Image fusion based on CS measurement matrix optimization

SUN Yong-ming; WU Jin; LIU Jing

doi:10.3788/YJYXS20142903.0461

您当前的位置：

首页 >

文章列表页 >

Image fusion based on CS measurement matrix optimization

更新时间：2020-08-12

- Image fusion based on CS measurement matrix optimization
- Chinese Journal of Liquid Crystals and Displays Vol. 29, Issue 3, Pages: 461-465(2014)
- 作者机构：
  
  武汉科技大学信息科学与工程学院, 湖北武汉 430081
- 作者简介：
- 基金信息：
- DOI：10.3788/YJYXS20142903.0461
  CLC：
- Received：16 September 2013，
  
  Revised：14 October 2013，
  
  Published：25 April 2014
- 稿件说明：
移动端阅览
孙永明, 吴谨, 刘劲. 基于CS测量矩阵优化的图像融合[J]. 液晶与显示, 2014,29(3): 461-465

SUN Yong-ming, WU Jin, LIU Jing. Image fusion based on CS measurement matrix optimization[J]. , 2014,29(3): 461-465
孙永明, 吴谨, 刘劲. 基于CS测量矩阵优化的图像融合[J]. 液晶与显示, 2014,29(3): 461-465 DOI： 10.3788/YJYXS20142903.0461.

SUN Yong-ming, WU Jin, LIU Jing. Image fusion based on CS measurement matrix optimization[J]. , 2014,29(3): 461-465 DOI： 10.3788/YJYXS20142903.0461.

摘要

测量矩阵是压缩感知理论的三大核心部分之一，它直接影响着压缩感知理论在图像融合领域的应用。针对随机测量矩阵不易硬件实现的问题，本文设计了一种仅由-1、0和1三个值组成的测量矩阵，并利用基于Gram矩阵的优化方法使其尽可能地与稀疏变换矩阵不相关。实验结果表明，该测量矩阵不仅能提高重构图像的PSNR（Peak Signal to Noise Ratio），而且将其应用于基于压缩感知的图像融合中，在采样率仅为非压缩域50%的情况下仍能取得较好的融合效果。

Abstract

The measurement matrix is one of the three core part of the compressed sensing theory

it influences the application of CS (Compressed Sensing) theory in the field of image fusion. In order to solve the problem that random measurement matrix is hard to realize for hardware

this paper designs a measurement matrix which is consisted only of minus one

zero and one

and then makes it no-related to sparse matrix as much as possible according to Gram matrix-based optimization method. The experimental results show that the proposed method can obtain higher PSNR gain of reconstructed image

and in the application of CS-based image fusion

our method can get a favorable fuse effect in the case of only 50% sampled.

关键词

Keywords

references

Wan T,Canagarajah N,Achim A.Compressive image fusion[C]//Processing of the International Conference on Image Processing,California,USA:IEEE Press,2008:1308-1311.[2] 黄晓生,戴秋芳,曹义亲一种基于小波稀疏基的压缩感知图像融合算法[J]. 计算机应用研究, 2012,29(9):2581-2583. Huang X S,Dai Q F,Cao Y Q.Compressive sensing image fusion algorithm based on wavelet sparse basis [J].Application Research of Computers,2012,29(9):2581-2583.(in Chinese)[3] 赵瑞珍,秦周,胡绍海. 一种基于特征值分解的测量矩阵优化方法[J]. 信号处理, 2012,28(5):653-657. Zhao R Z,Qin Z,Hu S H.An optimization method for measurement matrix based on eigenvalue decomposition [J].Signal Processing,2012,28(5):653-657.(in Chinese)[4] 殷亚男, 王晓东, 李丙玉. 基于预测和JPEG2000的MODIS红外辐射多光谱图像无损压缩算法[J]. 液晶与显示, 2013,28(6): 922-926. Yin Y N,Wang X D,Li B Y.Lossless compression method based on prediction and JPEG2000 for MODIS emissive IR bands multispectral image [J].Chinese Journal of Liquid Crystals and Displays,2013,28(6): 922-926.(in Chinese)[5] Donoho D.Compressed sensing [J].IEEE Transactions on Information Theory,2006,52(4):1289-1306.[6] Candes E,Tao T.Robust uncertainty principles:exact signal reconstruction from highly incomplete information [J].IEEE Transactions on Information Theory,2006,52(2):489-509.[7] 郑万泽,何劲,魏星,等. 基于Contourlet变换的图像压缩感知重构[J]. 计算机工程, 2012,38(12):194-196. Zheng W Z,He J,Wei X,et al.Image compressive sensing reconstruction based on contourlet transform [J].Computer Engineering,2012,38(12):194-196.(in Chinese)[8] Abolghasemi V,Ferdowsi S,Sanei S.A gradient-based alternating minimization approach for optimization of the measurement matrix in compressive sensing [J].Signal Processing,2012(92):999-1009.[9] Needell D,Vershynin R.Uniform uncertainty principle and signal recovery via regularized orthogonal matching pursuit [J].Foundations of Computational Mathematics,2007,9(3):317-334.[10] 傅瑶,孙雪晨,薛旭成,等. 基于非下采样轮廓波变换的全彩色图像与多光谱图像融合方法研究[J]. 液晶与显示, 2013,28(3): 429-433. Fu Y,Sun X C,Xue X C,et al.Panchromatic and multispectral image fusion method based on nonsubsampled contourlet transform [J].Chinese Journal of Liquid Crystals and Displays,2013,28(3): 429-433.(in Chinese)

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Image fusion algorithm based on non-subsampled Shearlet transform and compressed sensing

Robust stitching method for borehole wall images under multi-interference imaging conditions

Image defogging algorithm based on multi-scale residual feature fusion

Infrared and visible image fusion algorithm based on structure-texture decomposition

Laboratory flame image segmentation and recognition by fusing infrared and visible light

Related Author

CHEN Zhen

XING Xiao-xue

LUO Qi

XU Wangming

LI Yaoxiang

ZHAO Yuhang

XIE Xindan

LI Xiaoyan

Related Institution

Information Engineering Department, Putian University

College of Information Engineering, Changchun University

School of Information Science and Engineering， Wuhan University of Science and Technology

School of Electronics and Information Engineering， Xi'an Technological University

School of Optoelectronic Engineering， Xi'an Technological University

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China 130033 Postal code：130033
Tel：0431-86176059 Email：yjxs@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰