A Novel Adaptive Shrinkage Threshold on Shearlet Transform for Image Denoising

Sheikh Md. Rabiul Islam, Xu HUANG, Kim LE

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-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 languageEnglish
Title of host publicationNeural Information Processing
Subtitle of host publication21st International Conference, ICOIP 2014, Kuching, Malaysia, November 3-6, 2014 Proceedings, Part III
EditorsChu Kiong Loo, Keem Siah Yap, Kok Wai Wong, Andrew Teoh, Kaizhu Huang
Place of PublicationCham, Switzerland
PublisherSpringer
Pages127-134
Number of pages8
Volume8836
Edition1
ISBN (Electronic)9783319126425
ISBN (Print)9783319126432
DOIs
Publication statusPublished - 2014
EventICONIP 2014: 21st International Conference on Neural Information Processing - Kuching, Kuching, Malaysia
Duration: 3 Nov 20146 Nov 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8836
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceICONIP 2014: 21st International Conference on Neural Information Processing
Abbreviated titleICONIP 2014
CountryMalaysia
CityKuching
Period3/11/146/11/14

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