• Acta Photonica Sinica
  • Vol. 47, Issue 9, 910002 (2018)
DING Wen-shan*, BI Du-yan, HE Lin-yuan, FAN Zun-lin, and WU Dong-peng
Author Affiliations
  • [in Chinese]
  • show less
    DOI: 10.3788/gzxb20184709.0910002 Cite this Article
    DING Wen-shan, BI Du-yan, HE Lin-yuan, FAN Zun-lin, WU Dong-peng. Infrared and Visible Image Fusion Based on Sparse Feature[J]. Acta Photonica Sinica, 2018, 47(9): 910002 Copy Citation Text show less
    References

    [1] BHATNAGAR G, WU Q M J, LIU Zheng. A new contrast based multimodal medical image fusion framework[J]. Neurocomputing, 2015, 157: 143-152.

    [2] MA Jia-yi, ZHAO Ji, MA Yong, et al. Non-rigid visible and infrared face registration via regularized gaussian fields criterion[J]. Pattern Recognition, 2015, 48(3): 772-784.

    [3] CHEN Chen, LI Ye-qing, LIU Wei,et al. Image fusion with local spectral consistency and dynamic gradient sparsity[C]. IEEE Conference on Computer Vision and Pattern Recognition, 2014: 2760-2765.

    [4] LI Yan-sheng, TAO Chao, TAN Yi-hua, et al. Unsupervised multilayer feature learning for satellite image scene classification[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(2): 157-161.

    [5] MAHYARI A, YAZDI M. Panchromatic and multispectral image fusion based on maximization of both spectral and spatial similarities[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(6): 1-10.

    [6] EASLEY G, LABATE D, LIM W. Sparse directional image representations using the discrete shearlet transform[J]. Applied and Computational Harmonic Analysis , 2008, 25(1): 25-46.

    [7] KUTYNIOK G, LABATE D. Construction of regular and irregular shearlet frames[J]. Journal of Wavelet Theory and Applications, 2007, 1(1): 1-12.

    [8] GUO Kang-hui, LABATE D. Optimally sparse multidimensional representation using shearlets[J]. Siam Journal on Mathematical Analysis, 2008, 39(1): 298-318.

    [9] GUO Kang-hui, LABATE D, LIM W, Edge analysis and identification using the continuous shearlet transform[J]. Applied and Computational Harmonic Analysis, 2009, 27(1): 24-46.

    [10] MIAO Qi-guang, SHI Cheng, XU Peng-fei,et al. A novel algorithm of image fusion using shearlets[J]. Optical Communications, 2011, 284(6): 1540-1547.

    [11] MIAO Qi-guang, XU Peng-fei, LIU Tian,et al. Linear feature separation from topographic maps using energy density and shear transform[J]. IEEE Transcations on Image Process, 2013, 22(4): 1548-1558.

    [12] EASLEY G, LABATE D, LIM W Q. Sparse directional image representation using the discrete shearlet transforms[J]. Applied and Computational Harmonic Analysis, 2008, 25(1): 25-46.

    [13] LIU Xuan, ZHOU Yue, WANG Jia-jun. Image fusion based on shearlet transform and regional features[J]. International Journal of Electronics Communications, 2014, 68(6): 471-477.

    [14] SINGH S, GUPTA D, ANAND RS, KUMAR V. Nonsubsampled shearlet based CT and MR medical image fusion using biologically inspired spiking neural network[J]. Biomedical Signal Processing and Control, 2015, 18: 91-101.

    [15] YIN Ming, LIU Wei, ZHAO Xia,et al. A novel image fusion algorithm based on nonsubsampled shearlet transform[J]. Optik, 2014, 125(10): 2274-2282.

    [16] LIU Xin-bing, MEI Wen-bo, DU Hu-qian. Structure tensor and nonsubsampled shearlet transform based algorithm for CT and MRI image fusion[J]. Neurocomputing, 2017, 235: 131-139.

    [17] KONG Wei-wei, ZHANG Long-jun, LEI Yang. Novel fusion method for visible light and infrared images based on NSST–SF–PCNN[J]. Infrared Physics & Technology, 2014, 65(7): 103-112.

    [18] LIU Zhan-wen, FENG Yan, ZHANG Yi-fan,et al. A fusion algorithm for infrared and visible images based on RDU-PCNN and ICA-bases in NSST domain[J]. Infrared Physics & Technology, 2016, 79: 183-190.

    [19] ZHANG Bao-hua, LU Xiao-qi, PEI Hai-quan,et al. A fusion algorithm for infrared and visible images based on saliency analysis and non-subsampled Shearlet transform[J]. Infrared Physics & Technology, 2015, 73: 286-297.

    [20] LIU Zhan-wen, FENG Yan, CHEN Hang,et al. A fusion algorithm for infrared and visible based on guided filtering and phase congruency in NSST domain[J]. Optics and Lasers in Engineering, 2017, 97: 71-77.

    [21] GUO Kang-hui, KUTYNIOK G, LABATE D. Sparse multidimensional representations using anisotropic dilation and shear operators[C]. Wavelets and Splines, Nashboro Press, Nashville, TN, 2006, 189-201.

    [22] GUO Kang-hui, KUTYNIOK G. Optimally Sparse multidimensional representation using shearlets[J]. SIAM Journal on Mathematical Analysis, 2007, 39(1): 298-318.

    [23] EASLEY G, LABATE D, LIM W. Sparse directional image representation using the discrete shearlets transform[J]. Applied and Computational Harmonic Analysis, 2008, 25(1): 25-46.

    [24] XIAO Yang, XIAO Tan, HU Shao-hai,et al. Two-dimensional hybrid transform (DCT-DWT) for 2-D signal processing[C]. ICSP 2006, Beijing, IEEE Press, 2006: 247-250.

    [25] HAN Bin, KUTYNIOK G, SHEN Zuo-wei. Adaptive multiresolution analysis structures and Shearlet systems[J]. SIAM Journal on Numerical Analysis, 2011, 49(5): 1921-1946.

    [26] DONOHO D. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.

    [27] CANDS E, WAKIN M. An introduction to compressive sampling[J]. IEEE Signal Processing Magazine, 2008, 25(2): 21-30.

    [28] YANG Bin, LI Shu-tao. Multifocus image fusion and restoration with sparse representation[J]. IEEE Transactions on Instrumentation and Measurement, 2010, 59(4): 884-892.

    [29] DONOHO D. For most large underdetermined systems of linear equations the minimal 11-norm solution is also the sparsest solution[J]. Communications on Pure and Applied Mathematics. 2006, 59(6): 797-829.

    [30] [EB/OL]. [2018-04-02].http: //www.imagefusion.org/

    [31] PAN Yu, SUN Quan-sen, XIA De-sen. Image fusion framework based on decomposition of PCA[J]. Computer Engineering, 2011, 37(13): 210-213.

    [32] PAJARES G, MANUEL J. A wavelet-based image fusion tutorial[J]. Pattern Recognition, 2004, 37: 1855-1872.

    [33] YAN Ruo-mei, SHAO Ling, LIU Yan. Nonlocal hierarchical dictionary learning using wavelets for image denoising[J]. IEEE Transactions on Image Process, 2013, 22(12): 4689-4698.

    [34] EASLEY G, LABATE D, LIM W. Sparse directional image representation using the discrete shearlet transforms[J].Applied and Computational Harmonic Analysis, 2008, 25(1): 25-46.

    [35] YANG Bin, LI Shu-tao. Multifocus image fusion and restoration with sparse representation[J]. IEEE Transactions on Instrumentation and Measurement, 2010,59(14): 884-892.

    [36] QU Gui-hong, ZHANG Da-li, YAN Ping-fan. Information measure for performance of image fusion[J]. Electronics Letters,2002, 38(7): 313-315.

    [37] PIELLA G, HEIJMANS H. A new quality metric for image fusion[C]. Proceedings 2003 International Conference on Image Processing, 2003: III-173- III-176.

    [38] YHANG Cui, ZHANG Jian-qi, WANG Xiao-rui,et al. A novel similarity based quality metric for image fusion[J]. Information Fusion, 2008, 9(2): 156-160.

    CLP Journals

    [1] WANG Guang-xia, FENG Hua-jun, XU Zhi-hai, LI Qi, CHEN Yue-ting. Low-light Image Pairs Fusion Method Based on Patch-match[J]. Acta Photonica Sinica, 2019, 48(4): 410003

    [2] JIANG Ze-tao, HE Yu-ting, ZHANG Shao-qin. Infrared and Low-light-level Visible Image Fusion Algorithm Based on Contrast Enhancement and Cauchy Fuzzy Function[J]. Acta Photonica Sinica, 2019, 48(6): 610001

    DING Wen-shan, BI Du-yan, HE Lin-yuan, FAN Zun-lin, WU Dong-peng. Infrared and Visible Image Fusion Based on Sparse Feature[J]. Acta Photonica Sinica, 2018, 47(9): 910002
    Download Citation