基于梯度的压缩感知图像融合

Chen Yang; Qin Zheng

doi:10.1631/FITEE.1400217

Your Location：

Home >

Browse articles >

基于梯度的压缩感知图像融合

计算机与自动化 | Updated：2022-06-06

- 基于梯度的压缩感知图像融合
- Gradient-based compressive image fusion*
- 信息与电子工程前沿（英文） 2015年16卷第3期页码：227-237
- Affiliations：
  
  Department of Computer Science & Technology, Tsinghua University, Beijing 100084, China
- Author bio：
  
  ‡Corresponding Author
- Funds：
  
  *Project supported by the National S&T Major Program (No. 9140A1550212 JW01047) and the ‘Twelfth Five’ Preliminary Research Project of PLA (No. 402040202)
- DOI：10.1631/FITEE.1400217
  中图分类号：
- 收稿：2014-06-18，
  
  修回：2015-01-28，
  
  录用：2014-10-30，
  
  纸质出版：2015-03
- Accepted：
Scan QR Code
基于梯度的压缩感知图像融合[J]. 信息与电子工程前沿（英文）, 2015,16(3):227-237.

Chen Yang, Qin Zheng. Gradient-based compressive image fusion*[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(3): 227-237.
基于梯度的压缩感知图像融合[J]. 信息与电子工程前沿（英文）, 2015,16(3):227-237. DOI： 10.1631/FITEE.1400217.

Chen Yang, Qin Zheng. Gradient-based compressive image fusion*[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(3): 227-237. DOI： 10.1631/FITEE.1400217.

摘要

目的

面向多传感器图像融合，实现基于梯度的压缩感知图像融合，使其具有传输量小，计算复杂度低的特点。

创新点

提出一种基于梯度的融合规则（图1），对压缩感知系数进行融合，并对融合后的压缩感知系数进行反变换得到原图像，提高压缩感知融合质量。

方法

首先，对多传感器捕获的图像进行压缩感知分解以提高传感器传输速率。然后在融合阶段，基于压缩感知系数梯度进行融合得到融合后的压缩感知系数，并对融合后的系数进行压缩感知反变换得到融合后图像。通过两种融合场景的应用实验（图2-7，表1-6），证明所提算法相比于其他传统压缩感知图像融合方法，在人眼视觉及客观融合标准中均更优。

结论

针对多种融合场景，提出一种高效的基于梯度的压缩感知的图像融合方法，提高图像融合精度。

Abstract

We present a novel image fusion scheme based on gradient and scrambled block Hadamard ensemble (SBHE) sampling for compressive sensing imaging. First

source images are compressed by compressive sensing

to facilitate the transmission of the sensor. In the fusion phase

the image gradient is calculated to reflect the abundance of its contour information. By compositing the gradient of each image

gradient-based weights are obtained

with which compressive sensing coefficients are achieved. Finally

inverse transformation is applied to the coefficients derived from fusion

and the fused image is obtained. Information entropy (IE)

Xydeas’s and Piella’s metrics are applied as non-reference objective metrics to evaluate the fusion quality in line with different fusion schemes. In addition

different image fusion application scenarios are applied to explore the scenario adaptability of the proposed scheme. Simulation results demonstrate that the gradient-based scheme has the best performance

in terms of both subjective judgment and objective metrics. Furthermore

the gradient-based fusion scheme proposed in this paper can be applied in different fusion scenarios.

关键词

Keywords

references

K Amolins , Y Zhang , P Dare . Wavelet based image fusion techniques—an introduction, review and comparison . ISPRS J Photogram Remote Sens , 2007 , 62 ( 4 ): 249 - 263 . DOI: 10.1016/j.isprsjprs.2007.05.009 http://doi.org/10.1016/j.isprsjprs.2007.05.009 .

J Byeungwoo , DA Landgrebe . Decision fusion approach for multitemporal classification . IEEE Trans Geosci Remote Sens , 1999 , 37 ( 3 ): 1227 - 1233 . DOI: 10.1109/36.763278 http://doi.org/10.1109/36.763278 .

EJ Candès , J Romberg . l 1 -Magic: Recovery of Sparse Signals via Convex Programming . 2005 . Available from http://www.acm.caltech.edu/l1magic/ .

EJ Candès , T Tao . Near-optimal signal recovery from random projections: universal encoding strategies . IEEE Trans Inform Theory , 2006 , 52 ( 12 ): 5406 - 5425 . DOI: 10.1109/TIT.2006.885507 http://doi.org/10.1109/TIT.2006.885507 .

EJ Candès , MB Wakin . An introduction to compressive sampling . IEEE Signal Process Mag , 2008 , 25 ( 2 ): 21 - 30 . DOI: 10.1109/MSP.2007.914731 http://doi.org/10.1109/MSP.2007.914731 .

RY Chen , S Li , R Yang , 等 . Multi-focus images fusion based on data assimilation and genetic algorithm . 2008 . Proc. Int. Conf. on Computer Science and Software Engineering . 249 - 252 . DOI: 10.1109/CSSE.2008.525 http://doi.org/10.1109/CSSE.2008.525 .

SS Chen , DL Donoho , MA Saunders . Atomic decomposition by basis pursuit . SIAM J Sci Comput , 1998 , 20 ( 1 ): 33 - 61 . DOI: 10.1137/S1064827596304010 http://doi.org/10.1137/S1064827596304010 .

M Ding , L Wei , BF Wang . Research on fusion method for infrared and visible images via compressive sensing . Infrared Phys Technol , 2013 , 57 56 - 67 . DOI: 10.1016/j.infrared.2012.12.014 http://doi.org/10.1016/j.infrared.2012.12.014 .

TT Do , G Lu , NH Nguyen , 等 . Fast and efficient compressive sensing using structurally random matrices . IEEE Trans Signal Process , 2012 , 60 ( 1 ): 139 - 154 . DOI: 10.1109/TSP.2011.2170977 http://doi.org/10.1109/TSP.2011.2170977 .

DL Donoho . Compressed sensing . IEEE Trans Inform Theory , 2006 , 52 ( 4 ): 1289 - 1306 . DOI: 10.1109/TIT.2006.871582 http://doi.org/10.1109/TIT.2006.871582 .

MF Duarte , MA Davenpot , D Takhar , 等 . Single-pixel imaging via compressive sampling . IEEE Signal Process Mag , 2008 , 25 ( 2 ): 83 - 91 . DOI: 10.1109/MSP.2007.914730 http://doi.org/10.1109/MSP.2007.914730 .

MAT Figueiredo , RD Nowak , SJ Wright . Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems . IEEE J Sel Topics Signal Process , 2007 , 1 ( 4 ): 586 - 597 . DOI: 10.1109/JSTSP.2007.910281 http://doi.org/10.1109/JSTSP.2007.910281 .

JJ Han , O Loffeld , K Hartmann , 等 . Multi image fusion based on compressive sensing . 2010 . Proc. Int. Conf. on Audio Language and Image Processing . 1463 - 1469 . DOI: 10.1109/ICALIP.2010.5684502 http://doi.org/10.1109/ICALIP.2010.5684502 .

IT Jolliffe . Principal Component Analysis , 1986 . Springer .

B Kang , WP Zhu , J Yan . Fusion framework for multi- focus images based on compressed sensing . IET Image Process , 2013 , 7 ( 4 ): 290 - 299 . DOI: 10.1049/iet-ipr.2012.0543 http://doi.org/10.1049/iet-ipr.2012.0543 .

ST Li , B Yang . Multifocus image fusion by combining curvelet and wavelet transform . Patt Recog Lett , 2008 , 29 ( 9 ): 1295 - 1301 . DOI: 10.1016/j.patrec.2008.02.002 http://doi.org/10.1016/j.patrec.2008.02.002 .

ST Li , JTY Kwok , IW Tsang , 等 . Fusing images with different focuses using support vector machines . IEEE Trans Neur Netw , 2004 , 15 ( 6 ): 1555 - 1561 . DOI: 10.1109/TNN.2004.837780 http://doi.org/10.1109/TNN.2004.837780 .

X Li , SY Qin . Efficient fusion for infrared and visible images based on compressive sensing principle . IET Image Process , 2011 , 5 ( 2 ): 141 - 147 . DOI: 10.1049/iet-ipr.2010.0084 http://doi.org/10.1049/iet-ipr.2010.0084 .

Z Liu , K Tsukada , K Hanasaki , 等 . Image fusion by using steerable pyramid . Patt Recog Lett , 2001 , 22 ( 9 ): 929 - 939 . DOI: 10.1016/S0167-8655(01)00047-2 http://doi.org/10.1016/S0167-8655(01)00047-2 .

XY Luo , J Zhang , JY Yang , 等 . Image fusion in compressed sensing . 2009 . Proc. 16th IEEE Int. Conf. on Image Processing . 2205 - 2208 . DOI: 10.1109/ICIP.2009.5413866 http://doi.org/10.1109/ICIP.2009.5413866 .

G Pajares , JM de la Cruz . A wavelet-based image fusion tutorial . Patt Recog , 2004 , 37 ( 9 ): 1855 - 1872 . DOI: 10.1016/j.patcog.2004.03.010 http://doi.org/10.1016/j.patcog.2004.03.010 .

VS Petrović , CS Xydeas . Gradient-based multiresolution image fusion . IEEE Trans Image Process , 2004 , 13 ( 2 ): 228 - 237 . DOI: 10.1109/TIP.2004.823821 http://doi.org/10.1109/TIP.2004.823821 .

G Piella , H Heijmans . A new quality metric for image fusion . 2003 . Proc. Int. Conf. on Image Processing . 173 - 176 . DOI: 10.1109/ICIP.2003.1247209 http://doi.org/10.1109/ICIP.2003.1247209 .

GH Qu , DL Zhang , PF Yan . Information measure for performance of image fusion . Electron Lett , 2002 , 38 ( 7 ): 313 - 315 . DOI: 10.1049/el:20020212 http://doi.org/10.1049/el:20020212 .

J Romberg . Imaging via compressive sampling . IEEE Signal Process Mag , 2008 , 25 ( 2 ): 14 - 20 . DOI: 10.1109/MSP.2007.914729 http://doi.org/10.1109/MSP.2007.914729 .

AA Ross , R Govindarajan . Feature level fusion of hand and face biometrics . 2005 . Proc. SPIE . 196 - 204 . DOI: 10.1117/12.606093 http://doi.org/10.1117/12.606093 .

WZ Shi , CQ Zhu , Y Tian , 等 . Wavelet-based image fusion and quality assessment . Int J Appl Earth Observ Geoinform , 2005 , 6 ( 3-4 ): 241 - 251 . DOI: 10.1016/j.jag.2004.10.010 http://doi.org/10.1016/j.jag.2004.10.010 .

LI Smith . A Tutorial on Principal Components Analysis . 2002 . Available from www.cs.otago.ac.nz/cosc453/ student_tutorials/principal_components.pdf .

J Tropp , AC Gilbert . Signal recovery from random measurements via orthogonal matching pursuit . IEEE Trans Inform Theory , 2007 , 53 ( 12 ): 4655 - 4666 . DOI: 10.1109/TIT.2007.909108 http://doi.org/10.1109/TIT.2007.909108 .

T Wan , ZC Qin . An application of compressive sensing for image fusion . Int J Comput Math , 2011 , 88 ( 18 ): 3 - 9 . DOI: 10.1080/00207160.2011.598229 http://doi.org/10.1080/00207160.2011.598229 .

R Wang , LF Du . Infrared and visible image fusion based on random projection and sparse representation . Int J Remote Sens , 2014 , 35 ( 5 ): 1640 - 1652 . DOI: 10.1080/01431161.2014.880819 http://doi.org/10.1080/01431161.2014.880819 .

Y Wang , JF Yang , W Yin , 等 . A new alternating minimization algorithm for total variation image reconstruction . SIAM J Image Sci , 2008 , 1 ( 3 ): 248 - 272 . DOI: 10.1137/080724265 http://doi.org/10.1137/080724265 .

CS Xydeas , V Petrović . Objective image fusion performance measure . Electron Lett , 2000 , 36 ( 4 ): 308 - 309 . DOI: 10.1049/el:20000267 http://doi.org/10.1049/el:20000267 .

XH Yang , HY Jin , LC Jiao . Adaptive image fusion algorithm for infrared and visible light images based on DT-CWT . J Infrared Millim Waves , 2007 , 26 ( 6 ): 419 - 424 . (in Chinese) .

Y Yang , CZ Han , X Kang , 等 . An overview on pixel-level image fusion in remote sensing . 2007 . Proc. IEEE Int. Conf. on Automation and Logistics . 2339 - 2344 . DOI: 10.1109/ICAL.2007.4338968 http://doi.org/10.1109/ICAL.2007.4338968 .

YZ Zheng , Z Qin . Region-based image fusion method using bidimensional empirical mode decomposition . J Electron Imag , 2009 , 18 ( 1 ): 013008 DOI: 10.1117/1.3099703 http://doi.org/10.1117/1.3099703 .

浏览量

Downloads

CSCD

文章被引用时，请邮件提醒。

Submit

工具集

关联资源

Multi-focus image fusion based on fractional-order derivative and intuitionistic fuzzy sets