Abstract
| Original language | English |
|---|---|
| Title of host publication | Neural Information Processing |
| Subtitle of host publication | 21st International Conference, ICOIP 2014, Kuching, Malaysia, November 3-6, 2014 Proceedings, Part III |
| Editors | Chu Kiong Loo, Keem Siah Yap, Kok Wai Wong, Andrew Teoh, Kaizhu Huang |
| Place of Publication | Cham, Switzerland |
| Publisher | Springer |
| Pages | 127-134 |
| Number of pages | 8 |
| Volume | 8836 |
| Edition | 1 |
| ISBN (Electronic) | 9783319126425 |
| ISBN (Print) | 9783319126432 |
| DOIs | |
| Publication status | Published - 2014 |
| Event | ICONIP 2014: 21st International Conference on Neural Information Processing - Kuching, Kuching, Malaysia Duration: 3 Nov 2014 → 6 Nov 2014 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 8836 |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Conference
| Conference | ICONIP 2014: 21st International Conference on Neural Information Processing |
|---|---|
| Abbreviated title | ICONIP 2014 |
| Country | Malaysia |
| City | Kuching |
| Period | 3/11/14 → 6/11/14 |
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A Novel Adaptive Shrinkage Threshold on Shearlet Transform for Image Denoising. / Islam, Sheikh Md. Rabiul; HUANG, Xu; LE, Kim.
Neural Information Processing: 21st International Conference, ICOIP 2014, Kuching, Malaysia, November 3-6, 2014 Proceedings, Part III. ed. / Chu Kiong Loo; Keem Siah Yap; Kok Wai Wong; Andrew Teoh; Kaizhu Huang. Vol. 8836 1. ed. Cham, Switzerland : Springer, 2014. p. 127-134 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8836).Research output: A Conference proceeding or a Chapter in Book › Conference contribution
TY - GEN
T1 - A Novel Adaptive Shrinkage Threshold on Shearlet Transform for Image Denoising
AU - Islam, Sheikh Md. Rabiul
AU - HUANG, Xu
AU - LE, Kim
PY - 2014
Y1 - 2014
N2 - Shearlet is a new multidimensional and multiscale transform which is optimally efficient in representing image containing edges. In this paper an adaptive shrinkage threshold for image de-noising in shearlet domain is proposed. Experimental results show that images de-noised with the proposed approach had higher qualities than those produced with some of the other denoising methods like wavelet-based, bandlet-based, shearlet-based and curvelet-based
AB - Shearlet is a new multidimensional and multiscale transform which is optimally efficient in representing image containing edges. In this paper an adaptive shrinkage threshold for image de-noising in shearlet domain is proposed. Experimental results show that images de-noised with the proposed approach had higher qualities than those produced with some of the other denoising methods like wavelet-based, bandlet-based, shearlet-based and curvelet-based
KW - Adaptive threshold
KW - Denoising
KW - Kurtosis
KW - Shearlet transform
KW - Skewness
KW - UIQI
KW - Image Processing
UR - http://www.scopus.com/inward/record.url?scp=84909996047&partnerID=8YFLogxK
UR - http://www.mendeley.com/research/novel-adaptive-shrinkage-threshold-shearlet-transform-image-denoising
U2 - 10.1007/978-3-319-12643-2_16
DO - 10.1007/978-3-319-12643-2_16
M3 - Conference contribution
SN - 9783319126432
VL - 8836
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 127
EP - 134
BT - Neural Information Processing
A2 - Loo, Chu Kiong
A2 - Yap, Keem Siah
A2 - Wong, Kok Wai
A2 - Teoh, Andrew
A2 - Huang, Kaizhu
PB - Springer
CY - Cham, Switzerland
ER -