Infrared Technology, Volume. 46, Issue 3, 314(2024)

Infrared and Visible Image Fusion Based on Information Bottleneck Siamese Autoencoder Network

Luyao MA1,2,3, Xiaoqing LUO1,2,3、*, and Zhancheng ZHANG4
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
  • 3[in Chinese]
  • 4[in Chinese]
  • show less
    References(29)

    [1] [1] ZHANG D D, WANG C P, FU Q. Overview of infrared and visible image fusion algorithms based on deep learning framework[J]. Laser & Infrared, 2022, 52(9): 1288-1298.

    [2] [2] MA J, MA Y, LI C. Infrared and visible image fusion methods and applications: a survey[J]. Information Fusion, 2019, 45: 153-178.

    [3] [3] CHEN Y, ZHANG J J, WANG Z. Infrared and visible image fusion based on multi-scale dense attention connection network[J]. Optics and Precision Engineering, 2022, 30(18): 2253-2266.

    [4] [4] SUN B, ZHUGE W W, GAO Y X, et al. Infrared and visible lmage fusion based on latent low-rank representation[J]. Infrared Technology, 2022, 44(8): 853-862.

    [5] [5] YANG S Y, XI Z H, WANG H D, et al. Image fusion based on NSCT and minimum-local mean gradient [J]. Infrared Technology, 2021, 43(1): 13-20.

    [6] [6] LIU Z J, JIA P, XIA Y H, et al. Development and performance evaluation of infrared and visual image fusion technology[J]. Laser & Infrared, 2019, 49(5): 123-130.

    [7] [7] Lee H Y, Tseng H Y, Mao Q, et al. Drit++: Diverse image-to-image translation via disentangled representations[J]. International Journal of Computer Vision, 2020, 128(10): 2402-2417.

    [8] [8] MA L, GOU Y T, LEI T, et al. Small object detection based on multi-scale feature fusion using remote sensing images[J]. Opto-Electronic Engineering, 2022, 49(4): 49-65.

    [9] [9] LEI D J, DU J H, ZHANG L P, et al. Multi-stream architecture and multiscale convolutional neural network for remote sensing image fusion[J]. Journal of Electronics & Information Technology, 2022, 44(1): 237-244.

    [10] [10] LI M, LIU F, LI J Z. Combining convolutional attention module and convolutional autoencoder for detail injection remote sensing image fusion[J]. Acta Photonica Sinica, 2022, 51(6): 406-418.

    [11] [11] LIU B, HAN G L, LUO H Y. Image fusion algorithm based on multi-scale detail siamese convolutional neural network[J]. Chinese Journal of Liquid Crystals and Displays, 2021, 36(9): 1283-1293.

    [12] [12] Krishna V A, Reddy A A, Nagajyothi D. Signature recognition using siamese neural networks[C]//IEEE International Conference on Mobile Networks and Wireless Communications (ICMNWC), 2021: 1-4.

    [13] [13] LI H, WU X J. DenseFuse: A fusion approach to infrared and visible images[J]. IEEE Transactions on Image Processing, 2018, 28(5): 2614-2623.

    [14] [14] LI H, WU X J, Durrani T. NestFuse: An infrared and visible image fusion architecture based on nest connection and spatial/channel attention models[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 69(12): 9645-9656.

    [15] [15] LU B, CHEN J C, Chellappa R. Unsupervised domain-specific deblurring via disentangled representations[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 10225-10234.

    [16] [16] WANG G, HAN H, SHAN S, et al. Cross-domain face presentation attack detection via multi-domain disentangled representation learning[C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 6678-6687.

    [17] [17] WEN Z D, WANG J R, WANG X X, et al. A review of disentangled representation learning[J]. Acta Automatica Sinica, 2022, 48(2): 351-374.

    [18] [18] ZHAO Z, XU S, ZHANG C, et al. DIDFuse: Deep image decomposition for infrared and visible image fusion[J]. arXiv preprint arXiv:2003.09210, 2020.

    [19] [19] XU H, WANG X, MA J. DRF: Disentangled representation for visible and infrared image fusion[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1-13.

    [20] [20] XU H, GONG M, TIAN X, et al. CUFD: An encoder–decoder network for visible and infrared image fusion based on common and unique feature decomposition[J]. Computer Vision and Image Understanding, 2022, 218: 103407.

    [21] [21] Tishby N, Pereira F C, Bialek W. The information bottleneck method[J]. arXiv preprint physics/0004057, 2000.

    [22] [22] Tishby N, Zaslavsky N. Deep learning and the information bottleneck principle[C]// IEEE Information Theory Workshop (ITW). IEEE, 2015: 1-5.

    [23] [23] Shwartz-Ziv R, Tishby N. Opening the black box of deep neural networks via information[J]. arXiv preprint arXiv:1703.00810, 2017.

    [24] [24] Alemi A A, Fischer I, Dillon J V, et al. Deep variational information bottleneck[J]. arXiv preprint arXiv:1612.00410, 2016.

    [25] [25] Tishby N, Zaslavsky N. Deep learning and the information bottleneck principle[C]// IEEE Information Theory Workshop (ITW). IEEE, 2015: 1-5.

    [26] [26] ZHANG Y, LIU Y, SUN P, et al. IFCNN: A general image fusion framework based on convolutional neural network[J]. Information Fusion, 2020, 54: 99-118.

    [27] [27] MA J, CHEN C, LI C, et al. Infrared and visible image fusion via gradient transfer and total variation minimization[J]. Information Fusion, 2016, 31: 100-109.

    [28] [28] ZHANG H, MA J. SDNet: A versatile squeeze-and-decomposition network for real-time image fusion[J]. International Journal of Computer Vision, 2021, 129(10): 2761-2785.

    [29] [29] LIU Y, LIU S, WANG Z. A general framework for image fusion based on multi-scale transform and sparse representation[J]. Information Fusion, 2015, 24: 147-164.

    Tools

    Get Citation

    Copy Citation Text

    MA Luyao, LUO Xiaoqing, ZHANG Zhancheng. Infrared and Visible Image Fusion Based on Information Bottleneck Siamese Autoencoder Network[J]. Infrared Technology, 2024, 46(3): 314

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category:

    Received: Nov. 24, 2022

    Accepted: --

    Published Online: Sep. 2, 2024

    The Author Email: Xiaoqing LUO (xqluo@jiangnan.edu.cn)

    DOI:

    CSTR:32186.14.

    Topics