Citation: | 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. |
[1] |
Paramanandham N, Rajendiran K. Multi sensor image fusion for surveillance applications using hybrid image fusion algorithm[J]. Multimedia Tools and Applications, 2018, 77(10): 12405-12436. DOI: 10.1007/s11042-017-4895-3
|
[2] |
ZHANG Xingchen, YE Ping, QIAO Dan, et al. Object fusion tracking based on visible and infrared images: a comprehensive review[J]. Information Fusion, 2020, 63: 166-187. DOI: 10.1016/j.inffus.2020.05.002
|
[3] |
TU Zhengzheng, LI Zhun, LI Chenglong, et al. Multi-interactive dual- decoder for RGB-thermal salient object detection[J]. IEEE Transactions on Image Processing, 2021, 30: 5678-5691. DOI: 10.1109/TIP.2021.3087412
|
[4] |
汪荣贵, 王静, 杨娟, 等. 基于红外和可见光模态的随机融合特征金子塔行人重识别[J]. 光电工程, 2020, 47(12): 190669. Doi: 10.12086/oee.2020.190669.
WANG Ronggui, WANG Jing, YANG Juan, et al. Random feature fusion of golden Tower for pedestrian rerecognition based on infrared and visible modes[J]. Opto-Electronic Engineering, 2020, 47(12): 190669. Doi: 10.12086/oee.2020.190669
|
[5] |
WANG Zhishe, XU Jiawei, JIANG Xiaolin, et al. Infrared and visible image fusion via hybrid decomposition of NSCT and morphological sequential toggle operator[J]. Optik, 2020, 201: 163497. DOI: 10.1016/j.ijleo.2019.163497
|
[6] |
LI Hui, WU Xiaojun, Kittle J. MDLatLRR: a novel decomposition method for infrared and visible image fusion[J]. IEEE Transactions on Image Processing, 2020, 29: 4733-4746. DOI: 10.1109/TIP.2020.2975984
|
[7] |
孙彬, 诸葛吴为, 高云翔, 等. 基于潜在低秩表示的红外和可见光图像融合[J]. 红外技术, 2022, 44(8): 853-862. http://hwjs.nvir.cn/article/id/7fc3a60d-61bb-454f-ad00-e925eeb54576
SUN Bin, ZHUGE Wuwei, GAO Yunxiang et al. Infrared and visible image fusion based on potential low-rank representation[J]. Infrared Technology, 2022, 44(8): 853-862. http://hwjs.nvir.cn/article/id/7fc3a60d-61bb-454f-ad00-e925eeb54576
|
[8] |
MA Jinlei, ZHOU Zhiqiang, WANG Bo, et al. Infrared and visible image fusion based on visual saliency map and weighted least square optimization[J]. Infrared Physics & Technology, 2017, 82: 8-17.
|
[9] |
KONG Weiwei, LEI Yang, ZHAO Huaixun. Adaptive fusion method of visible light and infrared images based on non-subsampled shearlet transform and fast non-negative matrix factorization[J]. Infrared Physics & Technology, 2014, 67: 161-172.
|
[10] |
姜迈, 沙贵君, 李宁. 基于PUCS与DTCWT的红外与弱可见光图像融合[J]. 红外技术, 2022, 44(7): 716-725. http://hwjs.nvir.cn/article/id/ee43f5b8-9a1f-441c-9d95-e339989d8954
JIANG Mai, SHA Guijun, LI Ning. Infrared and inferior visible image fusion based on PUCS and DTCWT [J]. Infrared Technology, 2022, 44(7): 716-725. http://hwjs.nvir.cn/article/id/ee43f5b8-9a1f-441c-9d95-e339989d8954
|
[11] |
WANG Zhishe, YANG Fengbao, PENG Zhihao, et al. Multi-sensor image enhanced fusion algorithm based on NSST and top-hat transformation[J]. Optik, 2015, 126(23): 4184-4190. DOI: 10.1016/j.ijleo.2015.08.118
|
[12] |
LIU Yu, CHEN Xun, PENG Hu, et al. Multi-focus imagefusion with a deep convolutional neural network[J]. Information Fusion, 2017, 36: 191-207. DOI: 10.1016/j.inffus.2016.12.001
|
[13] |
ZHANG Hao, XU Han, TIAN Xin, et al. Image fusion meets deep learning: A survey and perspective[J]. Information Fusion, 2021, 76: 323-336. DOI: 10.1016/j.inffus.2021.06.008
|
[14] |
ZHANG Yu, LIU Yu, SUN Peng, et al. IFCNN: A general image fusion framework based on convolutional neural network[J]. Information Fusion, 2020, 54: 99-118. DOI: 10.1016/j.inffus.2019.07.011
|
[15] |
LI Hui, WU Xiaojun. DenseFuse: a fusion approach to infrared and visible images[J]. IEEE Transactions on Image Processing, 2019, 28(5): 2614- 2623. DOI: 10.1109/TIP.2018.2887342
|
[16] |
LI Hui, WU Xiaojun, Kittler J. RFN-Nest: An end-to-end residual fusion network for infrared and visible images[J]. Information Fusion, 2021, 73: 72-86. DOI: 10.1016/j.inffus.2021.02.023
|
[17] |
JIAN Lihua, YANG Xiaomin, LIU Zheng, et al. SEDRFuse: A symmetric encoder–decoder with residual block network for infrared and visible image fusion[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 70: 1-15.
|
[18] |
ZHANG Hao, XU Han, XIAO Yang, et al. Rethinking the image fusion: A fast unified image fusion network based on proportional maintenance of gradient and intensity[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(7): 12797-12804.
|
[19] |
WANG Zhishe, WANG Junyao, WU Yuanyuan, et al. UNFusion: a unified multi-scale densely connected network for infrared and visible image fusion[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2022, 32(6): 3360- 3374.
|
[20] |
WANG Zhishe; WU Yuanyuan; WANG Junyao, et al. Res2Fusion: infrared and visible image fusion based on dense Res2net and double non-local attention models[J]. IEEE Transactions on Instrumentation and Measurement, 2022, 71: 1-12.
|
[21] |
MA Jiayi, YU Wei, LIANG Pengwei, et al. FusionGAN: a generative adversarial network for infrared and visible image fusion[J]. Information Fusion, 2019, 48: 11-26.
|
[22] |
MA Jiayi, ZHANG Hao, SHAO Zhenfeng, et al. GANMcC: a generative adversarial network with multiclassification constraints for infrared and visible image fusion[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1-14.
|
[23] |
王志社, 邵文禹, 杨风暴, 等. 红外与可见光图像交互注意力生成对抗融合方法[J]. 光子学报, 2022, 51(4): 318-328. https://www.cnki.com.cn/Article/CJFDTOTAL-GZXB202204029.htm
WANG Zhishe, SHAO Wenyu, YANG Fengbao, et al. A generative antagonism fusion method for interactive attention of infrared and visible images [J]. Acta Photonica Sinica, 2022, 51(4): 318-328. https://www.cnki.com.cn/Article/CJFDTOTAL-GZXB202204029.htm
|
[24] |
LI Jing, ZHU Jianming, LI Chang, et al. CGTF: Convolution-Guided Transformer for Infrared and Visible Image Fusion [J]. IEEE Transactions on Instrumentation and Measurement. 2022, 71: 1-14.
|
[25] |
RAO Dongyu, WU Xiaojun, XU Tianyang. TGFuse: An infrared and visible image fusion approach based on transformer and generative adversarial network [J/OL].arXiv preprint arXiv: 2201.10147. 2022.
|
[26] |
WANG Zhishe, CHEN Yanlin, SHAO Wenyu, et al. SwinFuse: a residual swin transformer fusion network for infrared and visible images[J/OL]. arXiv preprint arXiv: 2204.11436. 2022.
|
[27] |
ZHAO Haibo, NIE Rencan. DNDT: infrared and visible image fusion via DenseNet and dual-transformer[C]// International Conference on Information Technology and Biomedical Engineering (ICITBE), 2021: 71-75.
|
[28] |
VS V, Valanarasu J M J, Oza P, et al. Image fusion transformer [J/OL]. arXiv preprint arXiv: 2107.09011. 2021.
|
[29] |
LIU Ze, LIN Yutong, CAO Yue, et al. Swin transformer: Hierarchical vision transformer using shifted windows[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021: 10012-10022.
|
[30] |
TOET A. TNO Image Fusion Datase[DB/OL]. [2014-04-26].https://figshare.com/articles/TNImageFusionDataset/1008029.
|
[31] |
XU Han. Roadscene Database[DB/OL]. [2020-08-07].https://github.com/hanna-xu/RoadScene.
|
[32] |
LI Hui, WU Xiaojun, Kittle J. MDLatLRR: a novel decomposition method for infrared and visible image fusion[J]. IEEE Transactions on Image Processing, 2020, 29: 4733-4746.
|
[1] | YE Zhihui, WU Jian, ZHAO Xiaozhong, WANG Wenjuan, SHAO Xinguang. Multimodal Object Detection Based on Feature Interaction and Adaptive Grouping Fusion[J]. Infrared Technology , 2025, 47(4): 468-474. |
[2] | LI Minglu, WANG Xiaoxia, HOU Maoxin, YANG Fengbao. An Object Detection Algorithm Based on Infrared-Visible Feature Enhancement and Fusion[J]. Infrared Technology , 2025, 47(3): 385-394. |
[3] | QIAO Zhiping, HUANG Jingying, WANG Lihe. Infrared Dual-band Target Detecting Fusion Algorithm Based on Multiple Features[J]. Infrared Technology , 2024, 46(10): 1201-1208. |
[4] | CHEN Sijing, FU Zhitao, LI Ziqian, NIE Han, SONG Jiawen. A Visible and Infrared Image Fusion Algorithm Based on Adaptive Enhancement and Saliency Detection[J]. Infrared Technology , 2023, 45(9): 907-914. |
[5] | QU Haicheng, HU Qianqian, ZHANG Xuecong. Infrared and Visible Image Fusion Combining Information Perception and Multiscale Features[J]. Infrared Technology , 2023, 45(7): 685-695. |
[6] | WANG Fang, LI Chuanqiang, WU Bo, YU Kun, JIN Chan, CHEN Yake, LU Yinghui. Infrared Small Target Detection Method Based on Multi-Scale Feature Fusion[J]. Infrared Technology , 2021, 43(7): 688-695. |
[7] | WEI Shuigen, WANG Chengwei, ZHANG Congxuan, YAN Huibin. Infrared Dim Target Detection Based on Multi-information Fusion[J]. Infrared Technology , 2019, 41(9): 857-865. |
[8] | YUAN Jingzhen, JIN Wang. Multi-scale Moving Target Detection Method Based on Improved Bilateral Filtering[J]. Infrared Technology , 2019, 41(8): 772-777. |
[9] | ZHANG Shuang-lei, CHEN Fan-sheng, WANG Tao. A Dim Small Target Detection Algorithm Based on Multi-Features Fusion Algorithm[J]. Infrared Technology , 2015, (8): 635-641. |
[10] | XIONG Da-rong, YANG Xuan. Long-Range Target Detection Based on Multisensor Data Fusion[J]. Infrared Technology , 2006, 28(12): 695-698. DOI: 10.3969/j.issn.1001-8891.2006.12.004 |