EDP Sciences logo
Submit your paper
Search
Menu
All issues Volume 38 / No 4 (August 2020) JNWPU, 38 4 (2020) 904-912 References
Open Access
Issue
JNWPU
Volume 38, Number 4, August 2020
Page(s) 904 - 912
DOI https://doi.org/10.1051/jnwpu/20203840904
Published online 06 October 2020
  1. WANG Fengcheng, CHENG Yongmei, LI Hui. Image Fusion Algorithm of Focal Region Detection and TAM-SCM Based on SHT Domainxy1 Journal of Northwestern Polytechnical University, 2019, 37 (1): 114– 121 [Article] (in Chinese) [CrossRef] [Google Scholar]
  2. MA J Y, MA Y, LI C. Infrared and Visible Image Fusion Methods and Applications:a Survey[J]. Information Fusion, 2019, 45: 153– 178 [Article] [CrossRef] [Google Scholar]
  3. LIU Zhanwen, FENG Yan, LI Xu, et al. A Fusion Algorithm for Infrared and Visible Images Based on Dictionary Learning and NSSTxy1 Journal of Northwestern Polytechnical University, 2017, 35 (3): 408– 413 [Article] (in Chinese) [Google Scholar]
  4. LIN Zihui, WEI Yuxing, ZHANG Jianlin, et al. Image Fusion of Infrared and Visible Images Based on Saliency Map[J]. Infrared Technology, 2019, 41 (7): 640– 645 [Article] (in Chinese) [Google Scholar]
  5. LIU Yanyu, ZHOU Dongming, NIE Rencan, et al. Infrared and Visible Image Fusion Scheme Using Low Rank Representation and Dictionary Learning[J]. Journal of Yunnan University, 2019, 41 (4): 689– 698 [Article] (in Chinese) [Google Scholar]
  6. HUANG Fusheng, LIN Suzhen. Comparison of Multi-Band Image Fusion Rules Based on Laplacian Pyramid Transform Method[J]. Infrared Technology, 2019, 41 (1): 64– 71 [Article] (in Chinese) [Google Scholar]
  7. FENG Yufang, YAN Hong, LU Houqing, et al. Infrared and Visible Image Fusion Based on Improved Convolutional Neural Network[J/OL]. (2019-08-18)[2019-09-10]. [Article] (in Chinese) [Google Scholar]
  8. PRABHAKAR K R, SRIKAR V S, BABU R V. DeepFuse: a Deep Unsupervised Approach for Exposure Fusion with Extreme Exposure Image Pairs[C]//2017 International Conference on Computer Vision, Venice, Italy, 2017: 4724–4732 [Google Scholar]
  9. MA J Y, YU W, LIANG P W. FusionGAN:a Generative Adversarial Network for Infrared and Visible Image Fusion[J]. Information Fusion, 2019, 48: 11– 26 [Article] [CrossRef] [Google Scholar]
  10. RONNEBERGER O, FISCHER P, BROX T. U-net: Convolutional Networks for Biomedical Image Segmentation[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich German, 2015, 234–241 [Google Scholar]
  11. TOET Alexander. TNO Image Fusion Dataset[EB/OL]. (2014-04-26)[2019-05-01]. [Article] [Google Scholar]
  12. LIU Y, WANG Z F. Simultaneous Image Fusion and Denoising with Adaptive Sparse Representation[J]. Image Processing Let, 2015, 9 (5): 347– 357 [Article] [CrossRef] [Google Scholar]
  13. NENCINI F, GARZELLI A, BARONTI S, et al. Remote Sensing Image Fusion Using the Curvelet Transform[J]. Information Fusion, 2007, 8 (2): 143– 156 [Article] [CrossRef] [Google Scholar]
  14. LEWIS J J, O'CALLAGHAN R J, NIKOLOV S G, et al. Pixel-and Region-Based Image Fusion with Complex Wavelets[J]. Information Fusion, 2007, 8 (2): 119– 130 [Article] [CrossRef] [Google Scholar]
  15. BAVIRISETTI D P, XIAO G, LIU G. Multi-Sensor Image Fusion Based on Fourth Order Partial Differential Equations[C]//Proceedings of the International Conference on Information Fusion, 2017: 1–9 [Google Scholar]
  16. LI S, KANG X D, HU J W. Image Fusion with Guided Filtering[J]. IEEE Trans on Image Processing, 2013, 22 (7): 2864– 2875 [Article] [CrossRef] [Google Scholar]
  17. KONG W, ZHANG L, LEI Y. Novel Fusion Method for Visible Light and Infrared Images Based on NSST-SF-PCNNxy1 Infrared Physics & Technology, 2014, 65: 103– 112 [Article] [CrossRef] [Google Scholar]
  18. BAVIRISETTI D P, DHULI R. Two-Scale Image Fusion of Visible and Infrared Images Using Saliency Detectionxy1 Infrared Physics & Technology, 2016, 76: 52– 64 [Article] [CrossRef] [Google Scholar]
  19. MA J, CHEN C, LI C. Infrared and Visible Image Fusion via Gradient Transfer and Total Variation Minimization[J]. Inform Fusion, 2016, 31: 100– 109 [Article] [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.