期刊
PROCEEDINGS OF THE 10TH INTERNATIONAL CONFERENCE OF INFORMATION AND COMMUNICATION TECHNOLOGY
卷 183, 期 -, 页码 609-615出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.procs.2021.02.104
关键词
Visible and infrared image fusion; convolution sparse representation; convolution dictionary learning; local-global
资金
- Sichuan Science and Technology Program [2020YFS0351]
- Luzhou Science and Technology Program
- Scientific Research Project of Sichuan Public Security Department [201917]
The proposed local processing convolution dictionary-learning method improves image fusion quality by addressing the limitations of global convolutional sparse coding and overcoming the blurry fusion image defect. Experimental results show superior performance compared to other methods, with an average improvement of 5.18%, 5.02%, and 4.77% in objective evaluation metrics.
Although convolutional sparse coding overcomes the limitations of block-based sparse representation in the process of convolutional dictionary learning, it relies too much on the Alternating Direction Method of Multipliers(ADMM) formula in the Fourier domain. This leads to a loss of locality, impacting the quality of the fused image which tends to have a fuzzy texture. To solve this problem, we use a local processing convolution dictionary-learning method to obtain a dictionary and apply the obtained dictionary to the fusion of visible-infrared images. The proposed method not only solves the problem of global convolutional sparse coding, but also overcomes the blurry fusion image defect. Experimental results show that the proposed method is superior to the comparison methods in subjective and objective evaluation. Compared with deep learning fusion methods, our fusion framework achieves an average improvement of 5.18%, 5.02%, and 4.77% in the objective evaluation QAB/F, Qe and Qp, respectively. (C) 2021 The Authors. Published by Elsevier B.V.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据