Title: Image Fusion With Contextual Statistical Similarity and Nonsubsampled Shearlet Transform
Abstract: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 f...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 CHMM is 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.Read More
Publication Year: 2016
Publication Date: 2016-12-30
Language: en
Type: article
Indexed In: ['crossref']
Access and Citation
Cited By Count: 60
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