香农熵加权稀疏表示图像融合方法研究

李奕; 吴小俊

doi:10.3724/SP.J.1004.2014.01819

香农熵加权稀疏表示图像融合方法研究

doi: 10.3724/SP.J.1004.2014.01819

李奕^1,2,
吴小俊^1, ,

1.
江南大学物联网工程学院无锡 214122;
2.
青岛大学国际教育合作学院青岛 266071

基金项目:

国家自然科学基金（60973094，61103128，61373055），教育部科学技术研究重大项目（311024）

详细信息

作者简介:
李奕江南大学物联网工程学院博士研究生. 主要研究方向为模式识别，智能计算及应用.E-mail：lyqgx@126.com

通讯作者:
吴小俊江南大学物联网工程学院教授．主要研究方向为人工智能，模式识别和计算机视觉.E-mail：wu xiaojun@aliyun.com

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- 被引次数: 0
出版历程
- 收稿日期: 2013-04-10
- 修回日期: 2014-02-17
- 刊出日期: 2014-08-20

Image Fusion Based on Sparse Representation Using Shannon Entropy Weighting

LI Yi^1,2,
WU Xiao-Jun^{1
, ,}

1.
School of Internet of Things Engineering, Jiangnan University, Wuxi 214122;
2.
College of International Cooperation in Education, Qingdao University, Qingdao 266071

Funds:

Supported by National Natural Science Foundation of China (60973094, 61103128, 61373055), and Key Grant Project of Chinese Ministry of Education (311024)

摘要

摘要: 针对传统稀疏表示同步超分图像融合模型中对于 LL （Low-low frequency）、LH （Low-high frequency）、H （High frequency）三部分等比例加权，不能突出重点信息之不足，本文提出一种香农熵多视角加权稀疏表示同步超分图像融合方法. 该方法引入香农熵加权技术，针对 LL、LH、H三部分根据图像特征进行自适应加权，突出重点频率段的影响，从而提高了图像融合的效果. 在多组不同类型图像上进行了实验，实验结果表明所提方法无论从融合视觉效果还是评价指标上均显示出有效性.
- 香农熵 /
- 多视角加权 /
- 稀疏表示 /
- 图像融合
Abstract: In order to overcome the shortcoming caused by the equal weighting on LL (Low-low frequency) components, LH (Low-high frequency) components of low resolution images and H (High frequency) components in the sparse representation image fusion model, a novel image fusion method of Shannon entropy multi-view weighting based on sparse representation is proposed. The proposed method can assign different weights to LL, LH and H components, and adaptively enhance the influences of the important components. Thus, the image fusion effect can be improved effectively. Experimental results obtained on different kinds of images indicate that the proposed method can achieve the promising performance in terms of both visual quality and quantitative evaluation metrics.
- Shannon entropy /
- multi-view weighting /
- sparse representation /
- image fusion

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