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Proceedings Paper

Grouped coordinate descent algorithms for robust edge-preserving image restoration
Author(s): Jeffrey A. Fessler
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Paper Abstract

We present a new class of algorithms for edge-preserving restoration of piecewise-smooth images measured in non- Gaussian noise under shift-variant blur. The algorithms are based on minimizing a regularized objective function, and are guaranteed to monotonically decrease the objective function. The algorithms are derived by using a combination of two previously unconnected concepts: A. De Pierro's convexity technique for optimization transfer, and P. Huber's iteration for M-estimation. Convergence to the unique global minimum is guaranteed for strictly convex objective functions. The convergence rate is very fast relative to conventional gradient-based iterations. The proposed algorithms are flexibly parallelizable, and easily accommodate non-negativity constraints and arbitrary neighborhood structures. Implementation in Matlab is remarkably simple, requiring no cumbersome line searches or tolerance parameters.

Paper Details

Date Published: 31 October 1997
PDF: 11 pages
Proc. SPIE 3170, Image Reconstruction and Restoration II, (31 October 1997); doi: 10.1117/12.279713
Show Author Affiliations
Jeffrey A. Fessler, Univ. of Michigan (United States)

Published in SPIE Proceedings Vol. 3170:
Image Reconstruction and Restoration II
Timothy J. Schulz, Editor(s)

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