Open Access
Volume 40, Number 6, December 2022
Page(s) 1404 - 1413
Published online 10 February 2023
  1. KUMAR S B K. Image fusion based on pixel significance using cross bilateral filter[J]. Signal Image & Video Processing, 2015, 9(5): 1193–1204. [Article] [CrossRef] [Google Scholar]
  2. BAVIRISETTI D P, DHULI R. Fusion of Infrared and visible sensor images based on anisotropic diffusion and Karhunen-Loeve transform[J]. IEEE Sensors Journal, 2015, 16(1): 203–209. [Article] [Google Scholar]
  3. 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. [Article] [CrossRef] [Google Scholar]
  4. FU Z Z, ZHAO Y F, XU Y W, et al. Gradient structural similarity based gradient filtering for multi-modal image fusion[J]. Information Fusion, 2020, 53: 251–268. [Article] [CrossRef] [Google Scholar]
  5. LI H, WU X J. Infrared and visible image fusion using latent low-rank representation[EB/OL]. (2018-4-24)[2022-11-11]. [Article] [Google Scholar]
  6. PEI Peipei, YANG Yanchun, DANG Jianwu, et al. Infrared and visible image fusion method based on rolling guidance filter and convolution sparse representation[J]. Laser & Optoelectronics Progress, 2022, 59(12): 56–63. [Article] (in Chinese) [Google Scholar]
  7. YANG Yong, LI Luyi, HUANG Shuying, et al. Remote sensing image fusion with convolutional sparse presentation based on adaptive dictionary learning[J]. Journal of Signal Processing, 2020, 36(1): 125–138. [Article] (in Chinese) [Google Scholar]
  8. ZHOU Z, WANG B, LI S, et al. Perceptual fusion of infrared and visible images through a hybrid multi-scale decomposition with gaussian and bilateral filters[J]. Information Fusion, 2016, 30: 15–26. [Article] [CrossRef] [Google Scholar]
  9. PRABHAKAR K R, SRIKAR V S, BABU R V. DeepFuse: a deep unsupervised approach for exposure fusion with extreme exposure image pairs[C]//Proceeding of the 2017 IEEE International Conference on Computer Vision, 2017 [Google Scholar]
  10. 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. [Article] [CrossRef] [Google Scholar]
  11. LI H, WU X J, KITTLER J. Infrared and visible image fusion using a deep learning framework[C]//Proceeding of the 24th International Conference on Pattern Recognition, 2018 [Google Scholar]
  12. LI H, WU X J, DURRANI T S. Infrared and visible image fusion with resnet and zero-phase component analysis[J]. Infrared Physics & Technology, 2019, 102: 103039 [CrossRef] [Google Scholar]
  13. CHEN Guangqiu, WANG Shuai, HUANG Dandan, et al. Infrared and visible image fusion based on multiscale local extrema decomposition and resnet152[J]. Journal of Optoelectronics·Laser, 2022, 33(3): 283–295. [Article] (in Chinese) [Google Scholar]
  14. ZHANG H, WU C, ZHANG Z, et al. ResNeSt: split-attention networks[C]//Proceeding of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022 [Google Scholar]
  15. HE K, ZHANG X, REN S, et al. Deep residual learning for image recognitio[C]//Proceeding of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016 [Google Scholar]
  16. JIE H, LI S, GANG S, et al. Squeeze-and-excitation networks[J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 2020, 42(8): 2011–2023. [Article] [CrossRef] [Google Scholar]
  17. LI X, WANG W, HU X, et al. Selective kernel networks[C]//Proceeding of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020 [Google Scholar]
  18. DENG J, DONG W, SOCHER R, et al. ImageNet: a large-scale hierarchical image database[C]//Proceeding of the 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2009: 248–255 [Google Scholar]
  19. MAAS A, HANNUN A, NG A. Rectifier nonlinearities improve neural network acoustic models[C]//Proceeding of the 30th International Conference on Machine Learning, 2013 [Google Scholar]
  20. CLEVERT D, UNTERTHINER T, HOCHREITER S. Fast and accurate deep network learning by exponential linear units(ELUs)[EB/OL]. (2015-11-23)[2022-11-11]. [Article] [Google Scholar]
  21. BISWAS K, KUMAR S, BANERJEE S, et al. SMU: smooth activation function for deep networks using smoothing maximum technique[EB/OL]. (2021-11-23)[2022-11-11]. [Article] [Google Scholar]
  22. MA Qi, ZHU Bin, ZHANG Hongwei. Dual-band image fusion method based on VGGNet[J]. Laser & Infrared, 2019, 49(11): 7. [Article] [Google Scholar]
  23. TOET A. The TNO multiband image data collection[J]. Data in Brief, 2017, 15: 249–251. [Article] [CrossRef] [Google Scholar]
  24. ZHANG X, YE P, XIAO G. VIFB: a visible and infrared image fusion benchmark[C]//Proceeding of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020 [Google Scholar]
  25. XU Dongdong. Research on infrared and visible image fusion based on unsupervised deep learning[D]. Changchun: Chinese Academy of Sciences, 2020: 72–74 (in Chinese) [Google Scholar]
  26. JAGALINGAM P, HEGDE A V. A review of quality metrics for fused image[J]. Aquatic Procedia, 2015, 4: 133–142. [Article] [CrossRef] [Google Scholar]
  27. WANG Z, BOVIK A C, SHEIKH H R, et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Trans on Image Processing, 2004, 13(4): 600–612. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  28. ESKICIOGLU A M, FISHER P S. Image quality measures and their performance[J]. IEEE Trans on Communications, 1995, 43(12): 2959–2965. [Article] [CrossRef] [Google Scholar]
  29. XYDEAS C S, PV V. Objective image fusion performance measure[J]. Military Technical Courier, 2000, 56(4): 181–193 [Google Scholar]

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