Journal of Information Processing Systems

  • 제14권5호
  • /
  • Pages.1293-1300
  • /
  • 2018
  • /
  • 1976-913X(pISSN)
  • /
  • 2092-805X(eISSN)
한국정보처리학회 (Korea Information Processing Society)
권호 표지
DOI QR코드

DOI QR Code

Research on the Multi-Focus Image Fusion Method Based on the Lifting Stationary Wavelet Transform

  • Hu, Kaiqun (Chongqing Technology and Business University) ;
  • Feng, Xin (Chongqing Technology and Business University)
  • 투고 : 2018.04.05
  • 심사 : 2018.05.14
  • 발행 : 2018.10.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

For the disadvantages of multi-scale geometric analysis methods such as loss of definition and complex selection of rules in image fusion, an improved multi-focus image fusion method is proposed. First, the initial fused image is quickly obtained based on the lifting stationary wavelet transform, and a simple normalized cut is performed on the initial fused image to obtain different segmented regions. Then, the original image is subjected to NSCT transformation and the absolute value of the high frequency component coefficient in each segmented region is calculated. At last, the region with the largest absolute value is selected as the postfusion region, and the fused multi-focus image is obtained by traversing each segment region. Numerical experiments show that the proposed algorithm can not only simplify the selection of fusion rules, but also overcome loss of definition and has validity.

키워드

참고문헌

  1. B. Liu, W. Liu, and J. Ma "Multi-focus image fusion based on three channel nonseparable symmetrical wavelets," Chinese Journal of Scientific Instrument, vol. 33, no. 5, pp. 1110-1116, 2012. https://doi.org/10.3969/j.issn.0254-3087.2012.05.022
  2. G. Wang, M. Z. Ma, Y. L. Zhao, Q. Ma, L. Xiao, and A. He, "Algorithm for image fusion in the curvelet transform domain," Chinese Journal of Scientific Instrument, vol. 29, no. 9, pp. 1841-1845, 2008. https://doi.org/10.3321/j.issn:0254-3087.2008.09.010
  3. S. Li, B. Yang, and J. Hu, "Performance comparison of different multi-resolution transforms for image fusion," Information Fusion, vol. 12, no. 2, pp. 74-84, 2011. https://doi.org/10.1016/j.inffus.2010.03.002
  4. L. Demanet and L. Ying, "Wave atoms and sparsity of oscillatory patterns," Applied and Computational Harmonic Analysis, vol. 23, no. 3, pp. 368-387, 2007. https://doi.org/10.1016/j.acha.2007.03.003
  5. H. Zheng, C. Zheng, X. Yan, and H. Chen "Visible and infrared image fusion algorithm based on shearlet transform," Chinese Journal of Scientific Instrument, vol. 33, no. 7, pp. 1613-1619, 2012.
  6. W. Zhang, Q. Huang, and Z. Wang, "The fusion of remote sensing images based on lifting wavelet transformation," Computer and Information Science, vol. 2, no. 1, pp. 69-75, 2009.
  7. T. Cour, F. Benezit, and J. Shi, "Spectral segmentation with multiscale graph decomposition," in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, CA, 2005, pp. 1124-1131.
  8. Q. Miao, C. Shi, P. Xu, M. Yang, and Y. Shi, "Multi-focus image fusion algorithm based on shearlets," Chinese Optics Letters, vol. 9, no. 4, article no. 041001, 2011.
  9. G. Qu, D. Zhang, and P. Yan, "Information measure for performance of image fusion," Electronics Letters, vol. 38, no. 7, pp. 313-315, 2002. https://doi.org/10.1049/el:20020212