吉林大学学报(工学版) ›› 2014, Vol. 44 ›› Issue (6): 1849-1859.doi: 10.13229/j.cnki.jdxbgxb201406048

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Image fusion based on area objective assessment in finite discrete shearlet transform domain

CHEN Guang-qiu1, 3, GAO Yin-han2, LIU Guang-wen3, SUN Jun-xi3   

  1. 1.College of Instrumentation & Electrical Engineering, Jilin University,Changchun 130061,China;
    2.State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022,China;
    3.School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun 130022, China
  • Received:2013-08-22 Online:2014-11-01 Published:2014-11-01

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Abstract: To enhance the accuracy of multi-source image fusion, an adaptive fusion method is proposed. This method is based on the area objective assessment in Finite Discrete Shearlet Transform (FDST) domain. The source images are decomposed to subband images with multi-scale and multi-direction by FDST. The gradient information correlation factor is taken as the coefficient weight for low-frequency subband fusion; while for high-frequency subband, consistency selection of the coefficient absolute value and area standard deviation is adopted as the fusion rule. The fused low and high frequency coefficients are reconstructed to image by Finite Discrete Shearlet Inverse Transform (FDSIT). Fusion experiment is done with several sets of different modality images, and objective performance assessments of the fusion results are implemented. Results indicate that the proposed method performs better in subjective and objective assessments than other existing Multi-Scale Decomposition (MSD) fusion techniques.

Key words: information processing, finite discrete shearlet transform (FDST), fusion rule, objective assessment, shift-invariant

CLC Number: 

  • TP391.4
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