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Proceedings Paper

Improving sparse representation algorithms for maritime video processing
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Paper Abstract

We present several improvements to published algorithms for sparse image modeling with the goal of improving processing of imagery of small watercraft in littoral environments. The first improvement is to the K-SVD algorithm for training over-complete dictionaries, which are used in sparse representations. It is shown that the training converges significantly faster by incorporating multiple dictionary (i.e., codebook) update stages in each training iteration. The paper also provides several useful and practical lessons learned from our experience with sparse representations. Results of three applications of sparse representation are presented and compared to the state-of-the-art methods; image compression, image denoising, and super-resolution.

Paper Details

Date Published: 8 June 2012
PDF: 13 pages
Proc. SPIE 8365, Compressive Sensing, 836508 (8 June 2012); doi: 10.1117/12.920756
Show Author Affiliations
L. N. Smith, U.S. Naval Research Lab. (United States)
J. M. Nichols, U.S. Naval Research Lab. (United States)
J. R. Waterman, U.S. Naval Research Lab. (United States)
C. C. Olson, Sotera Defense Solutions, Inc. (United States)
K. P. Judd, U.S. Naval Research Lab. (United States)

Published in SPIE Proceedings Vol. 8365:
Compressive Sensing
Fauzia Ahmad, Editor(s)

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