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Denoising infrared maritime imagery using tailored dictionaries via modified K-SVD algorithm |
Applied Optics, Vol. 51, Issue 17, pp. 3941-3949 (2012)
http://dx.doi.org/10.1364/AO.51.003941
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Abstract
Recent work has shown that tailored overcomplete dictionaries can provide a better image model than standard basis functions for a variety of image processing tasks. Here we propose a modified K-SVD dictionary learning algorithm designed to maintain the advantages of the original approach but with a focus on improved convergence. We then use the learned model to denoise infrared maritime imagery and compare the performance to the original K-SVD algorithm, several overcomplete “fixed” dictionaries, and a standard wavelet denoising algorithm. Results indicate the superiority of overcomplete representations and show that our tailored approach provides similar peak signal-to-noise ratios as the traditional K-SVD at roughly half the computational cost.
OCIS Codes
(100.2000) Image processing : Digital image processing
(100.2980) Image processing : Image enhancement
(100.3020) Image processing : Image reconstruction-restoration
ToC Category:
Image Processing
History
Original Manuscript: January 11, 2012
Revised Manuscript: April 5, 2012
Manuscript Accepted: April 10, 2012
Published: June 8, 2012
Citation
L. N. Smith, C. C. Olson, K. P. Judd, and J. M. Nichols, "Denoising infrared maritime imagery using tailored dictionaries via modified K-SVD algorithm," Appl. Opt. 51, 3941-3949 (2012)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-51-17-3941
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