• Acta Optica Sinica
  • Vol. 39, Issue 10, 1015001 (2019)
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
  • show less
    DOI: 10.3788/AOS201939.1015001 Cite this Article Set citation alerts
    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 Copy Citation Text show less
    References

    [1] Jiang Z T, Wu H, Zhou X L. Infrared and visible image fusion algorithm based on improved guided filtering and dual-channel spiking cortical model[J]. Acta Optica Sinica, 38, 0210002(2018).

    [2] Ma J Y, Ma Y, Li C. Infrared and visible image fusion methods and applications: a survey[J]. Information Fusion, 45, 153-178(2019). http://www.sciencedirect.com/science/article/pii/S1566253517307972

    [3] Liu X H, Chen Z B. Fusion of infrared and visible images based on multi-scale directional guided filter and convolutional sparse representation[J]. Acta Optica Sinica, 37, 1110004(2017).

    [4] Lin S Z, Han Z. Images fusion based on deep stack convolutional neural network[J]. Chinese Journal of Computers, 40, 2506-2518(2017).

    [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).

    [6] Liu Y, Chen X, Peng H et al. Multi-focus image fusion with a deep convolutional neural network[J]. Information Fusion, 36, 191-207(2017). http://www.sciencedirect.com/science/article/pii/S1566253516302081

    [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).

    [9] Lu Y S, Li Y X, Liu B et al. Hyperspectral data haze monitoring based on deep residual network[J]. Acta Optica Sinica, 37, 1128001(2017).

    [10] Liu Y, Chen X, Ward R K et al. Image fusion with convolutional sparse representation[J]. IEEE Signal Processing Letters, 23, 1882-1886(2016). http://ieeexplore.ieee.org/document/7593316/

    [11] Zhao H, Gallo O, Frosio I et al. Loss functions for image restoration with neural networks[J]. IEEE Transactions on Computational Imaging, 3, 47-57(2017). http://ieeexplore.ieee.org/document/7797130

    [12] Dong C, Loy C C, He K M et al. Image super-resolution using deep convolutional networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38, 295-307(2016). http://europepmc.org/abstract/MED/26761735

    [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).

    [15] Liu Y, Chen X, Cheng J et al. Infrared and visible image fusion with convolutional neural networks[J]. International Journal of Wavelets, Multiresolution and Information Processing, 16, 1850018(2018). http://www.worldscientific.com/doi/10.1142/S0219691318500182

    [16] Ma J L, Zhou Z Q, Wang B et al. Infrared and visible image fusion based on visual saliency map and weighted least square optimization[J]. Infrared Physics & Technology, 82, 8-17(2017). http://www.sciencedirect.com/science/article/pii/S1350449516305928

    [17] Zhang Y, Zhang L J, Bai X Z et al. Infrared and visual image fusion through infrared feature extraction and visual information preservation[J]. Infrared Physics & Technology, 83, 227-237(2017). http://adsabs.harvard.edu/abs/2017InPhT..83..227Z

    [18] Chen M S. Image fusion of visual and infrared image based on NSCT and compressed sensing[J]. Journal of Image and Graphics, 21, 39-44(2016).

    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
    Download Citation