Acta Optica Sinica, Volume. 39, Issue 10, 1015001(2019)

Infrared and Visible Image Fusion Method Based on Convolutional Auto-Encoder and Residual Block

Zetao Jiang and Yuting He*
Author Affiliations
  • Guangxi Key Laboratory of Image and Graphic Intelligent Processing, Guilin University of Electronic Technology, Guilin, Guangxi 541004, China
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    References(18)

    [5] Li H, Wu X J, Kittler J. Infrared and visible image fusion using a deep learning framework. [C]∥2018 24th International Conference on Pattern Recognition (ICPR), August 20-24, 2018, Beijing, China. New York: IEEE, 2705-2710(2018).

    [7] Prabhakar K R, Sai Srikar V, Babu R V. DeepFuse: a deep unsupervised approach for exposure fusion with extreme exposure image pairs. [C]∥2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy. New York: IEEE, 4724-4732(2017).

    [8] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 770-778(2016).

    [13] Huang R, Zhang S, Li T Y et al. Beyond face rotation: global and local perception GAN for photorealistic and identity preserving frontal view synthesis. [C]∥2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy. New York: IEEE, 2458-2467(2017).

    [14] Tao L, Zhu C, Xiang G Q et al. LLCNN: a convolutional neural network for low-light image enhancement. [C]∥2017 IEEE Visual Communications and Image Processing (VCIP), December 10-13, 2017, St. Petersburg, FL, USA. New York: IEEE, 17614346(2017).

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    Zetao Jiang, Yuting He. Infrared and Visible Image Fusion Method Based on Convolutional Auto-Encoder and Residual Block[J]. Acta Optica Sinica, 2019, 39(10): 1015001

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    Paper Information

    Category: Machine Vision

    Received: Apr. 23, 2019

    Accepted: May. 31, 2019

    Published Online: Oct. 9, 2019

    The Author Email: He Yuting (839191881@qq.com)

    DOI:10.3788/AOS201939.1015001

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