TY - GEN
T1 - K-cluster based reconstruction for Compressive Sensing
AU - Xu, Mai
AU - Lu, Jianhua
PY - 2011
Y1 - 2011
N2 - In this paper, we extend the existing CS by including the prior knowledge of K-cluster valued intensities available for an image. In order to reduce the measurement numbers, we then propose in this paper K-cluster based reconstruction approach for Compressive Sensing (CS), by incorporating the K-means algorithm in recovery algorithm to model the prior of K-cluster valued intensities for digital images. Finally, the performance of conventional CS and K-cluster based CS is evaluated using some natural images and background subtraction images.
AB - In this paper, we extend the existing CS by including the prior knowledge of K-cluster valued intensities available for an image. In order to reduce the measurement numbers, we then propose in this paper K-cluster based reconstruction approach for Compressive Sensing (CS), by incorporating the K-means algorithm in recovery algorithm to model the prior of K-cluster valued intensities for digital images. Finally, the performance of conventional CS and K-cluster based CS is evaluated using some natural images and background subtraction images.
UR - https://www.scopus.com/pages/publications/84555171106
U2 - 10.1109/WCSP.2011.6096751
DO - 10.1109/WCSP.2011.6096751
M3 - 会议稿件
AN - SCOPUS:84555171106
SN - 9781457710100
T3 - 2011 International Conference on Wireless Communications and Signal Processing, WCSP 2011
BT - 2011 International Conference on Wireless Communications and Signal Processing, WCSP 2011
T2 - 2011 International Conference on Wireless Communications and Signal Processing, WCSP 2011
Y2 - 9 November 2011 through 11 November 2011
ER -