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.
Original Manuscript: January 11, 2012
Revised Manuscript: April 5, 2012
Manuscript Accepted: April 10, 2012
Published: June 8, 2012
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)