A Statistical Image Fusion Scheme for Multi Focus Applications

In this paper, we propose a statistical scheme to judge the activity level measurement (ALM) that is based on wavelet-domain hidden Markov model (WD-HMM) and maximum likelihood (MLK). The source images are firstly decomposed by the wavelets and only the coefficients in the high frequency (HH) are ut...

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Hauptverfasser:	Liao, Z. W., Hu, S. X., Chen, W. F., Tang, Y. Y., Huang, T. Z.
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Applied sciences Artificial intelligence Computer science control theory systems Exact sciences and technology Hide Markov Model Hide State Image Fusion Multi Focus Image Pattern recognition. Digital image processing. Computational geometry Source Image
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creator	Liao, Z. W. Hu, S. X. Chen, W. F. Tang, Y. Y. Huang, T. Z.
description	In this paper, we propose a statistical scheme to judge the activity level measurement (ALM) that is based on wavelet-domain hidden Markov model (WD-HMM) and maximum likelihood (MLK). The source images are firstly decomposed by the wavelets and only the coefficients in the high frequency (HH) are utilized. Considering the shift-variance of wavelets, the merged image is obtained from the source images directly. The regions of each source image are obtained by the Hough transform (HT) and their ALM are decided by the ALM of their coefficients in HH according to MLK. Finally, two multi focus images are merged by our new framework. The fusion results show the high ability of our scheme in preserving edge information and avoiding shift-variant.
doi_str_mv	10.1007/11739685_115
format	Conference Proceeding
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W. ; Hu, S. X. ; Chen, W. F. ; Tang, Y. Y. ; Huang, T. Z.</creator><contributor>Yan, Hong ; Yeung, Daniel S. ; Wang, Xi-Zhao ; Liu, Zhi-Qiang</contributor><creatorcontrib>Liao, Z. W. ; Hu, S. X. ; Chen, W. F. ; Tang, Y. Y. ; Huang, T. Z. ; Yan, Hong ; Yeung, Daniel S. ; Wang, Xi-Zhao ; Liu, Zhi-Qiang</creatorcontrib><description>In this paper, we propose a statistical scheme to judge the activity level measurement (ALM) that is based on wavelet-domain hidden Markov model (WD-HMM) and maximum likelihood (MLK). The source images are firstly decomposed by the wavelets and only the coefficients in the high frequency (HH) are utilized. Considering the shift-variance of wavelets, the merged image is obtained from the source images directly. The regions of each source image are obtained by the Hough transform (HT) and their ALM are decided by the ALM of their coefficients in HH according to MLK. Finally, two multi focus images are merged by our new framework. The fusion results show the high ability of our scheme in preserving edge information and avoiding shift-variant.</description><identifier>ISSN: 0302-9743</identifier><identifier>ISBN: 3540335846</identifier><identifier>ISBN: 9783540335849</identifier><identifier>EISSN: 1611-3349</identifier><identifier>EISBN: 9783540335856</identifier><identifier>EISBN: 3540335854</identifier><identifier>DOI: 10.1007/11739685_115</identifier><language>eng</language><publisher>Berlin, Heidelberg: Springer Berlin Heidelberg</publisher><subject>Applied sciences ; Artificial intelligence ; Computer science; control theory; systems ; Exact sciences and technology ; Hide Markov Model ; Hide State ; Image Fusion ; Multi Focus Image ; Pattern recognition. Digital image processing. 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The fusion results show the high ability of our scheme in preserving edge information and avoiding shift-variant.</description><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computer science; control theory; systems</subject><subject>Exact sciences and technology</subject><subject>Hide Markov Model</subject><subject>Hide State</subject><subject>Image Fusion</subject><subject>Multi Focus Image</subject><subject>Pattern recognition. Digital image processing. 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Computational geometry</topic><topic>Source Image</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liao, Z. W.</creatorcontrib><creatorcontrib>Hu, S. X.</creatorcontrib><creatorcontrib>Chen, W. F.</creatorcontrib><creatorcontrib>Tang, Y. Y.</creatorcontrib><creatorcontrib>Huang, T. Z.</creatorcontrib><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liao, Z. W.</au><au>Hu, S. X.</au><au>Chen, W. F.</au><au>Tang, Y. Y.</au><au>Huang, T. 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The regions of each source image are obtained by the Hough transform (HT) and their ALM are decided by the ALM of their coefficients in HH according to MLK. Finally, two multi focus images are merged by our new framework. The fusion results show the high ability of our scheme in preserving edge information and avoiding shift-variant.</abstract><cop>Berlin, Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/11739685_115</doi><tpages>10</tpages></addata></record>
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subjects	Applied sciences Artificial intelligence Computer science control theory systems Exact sciences and technology Hide Markov Model Hide State Image Fusion Multi Focus Image Pattern recognition. Digital image processing. Computational geometry Source Image
title	A Statistical Image Fusion Scheme for Multi Focus Applications
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