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Proceedings Paper

Regularized image restoration using sequential and parallel architectures
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Paper Abstract

This paper presents discrete thresholded binary networks of the Hopfield-type as feasible configurations to perform image restoration with regularization. The typically large scale nature of image data processing is handled by partitioning these structures and adopting sequential or parallel update strategies on the partitions one at a time. Among the advantages of such architectures are the ability to efficiently utilize space- bandwidth constrained resources, obviate the need for zero self- feedback connections in sequential procedures and diminish the likelihood of limit cycling in parallel approaches. In the case of image data corrupted by blurring and AWGN, the least squares solution is attained in stages by switching between partitions to force energy descent. Two forms of partitioning have been discussed. The partial neuron decomposition is seen to be more efficient than the partial data strategy. Further, parallel update procedures are more practical from an electro-optical standpoint. The paper demonstrates the viability of these architectures through suitable examples.

Paper Details

Date Published: 24 October 1997
PDF: 9 pages
Proc. SPIE 3162, Advanced Signal Processing: Algorithms, Architectures, and Implementations VII, (24 October 1997); doi: 10.1117/12.279492
Show Author Affiliations
Ramakrishnan Sundaram, Information Systems, Inc. (United States)

Published in SPIE Proceedings Vol. 3162:
Advanced Signal Processing: Algorithms, Architectures, and Implementations VII
Franklin T. Luk, Editor(s)

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