Image fusion algorithm based on local NSST and PCNN

CHEN Guang-qiu; GAO Yin-han; CAI Hua; LIU Guang-wen; DUAN Yun-peng

doi:10.3788/YJYXS20153004.0701

您当前的位置：

首页 >

文章列表页 >

Image fusion algorithm based on local NSST and PCNN

Imaging Technology & Image Processing | 更新时间：2020-08-12

- Image fusion algorithm based on local NSST and PCNN
- Chinese Journal of Liquid Crystals and Displays Vol. 30, Issue 4, Pages: 701-712(2015)
- 作者机构：
  
  1. 长春理工大学电子信息工程学院,吉林长春,中国,130022
  2. 吉林大学汽车仿真与控制国家重点实验室,吉林长春,130022
  3. 北京遥感设备研究所北京,100854
- 作者简介：
- 基金信息：
- DOI：10.3788/YJYXS20153004.0701
  CLC： TP391.4
- Received：22 July 2014，
  
  Revised：09 October 2014，
  
  Published：05 August 2015
- 稿件说明：
移动端阅览
陈广秋, 高印寒, 才华, 刘广文, 段云鹏. 局部化NSST与PCNN相结合的图像融合[J]. 液晶与显示, 2015,30(4): 701-712

CHEN Guang-qiu, GAO Yin-han, CAI Hua, LIU Guang-wen, DUAN Yun-peng. Image fusion algorithm based on local NSST and PCNN[J]. , 2015,30(4): 701-712
陈广秋, 高印寒, 才华, 刘广文, 段云鹏. 局部化NSST与PCNN相结合的图像融合[J]. 液晶与显示, 2015,30(4): 701-712 DOI： 10.3788/YJYXS20153004.0701.

CHEN Guang-qiu, GAO Yin-han, CAI Hua, LIU Guang-wen, DUAN Yun-peng. Image fusion algorithm based on local NSST and PCNN[J]. , 2015,30(4): 701-712 DOI： 10.3788/YJYXS20153004.0701.

摘要

为了提升多模态图像融合精度

提出了一种局部化非下抽样剪切波变换与脉冲耦合神经网络相结合的图像融合方法。首先

利用局部化非下抽样剪切波对源图像进行多尺度、多方向分解;然后

在分解后的各子带图像中

利用局部区域奇异值构造的局部结构信息因子作为PCNN神经元链接强度。经过脉冲耦合神经网络点火处理

获取子带图像的点火映射图

通过判决选择算子

选择各子带图像中的明显特征部分生成子带融合图像;最后

应用局部化非下抽样剪切波逆变换重构图像。选用多组不同模态的图像进行实验

并对实验结果进行了客观评价。实验结果表明

本文提出的融合方法在主观和客观评价上均优于一些典型融合方法

可获得更好的融合效果。

Abstract

For enhancing fusion accuracy of multi-modality images

an adaptive image fusion algorithm based on local nonsubsampled shearlet transform (LNSST) and pulse coupled neural networks (PCNN) is proposed.First

source images are decomposed to multi-scale and multi-direction subband images by LNSST.Secondly

local area singular value decomposition in each subband image is done to construct a local structure information index which is served as linking strength of each neuron in PCNN.After the fire processing of PCNN

new fire mapping images of all the subbands are obtained

the clear objects of subband images are selected by the compare-selection operator with the fire mapping images pixel by pixel and then all of them are merged into a group of new clear subband images.Finally

fused subbands are reconstructed to image by local nonsubsampled shearlet inverse transform.Some fusion experiments on several sets of different modality images are done and objective performance assessments are implemented to fusion results.The experimental results indicate that the proposed method performs better in subjective and objective assessments than a few existing typical fusion techniques in the literature and obtains better fusion performance.

关键词

Keywords

references

Yang B, Jing Z L, Zhao H T.Review of Pixel-Level Image Fusion [J].Journal of Shanghai Jiao tong University (Science), 2010, 15(1):6-12.[2] 陈小林, 王延杰.非下采样变换的红外与可见光图像融合 [J].中国光学,2011,4(5):489-495. Chen X L, Wang Y J.Infrared and visible image fusion based on nonsubsampled Contourlet transform [J].Chinese Optics, 2011,4(5):489-495.(in Chinese)[3] 傅瑶, 孙雪晨, 薛旭成,等.基于非下采样轮廓波变换的全色图像与多光谱图像融合方法研究 [J].液晶与显示,2013,28(3):429-434. 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):429-434.(in Chinese)[4] 陈广秋,高印寒.基于图像质量评价参数的FDST域图像融合 [J].光电子激光,2013,24(11):2240-2248. Chen G Q, Gao Y H.Adaptive image fusion based on image quality assessment parameter in FDST domain [J].Journal of OptoelectronicsLaser,2013,24(11): 2240-2248.(in Chinese)[5] 高印寒,陈广秋,刘妍妍.基于图像质量评价参数的非下采样剪切波域自适应图像融合 [J].吉林大学学报:工学版,2014,,44(1):225-234. Gao Y H, Chen G Q, Liu Y Y.Adaptive image fusion based on image quality assessment parameter in NSST system [J].Journal of Jilin university:Engineering and Technology Edition, 2014, 44(1):225-234.(in Chinese)[6] 李光鑫, 吴伟平, 胡君.红外和彩色可见光图像亮度-对比度传递融合算法 [J].中国光学,2011,4(2):161-168. Li G X, Wu W P, Hu J.Luminance-contrast transfer based fusion algorithm for infrared and color visible images [J].Chinese Optics, 2011,4(2):161-168.(in Chinese)[7] 唐艳秋,张星祥,李新娥,等.基于人眼视觉灰度识别特性的图像动态范围小波变换处理方法 [J].液晶与显示,2012,27(3):385-390. Tang Y Q, Zhang X X, Li X E, et al. Image processing method of dynamic range with wavelet transform based on human visual gray recognition characteristics [J].Chinese Journal of Liquid Crystals and Displays, 2012,27(3):385-390.(in Chinese)[8] Cands E J, Donoho D L, New tight frames of curvelets and optimal representations of objects with piecewise C2 singularities [J].Comm on Pure and Appl.Math., 2004,57(2): 219-266.[9] Do M N, Vetterli M.The contourlet transform:an efficient directional multiresolution image representation [J].IEEE Trans.Image Proc.,2005,14 (12):2091-2106.[10] 马苗, 万仁远, 尹义龙.基于相似性灰关联的Curvelet域多聚焦图像融合 [J].电子学报,2012,40(10):1984-1988. MA Miao, Ma M, Wan R Y, et al. Multi-focus image fusion based on grey relation of similarity in Curvelet domain [J].Acta Electronica Sinica, 2012, 40(10):1984-1988.(in Chinese)[11] 冯鹏,王静,魏彪,等.一种基于Contourlet变换的GFP图像与相衬图像融合算法研究 [J].光电子激光,2013,24(1): 176-183. Feng P, Wang J, Wei B,et al. A fusion algorithm for GFP images and phase-contrast images based on Contourlet transform [J].Journal of OptoelectronicsLaser,2013,24(1):176-183.(in Chinese)[12] Easley G,Labate D,Lim W Q.Sparse directional image representations using the discrete shearlet transform [J].Applied and Computational Harmonic Analysis,2008,25(1):25-46.[13] 杨扬.基于多尺度分析的图像融合算法研究 [D].长春:中国科学院大学中国科学院长春光学精密机械与物理研究所,2013:19-25. YangY.Research on image Fusion algorithms using multiscale analysis [D].Changchun:University of Chinese Academy of SciencesChangchun Institute of Optics, Fine Mechanics and Physics Chinese Academy of Sciences,2013:19-25.(in Chinese)[14] 王红梅, 陈励华, 李言俊, 等.一种基于显著特征的图像融合算法 [J].西北工业大学学报,2010,28(4):486-490. Wang H M, Chen L H, Li Y J, et al. A new and more effective image fusion algorithm based on salient feature [J].Journal of Northwestern Polytechnical University[J]. 2010, 28(4):486-490.(in Chinese)[15] 郭茂耘,李华锋,柴毅.提升静态小波与自适应 PCNN相结合的图像融合算法 [J].光电工程,2010,37(12):67-74. Guo M Y, Li H F, Chai Y.Image fusion using lifting stationary wavelet transform and adaptive PCNN [J].Opto-Electronic Engineering, 2010, 37(12):67-74.(in Chinese)[16] 赵景朝,曲仕茹.基于Curvelet 变换与自适应 PCNN 的红外与可见光图像融合 [J].西北工业大学学报,2011,29(6) : 849-853. Zhao J C, Qu S R.A better algorithm for fusion of infrared and visible image based on curvelet transform and adaptive Pulse Coupled Neural Networks (PCNN) [J].Journal of Northwestern Polytechnical University, 2011, 29(6): 849-853.(in Chinese)[17] 金星,李晖晖,时丕丽.非下采样Contourlet变换与脉冲耦合神经网络相结合的SAR与多光谱图像融合 [J].中国图像图形学报,2012,17(9):1188-1195. Jin X, Li H H, Shi P L.SAR and multispectral image fusion algorithm based on pulse coupled neural networks and non-subsampled Contourlet transform [J].Journal of Image and Graphics, 2012,17(9):1188-1195.(in Chinese)[18] 严春满, 郭宝龙, 马义德,等.一种新的基于双层 PCNN的自适应图像分割算法 [J].光电子激光, 2011,22(7):1102-1106. Yan C M, Guo B L, Ma Y D, et al. New adaptive algorithm for image segmentation using the dual-level PCNN model [J].Journal of OptoelectronicsLaser,2011,22(7):1102-1106.(in Chinese)[19] 王宇庆.基于图像结构信息复数表示与奇异值分解的灰度图像质量评价方法 [J].光电子激光,2012,23(9): 1827-1834. Wang Y Q.Image quality assessment based on complex number representation of image structure and singular value decomposition [J].Journal of OptoelectronicsLaser, 2012,23(9):1827-1834.(in Chinese)[20] Phillips R D, Watson L T, Wynne R H, et al. Feature reduction using a singular value decomposition for the iterative guided spectral class rejection hybrid classifier [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2009,64(1): 107-116.[21] Aznaveh A M, Azadeh M, Azar F T,et al. Image quality measurement besides distortion type classifying [J]. Optical Review, 2009, 16(1): 30-34.[22] Shnayderman A, Gusev A, Eskicioglu A M.A SVD based grayscale image quality measure for local and global assessment [J].IEEE Transactions on Image Process, 2005, 14(2):422-429.[23] 张飞艳, 谢伟, 陈荣元,等.基于视觉加权的奇异值分解压缩图像质量评价测度 [J].电子与信息学报, 2010,32(5):1061-1065. Zhang F Y,Xie W,Chen R Y,et al. Compression image quality assessment based on human visual weight and singular value decomposition [J].Journal of Electronics & Information Technology, 2010,32(5):1061-1065.(in Chinese)[24] 许光宇, 檀结庆, 钟金琴.自适应的有效非局部图像滤波 [J].中国图象图形学报, 2012,17(4):471-479. Xu G Y, Tan J Q, Zhong J Q.Adaptive efficient non-local image filtering [J].Journal of Image and Graphics, 2012, 17(4): 471-479.(in Chinese)[25] Qu G H, Zhang D L, Yan P F.Information measure for performance of image fusion [J].Electronic Letters, 2002,38(7) : 313-315.[26] Wang Z, Bovik A C Sheik H R, et al. Image Quality Assessment: From error visibility to structural similarity [J].IEEE Transactions on Image Processing, 2004,13(4): 600-612.[27] Xydeas C S, Petrovi V.Objective image fusion performance measure [J].Electronics Letters, 2000,36 (4):308-309.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Development of AOI inspection of Mura defects on TFT-LCD surface

Application of dual-wavelength fiber guided light based on OpenCV in high-precision assembly and alignment

Design and implementation of piecewise approximation bilateral filtering algorithm based on FPGA

Defect detection of small object solder joints based on improved YOLOv7

Human posture estimation and movement recognition in fitness behavior

Related Author

CHEN Zekang

SHEN Yi

ZHAI Chenyang

DONG Chenyao

WANG Shuangxi

LÜ Guanhui

LIU Xiaomei

ZHANG Zengbao

Related Institution

College of Engineering， Shantou University

Guangdong Provincial Key Laboratory of Automotive Display and Touch Technologies

College of Mechanical and Electrical Engineering， Hainan University

Key Laboratory of Chemical Lasers， Dalian Institute of Chemical Physics， Chinese Academy of Sciences

School of Microelectronics， Shanghai 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