Infrared and visible image fusion using co-occurrence filter

被引:31
|
作者
Zhang, Ping [1 ]
Yuan, Yuchen [2 ]
Fei, Chun [3 ]
Pu, Tian [2 ]
Wang, Shuhang [4 ]
机构
[1] Univ Elect Sci & Technol China, Sch Comp Optoelect Informat, Chengdu, Sichuan, Peoples R China
[2] Univ Elect Sci & Technol China, Sch Optoelect Informat, Chengdu 611731, Sichuan, Peoples R China
[3] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Sichuan, Peoples R China
[4] Harvard Med Sch, Schepens Eye Res Inst, Mass Eye & Ear, Boston, MA 02114 USA
基金
中国国家自然科学基金;
关键词
Co-occurrence filter; Image fusion; Boundary preserving; Two-scale decomposition; HEAT-SOURCE RECONSTRUCTION; OBJECT DETECTION; PERFORMANCE; TRANSFORM;
D O I
10.1016/j.infrared.2018.08.004
中图分类号
TH7 [仪器、仪表];
学科分类号
0804 ; 080401 ; 081102 ;
摘要
In this paper, an effective image fusion method for infrared image and visible image is proposed for generating a high-quality fused image to deal with the issue that existing image fusion methods suffer from loss of tiny details. The major contributions are as follows: (1) We apply the Co-occurrence filter (CoF), a recently proposed edge-preserving technique, to image fusion and propose a CoF-based image fusion framework to merge tiny details of the multiple input images. The fusion processing is respectively performed on the base layer and the detail layer, which are decomposed by the simple gaussian filter. (2) We propose a novel strategy to fuse the base layers and detail layers. The CoF is adopted directly to fuse the detail layer and an iterative CoF is used to fuse the base layer. It is demonstrated through experimental results and evaluations that the proposed method outperforms the state-of-the-art fusion methods with respect to edge preserving by both subjective evaluation and objective assessment.
引用
收藏
页码:223 / 231
页数:9
相关论文
共 50 条
  • [1] Infrared and Visible Image Fusion Based on Co-Occurrence Analysis Shearlet Transform
    Qi, Biao
    Jin, Longxu
    Li, Guoning
    Zhang, Yu
    Li, Qiang
    Bi, Guoling
    Wang, Wenhua
    [J]. REMOTE SENSING, 2022, 14 (02)
  • [2] Infrared-visible image fusion method based on multi-scale shearing Co-occurrence filter
    Zhu, Fang
    Liu, Wei
    [J]. INFRARED PHYSICS & TECHNOLOGY, 2024, 136
  • [3] Fusion of infrared and visible images through multi-level co-occurrence filtering
    Tan, Wei
    Liu, Yizhong
    [J]. SPIE FUTURE SENSING TECHNOLOGIES (2020), 2020, 11525
  • [4] Infrared and visible image fusion based on non-subsampled shearlet transform, regional energy, and co-occurrence filtering
    Zhang, Shuang
    Liu, Feng
    [J]. ELECTRONICS LETTERS, 2020, 56 (15) : 761 - +
  • [5] CLIM: Co-occurrence with Laplacian Intensity Modulation and Enhanced Color Space Transform for Infrared-Visible Image Fusion
    Misra, Indranil
    Rohil, Mukesh Kumar
    Moorthi, S. Manthira
    Dhar, Debajyoti
    [J]. INFRARED PHYSICS & TECHNOLOGY, 2023, 135
  • [6] Visible and Infrared Image Fusion Using Distributed Anisotropic Guided Filter
    Vasu, G. Tirumala
    Palanisamy, P.
    [J]. SENSING AND IMAGING, 2023, 24 (01):
  • [7] Visible and Infrared Image Fusion Using Distributed Anisotropic Guided Filter
    G. Tirumala Vasu
    P. Palanisamy
    [J]. Sensing and Imaging, 24
  • [8] Edge preserving infrared and visible image fusion with three layer decomposition based on multi-level co-occurrence filtering
    Sankar, P. Arathi
    Jayakumar, E. P.
    [J]. INFRARED PHYSICS & TECHNOLOGY, 2024, 139
  • [9] Multimodal medical image fusion using adaptive co-occurrence filter-based decomposition optimization model
    Zhu, Rui
    Li, Xiongfei
    Huang, Sa
    Zhang, Xiaoli
    [J]. BIOINFORMATICS, 2022, 38 (03) : 818 - 826
  • [10] Co-Occurrence Filter
    Jevnisek, Roy J.
    Avidan, Shai
    [J]. 30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017), 2017, : 3816 - 3824