多级方向加权最小二乘滤波器及其在多传感器图像融合中的应用

李颖奎; 陈广秋; 杨阳; 刘智; 才华

doi:10.3788/YJYXS20183308.0703

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多级方向加权最小二乘滤波器及其在多传感器图像融合中的应用

图像处理 | 更新时间：2020-07-06

- 多级方向加权最小二乘滤波器及其在多传感器图像融合中的应用
- Multistage directional weighted least squares filter and its application in multi-sensor image fusion
- 液晶与显示 2018年33卷第8期页码：703-715
- 作者机构：
  
  1.中国航空工业集团公司洛阳电光设备研究所, 河南洛阳 471023
  2.长春理工大学电子信息工程学院, 吉林长春 130022
- 作者简介：
  
  [ "李颖奎(1979-), 男, 吉林省吉林市人, 长春理工大学在读博士研究生, 高级工程师, 2017年于长春理工大学获得硕士学位, 现在中国航空工业集团公司洛阳电光设备研究所工作, 主要从事光电系统工程研究, E-mail:13838454132@163.com" ]
  [ "陈广秋(1979-), 男, 吉林长春人, 副教授, 2006年于吉林大学获得硕士学位, 2015年于吉林大学获得博士学位, 现为长春理工大学教师, 主要从事图像处理与机器视觉, E-mail:guangqiu_chen@126.com" ]
- 基金信息：
  
  吉林省教育厅"十三五"科学技术项目(JJKH20170625KJ);教育部留学基金委留学归国人员科研启动基金(教外司留1685);The Education Department of Jilin Province, "13th Five-Year" science and technology project(JJKH20170625KJ);Ministry of Education Overseas Student Fund Research and Start-up Fund for Returned Students
- DOI：10.3788/YJYXS20183308.0703
  中图分类号： TP391.41
- 收稿日期：2018-03-08，
  
  录用日期：2018-5-10，
  
  纸质出版日期：2018-08-05
- 稿件说明：
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李颖奎, 陈广秋, 杨阳, 等. 多级方向加权最小二乘滤波器及其在多传感器图像融合中的应用[J]. 液晶与显示, 2018,33(8):703-715.

Ying-kui LI, Guang-qiu CHEN, Yang YANG, et al. Multistage directional weighted least squares filter and its application in multi-sensor image fusion[J]. Chinese journal of liquid crystals and displays, 2018, 33(8): 703-715.
李颖奎, 陈广秋, 杨阳, 等. 多级方向加权最小二乘滤波器及其在多传感器图像融合中的应用[J]. 液晶与显示, 2018,33(8):703-715. DOI： 10.3788/YJYXS20183308.0703.

Ying-kui LI, Guang-qiu CHEN, Yang YANG, et al. Multistage directional weighted least squares filter and its application in multi-sensor image fusion[J]. Chinese journal of liquid crystals and displays, 2018, 33(8): 703-715. DOI： 10.3788/YJYXS20183308.0703.

摘要

针对多尺度分解在图像融合领域中的广泛应用，本文提出了一种多级方向加权最小二乘滤波器图像多尺度几何分析方法。该方法利用加权最小二乘滤波器对图像进行多级边缘保持分解，得到一个近似图像和多个不同尺度上的细节图像，然后采用小尺寸方向剪切滤波器对细节图像进行方向分析，在不同尺度上生成多个方向细节图像。根据近似图像和方向细节图像所具有的不同物理意义，分别采用不同的融合策略对分解后的图像系数进行合并处理，最后应用多级方向加权最小二乘滤波器的逆变换得到融合图像。多组图像融合实验结果表明，在图像融合领域，本文提出的基于多级方向加权最小二乘滤波器的图像分解方法优于已有文献中的一些典型多尺度分解方法。

Abstract

The multi-scale decomposition(MSD) method is extensively employed in image fusion domain and a novel image multi-scale geometrical analysis(MGA) technique based on multistage directional weighted least squares filter (MDWLSF) is proposed. In the developed method

the weighted least squares filter is utilized on image for multistage edge-preserving decomposition

and an approximate image and some detail images at the different scales are obtained. Then small size directional shear filters are applied to the detail images for the direction analysis

multiple directional detail images are generated at the different scales. On the basis of the different physical meanings embodied in the approximate image and the directional detail images

the different fusion criteria are used to merge the decomposed coefficients respectively. Finally

the fused image can be obtained by using the inverse MDWLSF transform. The fusion experimental results on multi-group different modality images demonstrate that the proposed MDWLSF method is superior to some classical multi-scale decomposition tools introduced in the existing literatures.

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references

LI S T, KANG X D, FANG L Y, et al. Pixel-level image fusion:a survey of the state of the art[J]. Information Fusion, 2017, 33:100-112.

许廷发, 苏畅, 罗璇, 等.基于梯度和小波变换的水下距离选通图像去噪[J].中国光学, 2016, 9(3):301-311.

XU T F, SU C, LUO X, et al. Underwater range-gated image denoising based on gradient and wavelet transform[J]. Chinese Optics, 2016, 9(3):301-311. (in Chinese)

TIAN Y Z, LUO J, ZHANG W J, et al. Multifocus image fusion in Q-Shift DTCWT domain using various fusion rules[J]. Mathematical Problems in Engineering, 2016, 2016:5637306.

INDIRA K P, HEMAMALINI R R, NANDHITHA N M. Performance evaluation of DWT, SWT and NSCT for fusion of PET and CT Images using different fusion rules[J].Biomedical Research, 2016, 27(1):123-131.

王珺楠, 邱欢, 张丽娟, 等.基于小波域Curvelet变换的湍流图像去噪算法[J].液晶与显示, 2017, 32(11):905-913.

WANG J N, QIU H, ZHANG L J, et al. Atmospheric turbulence image denoising algorithm based on wavelet-domain curvelet transform[J]. Chinese Journal of Liquid Crystals and Displays, 2017, 32(11):905-913. (in Chinese)

WU Y Q, WANG Z L. SAR and infrared image fusion in complex Contourlet domain based on joint sparse representation[J]. Journal of Radars, 2017, 6(4):349-358.

李杰, 郭盼, 王春哲.剪切波变换在星点提取中的应用[J].中国光学, 2015, 8(3):386-393.

LI J, GUO P, WANG C Z. Application of shearlet transform in the star extraction[J]. Chinese Optics, 2015, 8(3):386-393. (in Chinese)

ZHANG Q, MALDAGUE X. An adaptive fusion approach for infrared and visible images based on NSCT and compressed sensing[J]. Infrared Physics & Technology, 2016, 74:11-20.

CAI J J, CHENG Q M, PENG M J, et al. Fusion of infrared and visible images based on nonsubsampled contourlet transform and sparse K-SVD dictionary learning[J]. Infrared Physics & Technology, 2017, 82:85-95.

KUTYNIOK G, LIM W Q, REISENHOFER R. ShearLab 3D:faithful digital shearlet transforms based on compactly supported shearlets[J].ACM Transactions on Mathematical Software, 2016, 42(1):5.

EASLEY G, LABATE D, LIM WQ. Sparse directional image representations using the discrete shearlet transform[J]. Applied and Computational Harmonic Analysis, 2008, 25(1):25-46.

ZHAO J F, FENG H J, XU Z H, et al. Detail enhanced multi-source fusion using visual weight map extraction based on multi scale edge preserving decomposition[J]. Optics Communications, 2013, 287:45-52.

HU J W, LI S T. The multiscale directional bilateral filter and its application to multisensor image fusion[J]. Information Fusion, 2012, 13(3):196-206.

郝志成, 吴川, 杨航, 等.基于双边纹理滤波的图像细节增强方法[J].中国光学, 2016, 9(4):423-431.

HAO Z C, WU C, YANG H, et al. Image detail enhancement method based on multi-scale bilateral texture filter[J]. Chinese Optics, 2016, 9(4):423-431. (in Chinese)

TOET A, HOGERVORST M A. Multiscale image fusion through guided filtering[J]. SPIE, 2016, 9997:99970J.

FARBMAN Z, FATTAL R, LISCHINSKI D, et al. Edge-preserving decompositions for multi-scale tone and detail manipulation[J]. ACM Transactions on Graphics, 2008, 27(3):67.

陈小潘, 张凯, 臧文乾, 等.基于高提升滤波与àtrous小波分解的遥感图像融合算法[J].河南大学学报(自然科学版), 2016, 46(2):202-206.

CHEN X P, ZHANG K, ZANG W Q, et al. Remote sensing image fusion based on high-boost filtering and àtrous wavelet transform[J]. Journal of Henan University (Natural Science), 2016, 46(2):202-206. (in Chinese)

DA CUNHA A L, ZHOU J, DO M N. The nonsubsampled contourlet transform:theory, design, and applications[J]. IEEE Transactions on Image Processing, 2006, 15(10):3089-3101.

STARCK J L, CANDES E J, DONOHO D L. The curvelet transform for image denoising[J]. IEEE Transactions on Image Processing, 2002, 11(6):670-684.

YOU C Y, LIU Y, ZHAO B, et al. An objective quality metric for image fusion based on mutual information and muti-scale structural similarity[J]. Journal of Software, 2014, 9(4):1050-1054.

WANGS Q, Rehman A, Zeng K, et al. SSIM-motivated two-pass VBR coding for HEVC[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2017, 27(10):2189-2203.

张小利. 图像融合及其性能评估若干问题研究[D]. 长春: 吉林大学, 2016: 93-94. http://cdmd.cnki.com.cn/Article/CDMD-10183-1016094025.htm

ZHANG X L. Study on some issues of image fusion and performance evaluation[D]. Changchun: Jilin University, 2016: 93-94. (in Chinese)

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