Journal
IEEE SENSORS JOURNAL
Volume 17, Issue 6, Pages 1760-1771Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSEN.2016.2646741
Keywords
Contextual hidden Markov model; nonsubsampled shearlet transform; statistical similarity; image fusion
Funding
- National Natural Science Foundation of China [61300151, 61373055]
- Postdoctoral Science Foundation of China [2013M541601, 1301079C]
- Provincial Researc [BK20151358, BK20151202, BK20130155]
- Ministry of Housing and Urban-Rural Development of the People's Republic of China [2015-K8-035]
- Fundamental Research Funds for the Central Universities [JUSRP51618B]
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Image fusion has the capability to integrate useful information from source images into a more comprehensive image. How to obtain the effective representation of source images is a key step to image fusion. Due to the loss of the dependence of coefficients, most of traditional multi-scale decomposition-based image fusion methods suffer from an inaccurate image representation. To solve this problem, a novel image fusion method with contextual statistical similarity in nonsubsampled shearlet transform (NSST) is presented. The key contributions include: 1) the dependence of NSST coefficients is captured by the contextual hidden Markov model (CHMM); 2) the contextual statistical similarity of coefficients is proposed; 3) an effective fusion rule based on the characteristic of CHMMis developed for high-frequency subbands in NSST domain. By the visual analysis and quantitative evaluations on experimental results, the superiority of the proposed method is demonstrated.
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