Infrared and Visible Image Fusion Based on Image Enhancement and Rolling Guidance Filtering

被引:3
|
作者
Liang Jiaming [1 ]
Yang Shen [1 ]
Tian Lifan [1 ]
机构
[1] Wuhan Univ Sci & Technol, Sch Informat Sci & Engn, Wuhan 430081, Hubei, Peoples R China
关键词
image processing; image fusion; rolling guidance filtering; adaptive image enhancement; FRAMEWORK; NETWORK;
D O I
10.3788/LOP212636
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
A multiscale fusion algorithm based on image enhancement and rolling guidance filtering is proposed to solve the problems of thermal target brightness loss and visible image detail information loss caused by infrared and visible image fusion. First, an adaptive image enhancement method is proposed to improve the overall brightness of the visible image and maintain the contrast of the details. Second, according to the different features, the source image is divided into three layers, and the luminance layer is obtained by using the significant extraction method based on guidance filtering. The favorable scale perception and edge preservation characteristics of rolling guidance filtering are used, and the basic layer and detail layer are obtained by combining Gaussian filtering. Finally, the fusion rule of large pixel value is used for the luminance layer, a least-squares optimization scheme is proposed for the basic layer, and the sum of the modified Laplace energy is used as a measure of sharpness for the detail layer. The experimental results show that, compared with other fusion methods, the proposed method has better performances in both subjective and objective evaluations.
引用
收藏
页数:13
相关论文
共 28 条
  • [11] Image Fusion with Guided Filtering
    Li, Shutao
    Kang, Xudong
    Hu, Jianwen
    [J]. IEEE TRANSACTIONS ON IMAGE PROCESSING, 2013, 22 (07) : 2864 - 2875
  • [12] Infrared and visible image fusion with convolutional neural networks
    Liu, Yu
    Chen, Xun
    Cheng, Juan
    Peng, Hu
    Wang, Zengfu
    [J]. INTERNATIONAL JOURNAL OF WAVELETS MULTIRESOLUTION AND INFORMATION PROCESSING, 2018, 16 (03)
  • [13] A general framework for image fusion based on multi-scale transform and sparse representation
    Liu, Yu
    Liu, Shuping
    Wang, Zengfu
    [J]. INFORMATION FUSION, 2015, 24 : 147 - 164
  • [14] FusionGAN: A generative adversarial network for infrared and visible image fusion
    Ma, Jiayi
    Yu, Wei
    Liang, Pengwei
    Li, Chang
    Jiang, Junjun
    [J]. INFORMATION FUSION, 2019, 48 : 11 - 26
  • [15] Infrared and visible image fusion methods and applications: A survey
    Ma, Jiayi
    Ma, Yong
    Li, Chang
    [J]. INFORMATION FUSION, 2019, 45 : 153 - 178
  • [16] Infrared and visible image fusion based on visual saliency map and weighted least square optimization
    Ma, Jinlei
    Zhou, Zhiqiang
    Wang, Bo
    Zong, Hua
    [J]. INFRARED PHYSICS & TECHNOLOGY, 2017, 82 : 8 - 17
  • [17] An Efficient Image Fusion of Visible and Infrared Band Images using Integration of Anisotropic Diffusion and Discrete Wavelet Transform
    Panchotiya, Binal
    Israni, Dippal
    Patel, Ritesh
    [J]. JOURNAL OF COMMUNICATIONS SOFTWARE AND SYSTEMS, 2020, 16 (01) : 30 - 36
  • [18] Infrared and Visible Image Fusion Method Based on Rolling Guidance Filter and Convolution Sparse Representation
    Pei Peipei
    Yang Yanchun
    Dang Jianwu
    Wang Yangping
    [J]. LASER & OPTOELECTRONICS PROGRESS, 2022, 59 (12)
  • [19] Gradient-based multiresolution image fusion
    Petrovic, VS
    Xydeas, CS
    [J]. IEEE TRANSACTIONS ON IMAGE PROCESSING, 2004, 13 (02) : 228 - 237
  • [20] Compressed Fusion of Infrared and Visible Images Combining Robust Principal Component Analysis and Non-Subsampled Contour Transform
    Su Jinfeng
    Zhang Guicang
    Wang Kai
    [J]. LASER & OPTOELECTRONICS PROGRESS, 2020, 57 (04)