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8 June 2012 Improving sparse representation algorithms for maritime video processing
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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.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
L. N. Smith, J. M. Nichols, J. R. Waterman, C. C. Olson, and K. P. Judd "Improving sparse representation algorithms for maritime video processing", Proc. SPIE 8365, Compressive Sensing, 836508 (8 June 2012); https://doi.org/10.1117/12.920756
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