A Heterogeneous Image Fusion Algorithm Based on LLC Coding

  • Bing ZhuEmail author
  • Weixin Gao
  • Xiaomeng Wu
  • Ruixing Yu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11010)


Most image fusion algorithms are not good at batch processing. To address this, we propose a LLC coding based image fusion algorithm, by which multiple infrared and visible light images can be fused and identified. The images were encoded and several image features were extracted by those codes. It was judged whether the images could be merged by the coincidence of the non-zero coding counterpart obtained from comparing the LLC coding of two heterogeneous images. The max-pooling criterion was employed to fuse the features extracted from images by maximizing the complementary information and minimizing the redundant information. Consequently the SVM classifier was used to classify and identify the target. The simulated results show the accuracy of our proposed method.



Sponsored by Shaanxi Provincial Department of Education Scientific Research Plan Special Project (17JK0599), National Natural Science Foundation of China (41604122), Xi’an shiyou University Youth Science and Technology Innovation Fund project(2015BS18), Aviation Science Fund Project (20160153001), SAST Foundation (Grant No. SAST2015040), Xi’an Government Science and Technology Plan Project(2017081CGRC044 (XASY007))


  1. 1.
    Olshausen, B.A., Field, D.J.: Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381(6583), 607–609 (1996)CrossRefGoogle Scholar
  2. 2.
    Li, Z.-Q., Shi, Z.-P., Li, Z.-X., Shi, Z.-Z.: Structural similarity sparse coding and image feature extraction. Pattern Recognit. Artif. Intell. 23(1), 17–22 (2010)CrossRefGoogle Scholar
  3. 3.
    Yang, J., Yu, K., Gong, Y., Huang, T.: Linear spatial pyramid matching using sparse coding for image classification. In: Proceedings of CVPR 2009 (2009)Google Scholar
  4. 4.
    Yu, K., Zhang, T., Gong, Y.: Nonlinear learning using local coordinate coding. In: Proceedings of NIPS09 (2009)Google Scholar
  5. 5.
    Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., Gong, Y.: Locality-constrained linear coding for image classification. In: CVPR10 (2010)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Xi’an Shiyou UniversityXi’anPeople’s Republic of China
  2. 2.Northwestern Polytechnical UniversityXi’anPeople’s Republic of China

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