基于压缩感知的红外与可见光图像融合

周渝人; 耿爱辉; 张强; 陈娟; 董宇星

doi:10.3788/OPE.20152303.0855

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基于压缩感知的红外与可见光图像融合

信息科学 | 更新时间：2020-08-13

- 基于压缩感知的红外与可见光图像融合
- Fusion of infrared and visible images based on compressive sensing
- 光学精密工程 2015年23卷第3期页码：855-863
- 作者机构：
  
  1. 中国科学院长春光学精密机械与物理研究所,吉林长春,中国,130033
  2. 中国科学院大学北京,中国,100049
  3. 长春工业大学电气与电子工程学院,吉林长春,130012
  4. 长春理工大学理学院,吉林长春,130022
- 作者简介：
- 基金信息：
  
  吉林省科技厅科技发展计划资助项目(No.50105)()
- DOI：10.3788/OPE.20152303.0855
  中图分类号： TP391.4
- 收稿日期：2014-01-29，
  
  修回日期：2014-03-05，
  
  纸质出版日期：2015-03-25
- 稿件说明：
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周渝人, 耿爱辉, 张强等. 基于压缩感知的红外与可见光图像融合[J]. 光学精密工程, 2015,23(3): 855-863

ZHOU Yu-ren, GENG Ai-hui, ZHANG Qiang etc. Fusion of infrared and visible images based on compressive sensing[J]. Editorial Office of Optics and Precision Engineering, 2015,23(3): 855-863
周渝人, 耿爱辉, 张强等. 基于压缩感知的红外与可见光图像融合[J]. 光学精密工程, 2015,23(3): 855-863 DOI： 10.3788/OPE.20152303.0855.

ZHOU Yu-ren, GENG Ai-hui, ZHANG Qiang etc. Fusion of infrared and visible images based on compressive sensing[J]. Editorial Office of Optics and Precision Engineering, 2015,23(3): 855-863 DOI： 10.3788/OPE.20152303.0855.

摘要

基于压缩感知理论提出了一种红外与可见光图像的融合新方法。该方法将Contourlet变换(CT)和小波变换(WT)相结合

以进一步增加变换后系数的稀疏性

同时对采样模式和融合规则进行改进。首先对图像进行Contourlet变换

再对各高层分解系数进行正交小波变换;然后使用各层采样率不同的分立双放射形采样矩阵对系数采样

并用不同的规则对各层采样值进行融合;最后使用非线性共轭梯度法重构融合图像。实验结果表明

在采样率为0.5时

本文方法融合图像的细节信息比小波方法和小波变换压缩感知(WTCS)方法更加丰富;在所有采样率上

本文方法的融合效果比WTCS法在互信息、空间频率和融合信息逼真度等客观融合质量评价指标上均提高约10%。

Abstract

A novel method to fuse infrared and visible images was proposed based on compressive sensing theory. The method combined Contourlet Transform (CT) with Wavelet Transform (WT) to increase the sparsity of transformed coefficients and also to improve sample patterns and fusion rules. Firstly

the original images were decomposed in a Contourlet domain

and orthogonal wavelet transform was applied to the high level decomposed coefficients. Then

the composite double radially sampling mode with different sampling rates in each decomposition level was used to perform the linear measurements of coefficients and to fuse the measurement values using different rules in each level. Finally

the fused image was reconstructed by using nonlinear conjugate-gradient solution. The experimental results demonstrate that the detail information of fusion image by proposed method is more salient than that of discrete wavelet transform fusion image when sampling rate is 0.5. As compared with WTCS method

the mutual information

spatial frequency and the visual information fidelity of fused image from proposed method are increased by 10%.

关键词

Keywords

references

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