基于非下采样Shearlet变换与压缩感知的图像融合

陈贞; 邢笑雪

doi:10.3788/YJYXS20153006.1024

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基于非下采样Shearlet变换与压缩感知的图像融合

成像技术与图像处理 | 更新时间：2020-08-12

- 基于非下采样Shearlet变换与压缩感知的图像融合
- Image fusion algorithm based on non-subsampled Shearlet transform and compressed sensing
- 液晶与显示 2015年30卷第6期页码：1024-1031
- 作者机构：
  
  1. 莆田学院信息工程学院, 福建莆田 351100
  2. 长春大学电子信息工程学院,吉林长春,130022
- 作者简介：
  
  [ "陈贞(1977-),女,福建莆田人,硕士,讲师,主要从事图像处理、粗糙集理论方面的研究。E-mail:chenfjpt@yeah.net" ]
  [ "邢笑雪(1981-),女,山西霍州人,博士,讲师,研究方向为数字图像处理、模式识别等。" ]
- 基金信息：
  
  国家教育部"();春晖计划"();项目(No.Z2014138)();吉林省教育厅基金项目(No.20140529)();莆田市科技计划项目(No.20
- DOI：10.3788/YJYXS20153006.1024
  中图分类号： TP391.41
- 收稿日期：2015-01-05，
  
  修回日期：2015-02-05，
  
  纸质出版日期：2015-12-05
- 稿件说明：
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陈贞, 邢笑雪,. 基于非下采样Shearlet变换与压缩感知的图像融合[J]. 液晶与显示, 2015,30(6): 1024-1031

CHEN Zhen, XING Xiao-xue,. Image fusion algorithm based on non-subsampled Shearlet transform and compressed sensing[J]. , 2015,30(6): 1024-1031
陈贞, 邢笑雪,. 基于非下采样Shearlet变换与压缩感知的图像融合[J]. 液晶与显示, 2015,30(6): 1024-1031 DOI： 10.3788/YJYXS20153006.1024.

CHEN Zhen, XING Xiao-xue,. Image fusion algorithm based on non-subsampled Shearlet transform and compressed sensing[J]. , 2015,30(6): 1024-1031 DOI： 10.3788/YJYXS20153006.1024.

摘要

针对非下采样剪切波变换(NSST)分解后图像的高频系数数据量较大且具有较大稀疏性的问题

本文提出一种基于NSST和压缩感知(CS)的图像融合算法。算法首先采用NSST对源图像进行分解;其次利用CS算法将NSST分解后的图像的高频系数进行压缩、融合及重构;然后利用"局部区域能量和局部区域方差"联合指导待融合图像的低频系数的融合;最后利用NSST逆变换重构融合图像。由于只需要对高频系数的压缩值进行融合

因此算法可以在不影响图像融合效果的同时加快代码的运行速度。仿真实验表明

该算法不需要原图像的先验知识就可以完成图像的融合

当图像的尺寸较大时

该算法牺牲了微小的融合图像质量

但却可以显著提高算法的运行速度

减小代码的时间代价

降低对硬件系统的要求。该算法对于融合系统的实时性要求提供了一种思路

具有较大的应用价值。

Abstract

After the image decomposition with NSST

the high-frequency coefficients have a large amount of data and greater sparsity. In order to obtain fusion results rapidly

an image fusion algorithm based on Non-subsampled Shearlet Transform (NSST)combined with Compressed Sensing (CS) is presented. Firstly

the source images are decomposed with NSST; secondly

the high-frequency sub-band coefficients of the decomposed images are compressed

fused and reconstructed by CS; then

based on local area variance and local area energy

the low-frequency coefficients was fused; finally

the inverse NSST is used to get the final fused image. Because only the compressed values of the high frequency coefficients are fused

the image fusion effects can't be affected

and the running time of the algorithm can be reduced. In this paper

the multi-focus image

medical image and infrared and visible images are used to verify the effectiveness of the algorithm. The simulation results indicate that this algorithm can achieve the fusion of the image without prior knowledge of the original image. When the image size is larger

although the fusion image quality is sacrificed

it can significantly improve the speed to reduce the time cost and hardware requirements. The algorithm provides an idea on how to satisfy the real time requirements in the fusion system

which has a great practical value.

关键词

Keywords

references

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