基于非下采样Shearlet变换与聚焦区域检测的多聚焦图像融合算法

doi:10.11772/j.issn.1001-9081.2015.02.0490

计算机应用 ›› 2015, Vol. 35 ›› Issue (2): 490-494.DOI: 10.11772/j.issn.1001-9081.2015.02.0490

基于非下采样Shearlet变换与聚焦区域检测的多聚焦图像融合算法

欧阳宁¹, 邹宁¹, 张彤², 陈利霞³

1. 认知无线电与信息处理教育部重点实验室(桂林电子科技大学), 广西桂林 541004;
2. 桂林电子科技大学机电工程学院, 广西桂林 541004;
3. 桂林电子科技大学数学与计算科学学院, 广西桂林 541004

收稿日期:2014-09-11 修回日期:2014-11-10 出版日期:2015-02-10 发布日期:2015-02-12
通讯作者: 邹宁
作者简介:欧阳宁(1972-),男,湖南宁远人,教授,博士,主要研究方向:模式识别、智能信息处理、图像信号处理; 邹宁(1988-),男,广西柳州人,硕士研究生,主要研究方向:图像融合、多尺度分析; 张彤(1973-),男,广西桂林人,副教授,硕士,主要研究方向:智能信息处理、视频监控、传感器网络; 陈利霞(1979-),女,湖北黄冈人,副教授,博士,主要研究方向:数字图像处理、小波分析、偏微分方程数值解。
基金资助:
国家自然科学基金资助项目(61362021);广西省自然科学基金资助项目(2013GXNSFDA019030,2013GXNSFAA019331,2012GXNSFBA053014,2012GXNSFAA053231,2014GXNSFDA118035,桂科攻1348020-6,桂科能1298025-7);广西教育厅重点项目(201202ZD044,2013YB091);桂林市科技开发项目(20130105-6,20140103-5)。

Multi-focus image fusion algorithm based on nonsubsampled shearlet transform and focused regions detection

OUYANG Ning¹, ZOU Ning¹, ZHANG Tong², CHEN Lixia³

1. Key Laboratory of Cognitive Radio and Information Processing, Ministry of Education (Guilin University of Electronic Technology), Guilin Guangxi 541004, China;
2. Electromechanical Engineering College, Guilin University of Electronic Technology, Guilin Guangxi 541004, China;
3. School of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilini Guangxi 541004, China

Received:2014-09-11 Revised:2014-11-10 Online:2015-02-10 Published:2015-02-12

摘要/Abstract

摘要：

为了提高基于多尺度变换的多聚焦图像融合中聚焦区域的准确性,提出了一种基于非下采样Shearlet变换(NSST)与聚焦区域检测的多聚焦图像融合算法。首先,通过基于非下采样Shearlet变换的融合方法得到初始融合图像;其次,将初始融合图像与源多聚焦图像作比较,得到初始聚焦区域;接着,利用形态学开闭运算对初始聚焦区域进行修正;最后,在修正的聚焦区域上通过改进的脉冲耦合神经网络(IPCNN)获得融合图像。与经典的基于小波变换、Shearlet变换的融合方法以及当前流行的基于NSST和脉冲耦合神经网络(PCNN)的融合方法相比,所提算法在客观评价指标互信息(MI)、空间频率和转移的边缘信息上均有明显的提高。实验结果表明,所提出的算法能更准确地识别出源图像中的聚焦区域,能从源图像中提取出更多的清晰信息到融合图像。

关键词: 图像融合, 多聚焦图像, 非下采样剪切波变换, 聚焦区域检测, 形态学开闭运算

Abstract:

To improve the accuracy of focusd regions in multifocus image fusion based on multiscale transform, a multifocus image fusion algorithm was proposed based on NonSubsampled Shearlet Transform (NSST) and focused regions detection. Firstly, the initial fused image was acquired by the fusion algorithm based on NSST. Secondly, the initial focusd regions were obtained through comparing the initial fused image and the source multifocus images. And then, the morphological opening and closing was used to correct the initial focusd regions. Finally, the fused image was acquired by the Improved Pulse Coupled Neural Network (IPCNN) in the corrected focusd regions. The experimental results show that, compared with the classic image fusion algorithms based on wavelet or Shearlet, and the current popular algorithms based on NSST and Pulse Coupled Neural Network (PCNN), objective evaluation criterions including Mutual Information (MI), spatial frequency and transferred edge information of the proposed method are improved obviously. The result illustrates that the proposed method can identify the focusd regions of source images more accurately and extract more sharpness information of source images to fusion image.

Key words: image fusion, multifocus image, NonSubsampled Shearlet Transform(NSST), focused region detection, morphological opening and closing

中图分类号:

TP391.41

欧阳宁, 邹宁, 张彤, 陈利霞. 基于非下采样Shearlet变换与聚焦区域检测的多聚焦图像融合算法[J]. 计算机应用, 2015, 35(2): 490-494.

OUYANG Ning, ZOU Ning, ZHANG Tong, CHEN Lixia. Multi-focus image fusion algorithm based on nonsubsampled shearlet transform and focused regions detection[J]. Journal of Computer Applications, 2015, 35(2): 490-494.

参考文献

[1] HUANG W, JING Z. Evaluation of focus measures in multi-focus image fusion [J]. Pattern Recognition Letters, 2007, 28(4): 493-500.
[2] CHAO R, ZHANG K, LI Y. An image fusion algorithm using wavelet transform [J]. Acta Electronica Sinica, 2004, 32(5): 750-753. (晁锐,张科,李言俊.一种基于小波变换的图像融合算法[J].电子学报, 2004, 32(5): 750-753.)
[3] LIANG D, LI Y, SHEN M, et al. An algorithm for multi-focus image fusion using wavelet based contourlet transform [J] Acta Electronica Sinica, 2007, 35(2): 320-322. (梁栋, 李瑶, 沈敏, 等. 一种基于小波——Contourlet变换的多聚焦图像融合算法[J].电子学报, 2007, 35(2): 320-322.)
[4] DO M N, VETTERLI M. The contourlet transform: an efficient directional multiresolution image representation [J]. IEEE Transactions on Image Processing, 2005, 14(12): 2091-2106.
[5] LIM W Q. The discrete shearlet transform: a new directional transform and compactly supported shearlet frames [J]. IEEE Transactions on Image Processing, 2010, 19(5): 1166-1180.
[6] MIAO Q, SHI C, XU P, et al. Multi-focus image fusion algorithm based on shearlets [J]. Chinese Optics Letters, 2011, 9(4): 1-5.
[7] CHENG S, MIAO Q, XU P. A novel algorithm of remote sensing image fusion based on shearlets and PCNN [J]. Neurocomputing, 2013, 117: 47-53.
[8] KONG W, ZHANG L, LEI Y. Novel fusion method for visible light and infrared images based on NSST-SF-PCNN [J]. Infrared Physics & Technology, 2014, 65: 103-112.
[9] LI H, CHAI Y, LI Z. Multi-focus image fusion based on nonsubsampled contourlet transform and focused regions detection [J]. Optik — International Journal for Light and Electron Optics, 2013, 124(1): 40-51.
[10] JOHNSON J L, PADGETT M L. PCNN models and applications [J]. IEEE Transactions on Neural Networks, 1998, 10(3): 480-498.
[11] KONG W, LIU J. Technique for image fusion based on nonsubsampled shearlet transform and improved pulse-coupled neural network [J]. Optical Engineering, 2013, 52(1): 1-12.
[12] WANG N, MA Y, ZHAN K. Spiking cortical model for multifocus image fusion [J]. Neurocomputing, 2014, 130: 44-51.
[13] CHAI Y, LI H, LI Z. Multifocus image fusion scheme using focused region detection and multiresolution [J]. Optics Communications, 2011, 284(19): 4376-4389.
[14] QU G, ZHANG D, YAN P. Information measure for performance of image fusion [J]. Electronics Letters, 2002, 38(7): 313-315.
[15] PETROVIC V, XYDEAS C. Objective evaluation of signal-level image fusion performance [J]. Optical Engineering, 2005, 44(8): 087003.

基于非下采样Shearlet变换与聚焦区域检测的多聚焦图像融合算法

Multi-focus image fusion algorithm based on nonsubsampled shearlet transform and focused regions detection

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	张林发, 张榆锋, 王琨, 李支尧. 基于直觉模糊集和亮度增强的医学图像融合[J]. 计算机应用, 2021, 41(7): 2082-2091.
[2]	牛康力, 谌雨章, 沈君凤, 曾张帆, 潘永才, 王绎冲. 基于深度学习的双通道夜视图像复原方法[J]. 计算机应用, 2021, 41(6): 1775-1784.
[3]	吴蕾, 杨晓敏. 改进的基于通道注意力反馈网络的遥感图像融合算法[J]. 计算机应用, 2021, 41(4): 1172-1178.
[4]	黄思远, 张敏情, 柯彦, 毕新亮. 基于显著性检测的图像隐写分析方法[J]. 计算机应用, 2021, 41(2): 441-448.
[5]	成亚玲, 柏智, 谭爱平. 基于引导滤波和差分图像的多聚焦图像融合方法[J]. 计算机应用, 2021, 41(1): 220-224.
[6]	李卫中. 基于场景局部特征的多曝光图像融合[J]. 计算机应用, 2020, 40(8): 2365-2371.
[7]	王继霄, 李阳, 王家宝, 苗壮, 张洋硕. 基于SqueezeNet的轻量级图像融合方法[J]. 计算机应用, 2020, 40(3): 837-841.
[8]	王书朋, 赵瑶. 基于自适应分割的多曝光图像融合算法[J]. 计算机应用, 2020, 40(1): 252-257.
[9]	牟琦, 唐洋, 李占利, 李洪安. 基于网格运动统计算法和最佳缝合线的密集重复结构图像快速拼接方法[J]. 计算机应用, 2020, 40(1): 239-244.
[10]	刘颖, 王凤伟, 刘卫华, 艾达, 李芸, 杨凡超. 基于亮度分区模糊融合的高动态范围成像算法[J]. 计算机应用, 2020, 40(1): 233-238.
[11]	张斌, 罗晓清, 张战成. 基于TV-L¹结构纹理分解的图像融合质量评价算法[J]. 计算机应用, 2019, 39(9): 2701-2706.
[12]	王克强, 张雨帅, 王保群. 基于Retinex理论的多曝光图像融合算法[J]. 计算机应用, 2019, 39(7): 2087-2092.
[13]	吴宗骏, 吴炜, 杨晓敏, 刘凯, Gwanggil Jeon, 袁皓. 改进的基于稀疏表示的全色锐化算法[J]. 计算机应用, 2019, 39(2): 540-545.
[14]	刘斌, 辛迦楠, 谌文江, 肖惠勇. 不可分拉普拉斯金字塔构造及其在多光谱图像融合中的应用[J]. 计算机应用, 2019, 39(2): 564-570.
[15]	高媛, 吴帆, 秦品乐, 王丽芳. 生成对抗残差网络的医学图像融合算法[J]. 计算机应用, 2019, 39(12): 3528-3534.