edition = "p 2",

note = "Visual Communications and Image Processing '94 ; Conference date: 25-09-1994 Through 29-09-1994",

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Super-exponential method for blur identification and image restoration. / Kostas, Thomas J.; Mugnier, Laurent M.; Katsaggelos, Aggelos K et al.
Proceedings of SPIE - The International Society for Optical Engineering. Vol. 2308 p 2. ed. Society of Photo-Optical Instrumentation Engineers, 1994. p. 921-929.

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

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T1 - Super-exponential method for blur identification and image restoration

AU - Kostas, Thomas J.

AU - Mugnier, Laurent M.

AU - Katsaggelos, Aggelos K

AU - Sahakian, Alan Varteres

PY - 1994/12/1

Y1 - 1994/12/1

N2 - This paper examines a super-exponential method for blind deconvolution. Possibly non- minimal phase point spread functions (PSFs) are identified. The PSF is assumed to be low pass in nature. No other prior knowledge of the PSF or the original image is necessary to assure convergence of the algorithm. Results are shown using synthetically degraded satellite images in order to demonstrate the accuracy of the PSF estimates. In addition, radiographic images are restored with no knowledge of the PSF of the x-ray imaging system. These experiments suggest a promising application of this algorithm to a variety of blur identification problems.

AB - This paper examines a super-exponential method for blind deconvolution. Possibly non- minimal phase point spread functions (PSFs) are identified. The PSF is assumed to be low pass in nature. No other prior knowledge of the PSF or the original image is necessary to assure convergence of the algorithm. Results are shown using synthetically degraded satellite images in order to demonstrate the accuracy of the PSF estimates. In addition, radiographic images are restored with no knowledge of the PSF of the x-ray imaging system. These experiments suggest a promising application of this algorithm to a variety of blur identification problems.

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AN - SCOPUS:0028735862

SN - 081941638X

VL - 2308

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BT - Proceedings of SPIE - The International Society for Optical Engineering

PB - Society of Photo-Optical Instrumentation Engineers

T2 - Visual Communications and Image Processing '94

Y2 - 25 September 1994 through 29 September 1994

ER -

Super-exponential method for blur identification and image restoration

Abstract

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ASJC Scopus subject areas

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