AFusionAlgorithmforVisibleImageandInfraredImageBasedon CompressiveSensingandNonsubsampledContourletTransform

doi:10.15961/j.jsuese.2014.05.043

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AFusionAlgorithmforVisibleImageandInfraredImageBasedon CompressiveSensingandNonsubsampledContourletTransform

更新时间：2023-11-14

- AFusionAlgorithmforVisibleImageandInfraredImageBasedon CompressiveSensingandNonsubsampledContourletTransform
- Advanced Engineering Sciences Vol. 46, Issue 5, Pages: 88-95(2014)
- 作者机构：
  
  1. 昆明理工大学计算中心
  2. 昆明理工大学云南省计算机技术应用重点实验室
  3. 昆明理工大学信息与自动化学院
  4. 四川大学计算机学院
  5. 四川大学视觉合成图形图像技术重点学科实验室
- 作者简介：
- 基金信息：
- DOI：10.15961/j.jsuese.2014.05.043
  CLC：
- Published：2014，
  
  Published Online：6 June 2014，
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柳翠寅,罗洪礼,李晓峰.基于压缩感知的红外与可见光图像融合[J].工程科学与技术,2014,46(5):88-95.

AFusionAlgorithmforVisibleImageandInfraredImageBasedon CompressiveSensingandNonsubsampledContourletTransform. [J]. Advanced Engineering Sciences 46(5):88-95(2014)
柳翠寅,罗洪礼,李晓峰.基于压缩感知的红外与可见光图像融合[J].工程科学与技术,2014,46(5):88-95. DOI： 10.15961/j.jsuese.2014.05.043.

AFusionAlgorithmforVisibleImageandInfraredImageBasedon CompressiveSensingandNonsubsampledContourletTransform. [J]. Advanced Engineering Sciences 46(5):88-95(2014) DOI： 10.15961/j.jsuese.2014.05.043.

摘要

中文摘要: 针对机载实时融合需求，提出了基于CS域的图像融合框架，并提出基于NSCT分解的压缩感知图像融合算法。算法采用服从高斯分布的测量矩阵，对经NSCT分解的图像的高频子带系数进行量测得到比高频子带系数更稀疏的测量值，对测量值采用最大值融合规则得到融合测量值，采用子空间追踪SP算法对测量值进行重构得到近似精确的方向子带融合系数，逆变换融合的高频与低频子带系数得到融合图像。通过多组图像融合实验，比较融合评价指标和算法消耗时间，证实本文算法在保证融合质量的同时有效地提高了运算效率，有利于满足机载实时性应用需求。

Abstract

Abstract:A fusion framework based on compressive sensing theory was proposed under the premise of guaranteeing fusion quality and reducing time-consuming.A fusion algorithm for visible image and infrared image was proposed based on compressive sensing and nonsubsampled contourlet transform.The highpass subbands attained by NSCT are the sparse representation of the source image.The sparse data was transferred to compressed domain by nonlinear mapping

and then the fusion operator was implemented in this domain.Finally

the fused image was attained by the optimization of the problem for exact reconstructing image.The calculation efficiency of algorithm was improved due to the fusion in compressed domain and the reduced the amount of data. Therefore

the algorithm is effective for improving the efficiency of fusion algorithm and the application of airborne real-time.

关键词

图像融合压缩感知稀疏表示

Keywords

imagefusioncompressivesensingsparserepresentation

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