Computer Science ›› 2020, Vol. 47 ›› Issue (1): 153-158.doi: 10.11896/jsjkx.181202437

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Image Fusion Method Based on Image Energy Adjustment

LI Xiao-yu,GAO Qing-wei,LU Yi-xiang,SUN Dong   

  1. (School of Electrical Engineering and Automation,Anhui University,Hefei 230601,China)
  • Received:2018-12-28 Published:2020-01-19
  • About author:LI Xiao-yu,born in 1993,postgraduate.His main research interests include machine learning;GAO Qing-wei,born in 1965,Ph.D,professor,Ph.D supervisor.His main research interests include digital signal processing and wavelet transform.
  • Supported by:
    This work was supported by Key Science Program of Anhui Education Department (KJ2018A0012) and Provincial Natural Science Foundation of Anhui (1608085MF125).

Abstract: Aiming at the problem that traditional image fusion algorithm can’t achieve good fusion effect on the image with large energy difference,this paper decomposed the high and low frequency signals of the two images by the combination of multi-scale transformation and sparse representation according to the energy division of the image.The sparse fusion rules of different energy image blocks are adjusted,and the consistency test is added in the high frequency part to further constrain the composite process of the MSD coefficients corresponding to the local spatial energy.Finally,the fused image is reconstructed by wavelet inverse transform.Infrared images,medical images and multi-focus images are used to verify its performance,the effects of sparse decomposition layer number and window step size on the fusion effect are analyzed,the optimal decomposition method under the framework is obtained,and then the fusion image with excellent subjective results and objective inclications are obtained.The experimental results show that the proposed algorithm can achieve better fusion effect when the image is obtained by any two types of sensors,and is not limited to the fusion of two images.It is superior to the traditional indicators such as SF,SSIM and EFQI of fusion algorithm and general multi-scale algorithm combined with sparse representation.

Key words: Multi-scale transformation, Sparse representation, Image fusion, Energy division, Consistency test

CLC Number: 

  • TP391
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