Citation: | YANG Jiuzhang, LIU Weijian, CHENG Yang. Asymmetric Infrared and Visible Image Fusion Based on Contrast Pyramid and Bilateral Filtering[J]. Infrared Technology , 2021, 43(9): 840-844. |
[1] |
Waxman A M, Gove A N, Fay D A, et al. Color night vision: opponent processing in the fusion of visible and IR imagery[J]. Neural Networks, 1997, 10(1): 1-6. http://www.onacademic.com/detail/journal_1000034198621910_6953.html
|
[2] |
XIANG T, YAN L, GAO R. A fusion algorithm for infrared and visible images based on adaptive dual-channel unit-linking PCNN in NSCT domain[J]. Infrared Physics & Technology, 2015, 69: 53-61. http://www.onacademic.com/detail/journal_1000037435766010_b6cd.html
|
[3] |
ZHAO J, GAO X, CHEN Y, et al. Multi-window visual saliency extraction for fusion of visible and infrared images[J]. Infrared Physics & Technology, 2016, 76: 295-302. http://smartsearch.nstl.gov.cn/paper_detail.html?id=4f0b14c597a48653341d44502ab3dc75
|
[4] |
YAN L, CAO J, Rizvi S, et al. Improving the performance of image fusion based on visual saliency weight map combined with CNN[J]. IEEE Access, 2020, 8(99): 59976-59986. http://ieeexplore.ieee.org/document/9044861
|
[5] |
Lewis J J, Robert J. O'Callaghan, Nikolov S G, et al. Pixel- and region-based image fusion with complex wavelets[J]. Information Fusion, 2007, 8(2): 119-130. DOI: 10.1016/j.inffus.2005.09.006
|
[6] |
赵立昌, 张宝辉, 吴杰, 等. 基于灰度能量差异性的红外与可见光图像融合[J]. 红外技术, 2020, 42(8): 775-782. http://hwjs.nvir.cn/article/id/hwjs202008012
ZHAO Lichang, ZHANG Baohui, WU Jie, et al. Fusion of infrared and visible images based on gray energy difference[J]. Infrared Technology, 2020, 42(8): 775-782. http://hwjs.nvir.cn/article/id/hwjs202008012
|
[7] |
崔晓荣, 沈涛, 黄建鲁, 等. 基于BEMD改进的视觉显著性红外和可见光图像融合[J]. 红外技术, 2020, 42(11): 1061-1071. http://hwjs.nvir.cn/article/id/c89c0447-6d07-4a75-99f6-1bf8681cf588
CUI Xiaorong, SHEN Tao, HUANG Jianlu, et al. Infrared and visible image fusion based on bemd and improved visual saliency[J]. Infrared Technology, 2020, 42(11): 1061-1071. http://hwjs.nvir.cn/article/id/c89c0447-6d07-4a75-99f6-1bf8681cf588
|
[8] |
李辰阳, 丁坤, 翁帅, 等. 基于改进谱残差显著性图的红外与可见光图像融合[J]. 红外技术, 2020, 42(11): 1042-1047. http://hwjs.nvir.cn/article/id/6e57a6fb-ba92-49d9-a000-c00e7a933365
LI Chenyang, DING Kun, WENG Shuai, et al. Image fusion of infrared and visible images based on residual significance[J]. Infrared Technology, 2020, 42(11): 1042-1047. http://hwjs.nvir.cn/article/id/6e57a6fb-ba92-49d9-a000-c00e7a933365
|
[9] |
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: 1-13. DOI: 10.1016/j.inffus.2015.11.002
|
[10] |
Toet A. Image fusion by a ratio of low-pass pyramid[J]. Pattern Recognition Letters, 1989, 9: 245-253. DOI: 10.1016/0167-8655(89)90003-2
|
[11] |
Akerman A. Pyramidal techniques for multisensor fusion[C]// Proceedings of SPIE the International Society for Optical Engineering, 1992, 1828: 124-131.
|
[12] |
LI Huafeng, QIU Hongmei, YU Zhengtao, et al. Infrared and visible image fusion scheme based on NSCT and low-level visual features[J]. Infrared Physics and Technology, 2016, 76: 174-184. DOI: 10.1016/j.infrared.2016.02.005
|
[13] |
彭进业, 王珺, 何贵青, 等. 基于非下采样Contourlet变换和稀疏表示的红外与可见光图像融合方法[J]. 兵工学报, 2013, 34(7): 815-820. https://www.cnki.com.cn/Article/CJFDTOTAL-BIGO201307003.htm
PENG Jinye, WANG Jun, HE Guiqing, et al. Fusion method for visible and infrared images based on non-subsampled Contourlet transform and sparse representation[J]. Acta Armamentarii, 2013, 34(7): 815-820. https://www.cnki.com.cn/Article/CJFDTOTAL-BIGO201307003.htm
|
[14] |
Pajares G, Jesús Manuel de la Cruz. A wavelet-based image fusion tutorial[J]. Pattern Recognition, 2004, 37(9): 1855-1872. DOI: 10.1016/j.patcog.2004.03.010
|
[15] |
朱攀, 刘泽阳, 黄战华. 基于DTCWT和稀疏表示的红外偏振与光强图像融合[J]. 光子学报, 2017, 46(12): 213-221. https://www.cnki.com.cn/Article/CJFDTOTAL-GZXB201712028.htm
ZHU Pan, LIU Zeyang, HUANG Zhanhua. Infrared polarization and intensity image fusion based on dual-tree complex wavelet transform and sparse representation[J]. Acta Photonica Sinica, 2013, 34(7): 815-820. https://www.cnki.com.cn/Article/CJFDTOTAL-GZXB201712028.htm
|
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