MGFCTFuse: A Novel Fusion Approach for Infrared and Visible Images

被引:3
作者
Hao, Shuai [1 ]
Li, Jiahao [1 ]
Ma, Xu [1 ]
Sun, Siya [1 ]
Tian, Zhuo [1 ]
Cao, Le [1 ]
机构
[1] Xian Univ Sci & Technol, Coll Elect & Control Engn, Xian 710054, Peoples R China
基金
中国国家自然科学基金;
关键词
image fusion; mutually guided image filtering; detail enhancement; cross-transmission; TRANSFORM;
D O I
10.3390/electronics12122740
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Traditional deep-learning-based fusion algorithms usually take the original image as input to extract features, which easily leads to a lack of rich details and background information in the fusion results. To address this issue, we propose a fusion algorithm, based on mutually guided image filtering and cross-transmission, termed MGFCTFuse. First, an image decomposition method based on mutually guided image filtering is designed, one which decomposes the original image into a base layer and a detail layer. Second, in order to preserve as much background and detail as possible during feature extraction, the base layer is concatenated with the corresponding original image to extract deeper features. Moreover, in order to enhance the texture details in the fusion results, the information in the visible and infrared detail layers is fused, and an enhancement module is constructed to enhance the texture detail contrast. Finally, in order to enhance the communication between different features, a decoding network based on cross-transmission is designed within feature reconstruction, which further improves the quality of image fusion. In order to verify the advantages of the proposed algorithm, experiments are conducted on the TNO, MSRS, and RoadScene image fusion datasets, and the results demonstrate that the algorithm outperforms nine comparative algorithms in both subjective and objective aspects.
引用
收藏
页数:19
相关论文
共 46 条
[1]   Multi-scale Guided Image and Video Fusion: A Fast and Efficient Approach [J].
Bavirisetti, Durga Prasad ;
Xiao, Gang ;
Zhao, Junhao ;
Dhuli, Ravindra ;
Liu, Gang .
CIRCUITS SYSTEMS AND SIGNAL PROCESSING, 2019, 38 (12) :5576-5605
[2]   Two-scale image fusion of visible and infrared images using saliency detection [J].
Bavirisetti, Durga Prasad ;
Dhuli, Ravindra .
INFRARED PHYSICS & TECHNOLOGY, 2016, 76 :52-64
[3]   Fusion of Infrared and Visible Sensor Images Based on Anisotropic Diffusion and Karhunen-Loeve Transform [J].
Bavirisetti, Durga Prasad ;
Dhuli, Ravindra .
IEEE SENSORS JOURNAL, 2016, 16 (01) :203-209
[4]   Detail preserved fusion of visible and infrared images using regional saliency extraction and multi-scale image decomposition [J].
Cui, Guangmang ;
Feng, Huajun ;
Xu, Zhihai ;
Li, Qi ;
Chen, Yueting .
OPTICS COMMUNICATIONS, 2015, 341 :199-209
[5]   Generative Adversarial Networks [J].
Goodfellow, Ian ;
Pouget-Abadie, Jean ;
Mirza, Mehdi ;
Xu, Bing ;
Warde-Farley, David ;
Ozair, Sherjil ;
Courville, Aaron ;
Bengio, Yoshua .
COMMUNICATIONS OF THE ACM, 2020, 63 (11) :139-144
[6]   Mutually Guided Image Filtering [J].
Guo, Xiaojie ;
Li, Yu ;
Ma, Jiayi .
PROCEEDINGS OF THE 2017 ACM MULTIMEDIA CONFERENCE (MM'17), 2017, :1283-1290
[7]   A new image fusion performance metric based on visual information fidelity [J].
Han, Yu ;
Cai, Yunze ;
Cao, Yin ;
Xu, Xiaoming .
INFORMATION FUSION, 2013, 14 (02) :127-135
[8]   NOSMFuse: An infrared and visible image fusion approach based on norm optimization and slime mold architecture [J].
Hao, Shuai ;
He, Tian ;
Ma, Xu ;
An, Beiyi ;
Wen, Hu ;
Wang, Feng .
APPLIED INTELLIGENCE, 2023, 53 (05) :5388-5401
[9]   VIF-Net: An Unsupervised Framework for Infrared and Visible Image Fusion [J].
Hou, Ruichao ;
Zhou, Dongming ;
Nie, Rencan ;
Liu, Dong ;
Xiong, Lei ;
Guo, Yanbu ;
Yu, Chuanbo .
IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING, 2020, 6 :640-651
[10]  
Hu J, 2018, PROC CVPR IEEE, P7132, DOI [10.1109/TPAMI.2019.2913372, 10.1109/CVPR.2018.00745]