Multi-focus image fusion based on support vector machines and window gradient

被引：0
|
作者：
Li X.-F. [1 ,2 ]
Wang J. [1 ,2 ]
Zhang X.-L. [1 ,2 ]
Fan T.-H. [3 ]
机构：
[1] College of Computer Science and Technology, Jilin University, Changchun
[2] Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun
[3] College of Instrumentation and Electrical Engineering, Jilin University, Changchun
来源：
Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2020年 / 50卷 / 01期
关键词：
Computer application; Empirical mode decomposition; Image gradient; Multi-focus image fusion; Support vector machine;
D O I：
10.13229/j.cnki.jdxbgxb20190116
中图分类号：
学科分类号：
摘要：
In order to improve the quality of multi-focus image fusion, a multi-focus image fusion method based on support vector machines (SVM) and window gradient is proposed in this paper. First, the multi-focus images are decomposed by window empirical mode decomposition (WEMD), and a set of intrinsic mode function components (high frequency part) and residual components (low frequency part) are obtained. WEMD can effectively solve the signal aliasing problem in image decomposition. Then, the fusion rule of low-frequency components is determined by the output of the support vector machine, and the clearer focus area is selected. The window gradient contrast algorithm proposed in this paper is used to guide the fusion of high-frequency components, and the consistency of the image is ensured while maintaining the contrast of the fused image. Finally, the WEMD inverse transform is performed to obtain the fused image. Experiments were carried out on 9 sets of multi-focus images. Results show that the proposed method can obtain better fusion quality than the other five methods in terms of the subjective evaluation and five objective evaluation indicators. © 2020, Jilin University Press. All right reserved.
引用
收藏
页码：227 / 236
页数：9
相关论文
共 20 条
  • [1] Lin S.-Z., Han Z., Images fusion based on deep stack convolutional neural network, Chinese Journal of Computers, 40, 11, pp. 2506-2518, (2017)
  • [2] Li S., Kang X., Fang L., Et al., Pixel-level image fusion: a survey of the state of the art, Information Fusion, 33, pp. 100-112, (2017)
  • [3] Zhu Z., Yin H., Chai Y., Et al., A novel multi-modality image fusion method based on image decomposition and sparse representation, Information Sciences, 432, pp. 516-529, (2018)
  • [4] Do M.N., Vetterli M., The contourlet transform: an efficient directional multiresolution image representation, IEEE Transactions on Image Processing, 14, 12, pp. 2091-2106, (2005)
  • [5] Da Cunha A.L., Zhou J., do Minh N., The nonsubsampled contourlet transform: theory, design, and applications, IEEE Transactions on Image Processing, 15, 10, pp. 3089-3101, (2006)
  • [6] Yang Y., Huang S.Y., Gao J., Et al., Multi-focus image fusion using an effective discrete wavelet transform based algorithm, Meas Sci Rev, 14, 2, pp. 102-108, (2014)
  • [7] Wang H., Multi-focus image fusion algorithm based on focus detection in spatial and NSCT domain, PloS One, 13, 9, (2018)
  • [8] Yang Y., Dai M., Zhou L.-Y., Et al., Multifocus image fusion based on nonsubsampled Bandelet transform, Journal of Jilin University (Engineering and Technology Edition), 44, 2, pp. 525-530, (2014)
  • [9] Moushmi S., Sowmya V., Soman K.P., Empirical wavelet transform for multifocus image fusion, Proceedings of the International Conference on Soft Computing Systems, pp. 257-263, (2016)
  • [10] Chen C., Gend P., Lu K., Multifocus İmage Fusion Based on Multiwavelet and DFB, Chem Engineer Trans, 46, pp. 277-282, (2015)
12