A Novel Adaptive Shrinkage Threshold on Shearlet Transform for Image Denoising

Sheikh Md. Rabiul Islam; Xu HUANG; Kim LE

doi:10.1007/978-3-319-12643-2_16

A Novel Adaptive Shrinkage Threshold on Shearlet Transform for Image Denoising

Sheikh Md. Rabiul Islam, Xu HUANG, Kim LE

Information, Technology (IT) & Systems

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

6 Citations (Scopus)

Abstract

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

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	https://doi.org/10.1007/978-3-319-12643-2_16
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

Access to Document

10.1007/978-3-319-12643-2_16

Fingerprint Dive into the research topics of 'A Novel Adaptive Shrinkage Threshold on Shearlet Transform for Image Denoising'. Together they form a unique fingerprint.

Cite this

Islam, S. M. R., HUANG, X., & LE, K. (2014). A Novel Adaptive Shrinkage Threshold on Shearlet Transform for Image Denoising. In C. K. Loo, K. S. Yap, K. W. Wong, A. Teoh, & K. Huang (Eds.), Neural Information Processing: 21st International Conference, ICOIP 2014, Kuching, Malaysia, November 3-6, 2014 Proceedings, Part III (1 ed., Vol. 8836, pp. 127-134). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8836). Springer. https://doi.org/10.1007/978-3-319-12643-2_16

Islam, Sheikh Md. Rabiul ; HUANG, Xu ; LE, Kim. / A Novel Adaptive Shrinkage Threshold on Shearlet Transform for Image Denoising. Neural Information Processing: 21st International Conference, ICOIP 2014, Kuching, Malaysia, November 3-6, 2014 Proceedings, Part III. editor / Chu Kiong Loo ; Keem Siah Yap ; Kok Wai Wong ; Andrew Teoh ; Kaizhu Huang. Vol. 8836 1. ed. Cham, Switzerland : Springer, 2014. pp. 127-134 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{24d2f74aff914890a64c5143397b4f30,

title = "A Novel Adaptive Shrinkage Threshold on Shearlet Transform for Image Denoising",

abstract = "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",

keywords = "Adaptive threshold, Denoising, Kurtosis, Shearlet transform, Skewness, UIQI, Image Processing",

author = "Islam, {Sheikh Md. Rabiul} and Xu HUANG and Kim LE",

year = "2014",

doi = "10.1007/978-3-319-12643-2_16",

language = "English",

isbn = "9783319126432",

volume = "8836",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "127--134",

editor = "Loo, {Chu Kiong} and Yap, {Keem Siah} and Wong, {Kok Wai} and Andrew Teoh and Kaizhu Huang",

booktitle = "Neural Information Processing",

address = "Netherlands",

edition = "1",

note = "ICONIP 2014: 21st International Conference on Neural Information Processing, ICONIP 2014 ; Conference date: 03-11-2014 Through 06-11-2014",

}

Islam, SMR, HUANG, X & LE, K 2014, A Novel Adaptive Shrinkage Threshold on Shearlet Transform for Image Denoising. in CK Loo, KS Yap, KW Wong, A Teoh & K Huang (eds), Neural Information Processing: 21st International Conference, ICOIP 2014, Kuching, Malaysia, November 3-6, 2014 Proceedings, Part III. 1 edn, vol. 8836, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8836, Springer, Cham, Switzerland, pp. 127-134, ICONIP 2014: 21st International Conference on Neural Information Processing, Kuching, Malaysia, 3/11/14. https://doi.org/10.1007/978-3-319-12643-2_16

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 › peer-review

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

T2 - ICONIP 2014: 21st International Conference on Neural Information Processing

Y2 - 3 November 2014 through 6 November 2014

ER -

Islam SMR, HUANG X, LE K. A Novel Adaptive Shrinkage Threshold on Shearlet Transform for Image Denoising. In Loo CK, Yap KS, Wong KW, Teoh A, Huang K, editors, Neural Information Processing: 21st International Conference, ICOIP 2014, Kuching, Malaysia, November 3-6, 2014 Proceedings, Part III. 1 ed. Vol. 8836. Cham, Switzerland: Springer. 2014. p. 127-134. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-12643-2_16

A Novel Adaptive Shrinkage Threshold on Shearlet Transform for Image Denoising

Abstract

Publication series

Conference

Access to Document

Other files and links

Cite this