Citation: | SUN Bin, ZHUGE Wuwei, GAO Yunxiang, WANG Zixuan. Infrared and Visible Image Fusion Based on Latent Low-Rank Representation[J]. Infrared Technology , 2022, 44(8): 853-862. |
[1] |
沈英, 黄春红, 黄峰, 等. 红外与可见光融合技术的研究进展[J]. 红外与激光工程, 2021(9): 1-16. https://www.cnki.com.cn/Article/CJFDTOTAL-HWYJ202109014.htm
SHEN Y, HUANG C H, HUANG F, et al. Infrared and visible image fusion: review of key technologies [J]. Infrared and Laser Engineering, 2021(9): 1-16. https://www.cnki.com.cn/Article/CJFDTOTAL-HWYJ202109014.htm
|
[2] |
杨孙运, 奚峥皓, 王汉东, 等. 基于NSCT和最小化-局部平均梯度的图像融合[J]. 红外技术, 2021, 43(1): 13-20. https://www.cnki.com.cn/Article/CJFDTOTAL-HWJS202101003.htm
YANG S Y, XI Z H, WANG H D, et al. Image fusion based on NSCT and minimum-local mean gradient [J]. Infrared Technology, 2021, 43(1): 13-20. https://www.cnki.com.cn/Article/CJFDTOTAL-HWJS202101003.htm
|
[3] |
ZHANG X, YE P, XIAO G. VIFB: A Visible and Infrared Image Fusion Benchmark[C]// IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops IEEE, 2020: 468-478.
|
[4] |
CHEN J, WU K, CHENG Z, et al, A saliency-based multiscale approach for infrared and visible image fusion[J]. Signal Processing, 2021, 182(4): 107936.
|
[5] |
LIU Y, CHEN X, Ward R K, et al. Image fusion with convolutional sparse representation[J]. IEEE signal processing letters, 2016, 23(12): 1882-1886. DOI: 10.1109/LSP.2016.2618776
|
[6] |
MA J Y, 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. DOI: 10.1016/j.inffus.2016.02.001
|
[7] |
ZHANG H, XU H, TIAN X, et al. Image fusion meets deep learning: A survey and perspective[J]. Information Fusion, 2021, 76(11): 323-336.
|
[8] |
Bavirisetti D P, D Huli R. Two-scale image fusion of visible and infrared images using saliency detection[J]. Infrared Physics & Technology, 2016, 76: 52-64.
|
[9] |
LI H, WU X J. Infrared and visible image fusion using latent low-rank representation[J/OL] [2018-04-24]. arXiv preprint. https://arxiv.org/abs/1804.08992.
|
[10] |
LIU G, YAN S. Latent low-rank representation for subspace segmentation and feature extraction[C]//International Conference on Computer Vision, 2011: 1615-1622.
|
[11] |
刘琰煜, 周冬明, 聂仁灿, 等. 低秩表示和字典学习的红外与可见光图像融合算法[J]. 云南大学学报: 自然科学版, 2019, 41(4): 689-698. https://www.cnki.com.cn/Article/CJFDTOTAL-YNDZ201904007.htm
LIU Y Y, ZHOU D M, NIE R C, et al. Infrared and visible image fusion scheme using low rank representation and dictionary learning[J]. Journal of Yunnan University: Natural Sciences Edition, 2019, 41(4): 689-698. https://www.cnki.com.cn/Article/CJFDTOTAL-YNDZ201904007.htm
|
[12] |
王凡, 王屹, 刘洋. 利用结构化和一致性约束的稀疏表示模型进行红外和可见光图像融合[J]. 信号处理, 2020, 36(4): 572-583. https://www.cnki.com.cn/Article/CJFDTOTAL-XXCN202004012.htm
WANG F, WANG Y, LIU Y. Infrared and visible image fusion method based on sparse representation with structured and spatial consistency constraints[J]. Journal of Signal Processing, 2020, 36(4): 572-583. https://www.cnki.com.cn/Article/CJFDTOTAL-XXCN202004012.htm
|
[13] |
LIU Y, LIU S, WANG Z. A general framework for image fusion based on multi-scale transform and sparse representation[J]. Information Fusion, 2015(24): 147-164.
|
[14] |
LI S, KANG X, FANG L, et al. Pixel-level image fusion: a survey of the state of the art[J]. Information Fusion, 2017, 33: 100-112. DOI: 10.1016/j.inffus.2016.05.004
|
[15] |
Toet Alexander. The TNO Multiband image data collection[J]. Data in Brief, 2017, 15: 249-251. DOI: 10.1016/j.dib.2017.09.038
|
[16] |
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.
|
[17] |
CHEN J, LI X J, LUO L B, et al. Infrared and visible image fusion based on target-enhanced multiscale transform decomposition[J]. Information Sciences, 2020, 508: 64-78. DOI: 10.1016/j.ins.2019.08.066
|
[18] |
Ba Virisetti D P. Multi-sensor image fusion based on fourth order partial differential equations[C]//20th International Conference on Information Fusion (Fusion) of IEEE, 2017: 1-9.
|
[19] |
MA J Y, ZHOU Y. Infrared and visible image fusion via gradientlet filter[J]. Computer Vision and Image Understanding, 2020, 197: 103016.
|
[20] |
Bavirisetti D P, D Huli R. Two-scale image fusion of visible and infrared images using saliency detection[J]. Infrared Physics & Technology, 2016, 76: 52-64.
|
[21] |
Naidu V. Image fusion technique using multi-resolution singular value decomposition[J]. Defence Science Journal, 2011, 61(5): 479-484. DOI: 10.14429/dsj.61.705
|
[22] |
刘智嘉, 贾鹏, 夏寅辉. 基于红外与可见光图像融合技术发展与性能评价[J]. 激光与红外, 2019, 49(5): 123-130. https://www.cnki.com.cn/Article/CJFDTOTAL-JGHW201905022.htm
LIU Z J, JIA P, XIA Y H, et al. Development and performance evaluation of infrared and visual image fusion technology[J]. Laser & Infrared, 2019, 49(5): 123-130. https://www.cnki.com.cn/Article/CJFDTOTAL-JGHW201905022.htm
|
[1] | XU Guangxian, ZHOU Weijie, MA Fei. Fusion of Hyperspectral and Multispectral Images Using a CNN Joint Multi-Scale Transformer[J]. Infrared Technology , 2025, 47(1): 52-62. |
[2] | CHEN Chaoyang, JIANG Yuanyuan. Infrared and Visible Image Fusion Based on Deep Image Decomposition[J]. Infrared Technology , 2024, 46(12): 1362-1370. |
[3] | CHEN Yanlin, WANG Zhishe, SHAO Wenyu, YANG Fan, SUN Jing. Multi-scale Transformer Fusion Method for Infrared and Visible Images[J]. Infrared Technology , 2023, 45(3): 266-275. |
[4] | LI Yongping, YANG Yanchun, DANG Jianwu, WANG Yangping. Infrared and Visible Image Fusion Based on Transform Domain VGGNet19[J]. Infrared Technology , 2022, 44(12): 1293-1300. |
[5] | SHEN Xuechen, LIU Jun, GAO Ming. Polarizing Image Fusion Algorithm Based on Wavelet-Contourlet Transform[J]. Infrared Technology , 2020, 42(2): 182-189. |
[6] | HUANG Fusheng, LIN Suzhen. Multi-Band Image Fusion Rules Comparison Based on the Laplace Pyramid Transformation Method[J]. Infrared Technology , 2019, 41(1): 64-71. |
[7] | GE Man-ling, WEI Meng-jia, YANG Hao-yu, SHI Peng-fei, CHEN Ying, FU Xiao-xuan, ZHANG Ji-chang, CHEN Yu-min. Simulation and Real-time Application of Infrared Thermal Image Rectification Technology of Rectification Algorithm of Double Threshold Segmentation in Pseudo-color Conversion[J]. Infrared Technology , 2015, (4): 272-276. |
[8] | LI Wei-wei, YANG Feng-bao, LIN Su-zhen, HE Dong. Study on Pseudo-color Fusion of Infrared Polarization and Intensity Image[J]. Infrared Technology , 2012, 34(2): 109-113. DOI: 10.3969/j.issn.1001-8891.2012.02.010 |
[9] | LUO Shao-peng, LU Xun. A Self-feedback Multi-focus Color Image Fusion Based on Lifting Wavelet Transform And Features of Human Vision System[J]. Infrared Technology , 2008, 30(1): 31-34,38. DOI: 10.3969/j.issn.1001-8891.2008.01.008 |
[10] | XU Kai-yu, LI Shuang-yi. A Images Fusion Algorithm Based on Wavelet Transform[J]. Infrared Technology , 2007, 29(8): 455-458. DOI: 10.3969/j.issn.1001-8891.2007.08.006 |