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<it>K</it>-cluster-valued compressive sensing for imaging

By Xu Mai and Lu Jianhua


<p>Abstract</p> <p>The success of compressive sensing (CS) implies that an image can be compressed directly into acquisition with the measurement number over the whole image less than pixel number of the image. In this paper, we extend the existing CS by including the prior knowledge of <it>K</it>-cluster values available for the pixels or wavelet coefficients of an image. In order to model such prior knowledge, we propose in this paper <it>K</it>-cluster-valued CS approach for imaging, by incorporating the <it>K</it>-means algorithm in CoSaMP recovery algorithm. One significant advantage of the proposed approach, rather than the conventional CS, is the capability of reducing measurement numbers required for the accurate image reconstruction. Finally, the performance of conventional CS and <it>K</it>-cluster-valued CS is evaluated using some natural images and background subtraction images.</p

Topics: compressive sensing, <it>K</it>-means algorithm, model-based method, Electrical engineering. Electronics. Nuclear engineering, TK1-9971, Technology, T, DOAJ:Electrical and Nuclear Engineering, DOAJ:Technology and Engineering, Telecommunication, TK5101-6720, Electronics, TK7800-8360
Publisher: Springer
Year: 2011
OAI identifier: oai:doaj.org/article:32beb956169345a58a56345880d40418
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