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Proceedings Paper

A Linear Programming Approach To Maximum Entropy Signal Restoration
Author(s): Gary A Mastin; Dennis C. Ghiglia; Richard J. Hanson
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Paper Abstract

In future computing environments where computer resources are abundant, a linear programming (LP) approach to maximum entropy signal/image restoration could have advantages over traditional techniques. A revised simplex LP algorithm with inequality constraints is presented. Dantzig's bounded-variable method is used to express the maximum entropy restoration problem as a LP problem. This is done by approximating the nonlinear objective function with piecewise linear segments, then bounding the variables as a function of the number of segments used. Linear inequality constraints may be used to assure a basic feasible solution. Experimental results with 512-point signals are presented. These include restorations of noisy signals. Problems with as many as 513 equations and 6144 unknowns are demonstrated. The complexity of the LP restoration approach is briefly addressed.

Paper Details

Date Published: 16 December 1988
PDF: 8 pages
Proc. SPIE 0974, Applications of Digital Image Processing XI, (16 December 1988); doi: 10.1117/12.948425
Show Author Affiliations
Gary A Mastin, Sandia National Laboratories (United States)
Dennis C. Ghiglia, Sandia National Laboratories (United States)
Richard J. Hanson, ADI (United States)

Published in SPIE Proceedings Vol. 0974:
Applications of Digital Image Processing XI
Andrew G. Tescher, Editor(s)

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