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Proceedings Paper

Two-dimensional signal deconvolution: design issues related to a novel multisensor-based approach
Author(s): Nicholaos D. Sidiropoulos; John S. Baras; Carlos A. Berenstein
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Paper Abstract

Recent results of analysis in several complex variables are employed to come up with a set of compactly supported approximate deconvolution kernels for the reconstruction of a two- dimensional signal based on multiple linearly degraded versions of the signal with a family of kernels that satisfies suitable technical conditions. The question of convergence of the proposed deconvolution kernels are discussed, simulation results that demonstrate the gain in bandwidth are presented, and two data parallel grid layouts for the off-line computation of the deconvolution kernels are proposed.

Paper Details

Date Published: 1 October 1991
PDF: 11 pages
Proc. SPIE 1569, Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision, (1 October 1991); doi: 10.1117/12.48393
Show Author Affiliations
Nicholaos D. Sidiropoulos, Univ. of Maryland (United States)
John S. Baras, Univ. of Maryland (United States)
Carlos A. Berenstein, Univ. of Maryland (United States)


Published in SPIE Proceedings Vol. 1569:
Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision
Su-Shing Chen, Editor(s)

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