TY - GEN
T1 - Compressive sensing-based image denoising using adaptive multiple sampling and optimal error tolerance
AU - Kang, Wonseok
AU - Lee, Eunsung
AU - Chea, Eunjung
AU - Katsaggelos, Aggelos K
AU - Paik, Joonki
PY - 2013/10/18
Y1 - 2013/10/18
N2 - In this paper, we present a compressive sensing-based image denoising algorithm using spatially adaptive image representation and estimation of optimal error tolerance based on sparse signal analysis. The proposed method performs block-based multiple compressive sampling after decomposing the sparse signal into feature and non-feature regions using simple statistical analysis. For minimization of recovery error and number of iterations, the modified OMP method estimates the optimal error tolerance using the average variance in the recovery step. Experimental results demonstrate that the proposed denoising algorithm better removes noise without undesired artifacts than existing state-of-the-art methods in terms of both objective (PSNR/SSIM) and subjective measures. Processing time of the proposed method is 5 to 10 times faster than the standard OMP-based method.
AB - In this paper, we present a compressive sensing-based image denoising algorithm using spatially adaptive image representation and estimation of optimal error tolerance based on sparse signal analysis. The proposed method performs block-based multiple compressive sampling after decomposing the sparse signal into feature and non-feature regions using simple statistical analysis. For minimization of recovery error and number of iterations, the modified OMP method estimates the optimal error tolerance using the average variance in the recovery step. Experimental results demonstrate that the proposed denoising algorithm better removes noise without undesired artifacts than existing state-of-the-art methods in terms of both objective (PSNR/SSIM) and subjective measures. Processing time of the proposed method is 5 to 10 times faster than the standard OMP-based method.
KW - Compressed sensing
KW - image denoising
KW - matching pursuit algorithms
UR - http://www.scopus.com/inward/record.url?scp=84890528244&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84890528244&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2013.6638106
DO - 10.1109/ICASSP.2013.6638106
M3 - Conference contribution
AN - SCOPUS:84890528244
SN - 9781479903566
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 2503
EP - 2507
BT - 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
T2 - 2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
Y2 - 26 May 2013 through 31 May 2013
ER -