TY - GEN
T1 - Compressed sensing using a gaussian scale mixtures model in wavelet domain
AU - Kim, Yookyung
AU - Nadar, Mariappan S.
AU - Bilgin, Ali
PY - 2010
Y1 - 2010
N2 - Compressed Sensing (CS) theory has gained attention recently as an alternative to the current paradigm of sampling followed by compression. Early CS recovery techniques operated under the implicit assumption that the transform coefficients in the sparsity domain are independently distributed. Recent works, however, demonstrated that exploiting the statistical dependencies between transform coefficients can further improve the recovery performance of CS. In this paper, we propose the use of a Gaussian Scale Mixtures (GSM) model in CS. This model can efficiently exploit the statistical dependencies between wavelet coefficients during CS recovery. The proposed model is incorporated into several recent CS techniques including Reweighted l1 minimization (RL1), Iteratively Reweighted Least Squares (IRLS), and Iterative Hard Thresholding (IHT). Experimental results show that the proposed method improves reconstruction quality for a given number of measurements or requires fewer measurements for a desired reconstruction quality.
AB - Compressed Sensing (CS) theory has gained attention recently as an alternative to the current paradigm of sampling followed by compression. Early CS recovery techniques operated under the implicit assumption that the transform coefficients in the sparsity domain are independently distributed. Recent works, however, demonstrated that exploiting the statistical dependencies between transform coefficients can further improve the recovery performance of CS. In this paper, we propose the use of a Gaussian Scale Mixtures (GSM) model in CS. This model can efficiently exploit the statistical dependencies between wavelet coefficients during CS recovery. The proposed model is incorporated into several recent CS techniques including Reweighted l1 minimization (RL1), Iteratively Reweighted Least Squares (IRLS), and Iterative Hard Thresholding (IHT). Experimental results show that the proposed method improves reconstruction quality for a given number of measurements or requires fewer measurements for a desired reconstruction quality.
KW - Compressed sensing
KW - Gaussian scale mixtures
KW - Wavelets
UR - http://www.scopus.com/inward/record.url?scp=78651087356&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78651087356&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2010.5652744
DO - 10.1109/ICIP.2010.5652744
M3 - Conference contribution
AN - SCOPUS:78651087356
SN - 9781424479948
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 3365
EP - 3368
BT - 2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings
T2 - 2010 17th IEEE International Conference on Image Processing, ICIP 2010
Y2 - 26 September 2010 through 29 September 2010
ER -