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论文:2013,Vol:31,Issue(5):793-797 |
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引用本文： |
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王珺, 彭进业, 何贵青, 冯晓毅. 基于多尺度字典学习的图像融合方法[J]. 西北工业大学 |
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Wang Jun, Peng Jinye, He Guiqing, Feng Xiaoyi. An Image Fusion Algorithm Based on Multi-Scale Dictionary Learning[J]. Northwestern polytechnical university |
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| 基于多尺度字典学习的图像融合方法 |
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| 王珺, 彭进业, 何贵青, 冯晓毅 |
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| 西北工业大学 电子信息学院, 陕西 西安 710072 |
| 摘要: |
| 将小波域多尺度分析的思想和图像域单尺度稀疏表示的思想有效结合,提出基于多尺度字典学习的图像融合方法。首先将训练图像变换到小波域,分别对各个子带系数训练字典;根据训练的字典求解并融合源图像各个子带的稀疏表示系数;经过逆小波变换重构融合图像。提出的方法综合了学习字典的稀疏特性和小波分析的多分辨率特性。实验结果表明较现有基于图像域字典学习的融合方法和基于小波域多尺度分析的融合方法均具有更优的融合效果。 |
| 关键词:
图像融合
多尺度字典学习
稀疏表示
K-SVD
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| An Image Fusion Algorithm Based on Multi-Scale Dictionary Learning |
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| Wang Jun, Peng Jinye, He Guiqing, Feng Xiaoyi |
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| Department of Electronics Engineering, Northwestern Polytechnical University, Xi'an 710072, China |
| Abstract: |
| We combine the multi-scale analysis in wavelet domain with the single-scale sparse representation in image domain and propose an image fusion algorithm based on multi-scale dictionary learning. We transform the trained images into wavelet domain and train the dictionary for each sub-band dictionary. We use the trained dictionary to solve and fuse the sparse representation coefficient of each sub-band of a source image. The fused image is reconstructed through the inverse wavelet domain. Our algorithm combines the sparse character of a learned dictionary with the multi-resolution character of wavelet analysis. The experimental results,given in Fig. 2 and Table1,and their analysis show that our image fusion algorithm outperforms those based on the learned dictionary in image domain and multi-scale analysis in wavelet domain respectively. |
| Key words:
algorithms
image fusion
image processing
wavelet transforms
image domain
muli-scale analysis
muli-scale dictionary learning
sparse representation
wavelet domain
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| 收稿日期: 2013-02-28
修回日期:
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| DOI: |
| 基金项目: 国家自然科学基金(61075014);航天支撑基金(2011XW080001C080001);西北工业大学博士论文创新基金(CX201318)资助 |
| 通讯作者:
Email： |
| 作者简介: 王珺(1987-),女,西北工业大学博士研究生,主要从事图像融合及稀疏表示的研究。
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参考文献: |
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