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Open Access Research

K-cluster-valued compressive sensing for imaging

Mai Xu* and Jianhua Lu

Author Affiliations

Department of Electronic Engineering, Tsinghua University, Beijing, People's Republic of China

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EURASIP Journal on Advances in Signal Processing 2011, 2011:75  doi:10.1186/1687-6180-2011-75

Published: 26 September 2011

Abstract

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 K-cluster values available for the pixels or wavelet coefficients of an image. In order to model such prior knowledge, we propose in this paper K-cluster-valued CS approach for imaging, by incorporating the K-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 K-cluster-valued CS is evaluated using some natural images and background subtraction images.

Keywords:
compressive sensing; K-means algorithm; model-based method