吉林大学学报(工学版) ›› 2014, Vol. 44 ›› Issue (01): 225-234.doi: 10.13229/j.cnki.jdxbgxb201401037

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Adaptive image fusion based on image quality assessment parameter in NSST system

GAO Yin-han1, CHEN Guang-qiu2,3, LIU Yan-yan2,3   

  1. 1. State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China;
    2. College of Instrumentation & Electrical Engineering, Jilin University, Changchun 130061, China;
    3. School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun 130022, China
  • Received:2012-12-12 Online:2014-01-01 Published:2014-01-01

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Abstract:

To enhance the multi-source image fusion accuracy, an adaptive fusion method based on image quality assessment parameter in Nonsubsampled Shearlet Transform (NSST) domain is proposed. The Source images are decomposed to subband images with multi-scale and multi-direction in NSST. The low frequency subband fusion rule is based on the structural similarity index with spatial frequency as coefficient weights. For the high frequency subands, the fusion rule of coefficient absolute value with neighborhood average energy consistency selection is adopted. The fused low and high frequency coefficients are reconstructed to image by nonsubsampled shearlet inverse transform. Fusion experiments are conducted with several sets of different modality images, and the objective assessment of fused results is done. The experiment results show that the proposed algorithm performs better in subjective and objective assessments than a few existing multi-scale fusion techniques, and obtains better fusion performance.

Key words: information processing, nonsubsampled shearlet transform, fusion rule, objective assessment, shift-invariant

CLC Number: 

  • TN911

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