• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

计算机工程与科学

• 图形与图像 • 上一篇    下一篇

基于引导滤波和shearlet稀疏的遥感图像融合算法

王威1,2,张佳娥1,2   

  1. (1.长沙理工大学计算机与通信工程学院,湖南 长沙 410114;
    2.综合交通运输大数据智能处理湖南省重点实验室,湖南 长沙 410114)
  • 收稿日期:2016-08-16 修回日期:2016-12-07 出版日期:2018-07-25 发布日期:2018-07-25
  • 基金资助:

    国防973计划(613XXX0301)

A remote sensing image fusion algorithm based on
 guided filtering and shearlet sparse base

WANG Wei1,2,ZHANG Jiae1,2   

  1. (1.School of Computer and Communication Engineering,Changsha University of Science & Technology,Changsha 410114;
    2.Hunan Province Key Laboratory of Comprehensive Transportation Big Data Intelligent Processing,Changsha 410114,China)
  • Received:2016-08-16 Revised:2016-12-07 Online:2018-07-25 Published:2018-07-25

摘要:

针对遥感图像空间分辨率和光谱分辨率不可兼得的情况,
结合多尺度变换与稀疏表示,提出一种shearlet稀疏基与引导滤波共同作用的遥感图像融合算法。以IHS融合模型为基础,利用引导滤波作拟合处理,再用shearlet变换分解亮度图像和全色图像,得到图像的高低频子带系数。
对低频子图进行稀疏化处理并获取最优稀疏系数,稀疏系数以图像块活跃度取大的标准进行替换融合。
基于区域能量和区域方差融合处理对应的高频子图,再利用shearlet反变换获取融合结果。
实验结果表明,本文算法能提高图像清晰度以及光谱保留度,在图像完整度和细节考量上远好于其他算法。

关键词: 图像融合, shearlet变换, 低频部分, 稀疏表示

Abstract:

For the situation that the spatial resolution and spectral resolution of remote sensing images cannot be combined, we propose a remote sensing image fusion algorithm based on shearlet sparse base and guided filtering by combing multiscale transform with sparse representation. Based on the IHS fusion model, we adopt the guided filtering for the fitting process. Then the brightness image and the panchromatic image are decomposed by the shearlet transform to obtain the high and low frequency subband coefficients of the image. The lowfrequency subimages are sparsely processed and the optimal sparse coefficients are obtained, and fusion is performed based on the criterion that the activity degree of image blocks is large. The corresponding highfrequency subimages are fused based on regional energy and regional variance and obtain the fusion results via the shearlet inverse transformation. Experimental results show that the proposed algorithm can improve image sharpness and spectral retention, and it outperforms other algorithms in image integrity and detail.


 

Key words: image fusion, shearlet transform, low frequency part, sparse representation