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Proceedings Paper

Super-exponential method for blur identification and image restoration
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Paper 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.

Paper Details

Date Published: 16 September 1994
PDF: 9 pages
Proc. SPIE 2308, Visual Communications and Image Processing '94, (16 September 1994); doi: 10.1117/12.186036
Show Author Affiliations
Thomas J. Kostas, Northwestern Univ. (United States)
Laurent M. Mugnier, Northwestern Univ. (United States)
Aggelos K. Katsaggelos, Northwestern Univ. (United States)
Alan V. Sahakian, Northwestern Univ. (United States)


Published in SPIE Proceedings Vol. 2308:
Visual Communications and Image Processing '94
Aggelos K. Katsaggelos, Editor(s)

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