TY - GEN
T1 - Novel information processing for image de-noising based on sparse basis
AU - Rabiul Islam, Sheikh Md
AU - Huang, Xu
AU - Ou, Keng-Liang
AU - Rojas, Raul Fernandez
AU - Cui, Hongyan
PY - 2015
Y1 - 2015
N2 - Image de-noising is one of the important information processing technologies and a fundamental image processing step for improving the overall quality of medical images. Conventional de-noising methods, however, tend to over-suppress high-frequency details. To overcome this problem, in this paper we present a novel compressive sensing (CS) based noise removing algorithm using proposed sparse basis on CDF9/7 wavelet transform. The measurement matrix is applied to the transform coefficients of the noisy image for compressive sampling. The orthogonal matching pursuit (OMP) and Basis Pursuit (BP) are applied to reconstruct image from noisy sparse image. In the reconstruction process, the proposed threshold with Bayeshrink thresholding strategies is used. Experimental results demonstrate that the proposed method removes noise much better than existing state-of-the-art methods in the sense image quality valuation indexes.
AB - Image de-noising is one of the important information processing technologies and a fundamental image processing step for improving the overall quality of medical images. Conventional de-noising methods, however, tend to over-suppress high-frequency details. To overcome this problem, in this paper we present a novel compressive sensing (CS) based noise removing algorithm using proposed sparse basis on CDF9/7 wavelet transform. The measurement matrix is applied to the transform coefficients of the noisy image for compressive sampling. The orthogonal matching pursuit (OMP) and Basis Pursuit (BP) are applied to reconstruct image from noisy sparse image. In the reconstruction process, the proposed threshold with Bayeshrink thresholding strategies is used. Experimental results demonstrate that the proposed method removes noise much better than existing state-of-the-art methods in the sense image quality valuation indexes.
KW - ATVD
KW - BP
KW - CS
KW - OMP
KW - Sparse
UR - http://www.scopus.com/inward/record.url?scp=84951968504&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84951968504&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-26555-1_50
DO - 10.1007/978-3-319-26555-1_50
M3 - Conference contribution
AN - SCOPUS:84951968504
SN - 9783319265544
VL - 9491
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 443
EP - 451
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PB - Springer Verlag
T2 - 22nd International Conference on Neural Information Processing, ICONIP 2015
Y2 - 9 November 2015 through 12 November 2015
ER -