[1]王建,吴锡生.基于改进的稀疏表示和PCNN的图像融合算法研究[J].智能系统学报,2019,14(5):922-928.[doi:10.11992/tis.201805045]
WANG Jian,WU Xisheng.Image fusion based on the improved sparse representation and PCNN[J].CAAI Transactions on Intelligent Systems,2019,14(5):922-928.[doi:10.11992/tis.201805045]
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WANG Jian,WU Xisheng.Image fusion based on the improved sparse representation and PCNN[J].CAAI Transactions on Intelligent Systems,2019,14(5):922-928.[doi:10.11992/tis.201805045]
基于改进的稀疏表示和PCNN的图像融合算法研究
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
14
期数:
2019年第5期
页码:
922-928
栏目:
学术论文—机器学习
出版日期:
2019-09-05
- Title:
- Image fusion based on the improved sparse representation and PCNN
- Keywords:
- image processing; image fusion; NSST; sparse representation; adaptive learning dictionary; joint dictionary; PCNN; improved spatial frequency
- 分类号:
- TP391.2
- 摘要:
- 为提高图像融合的清晰度，本文提出一种基于改进的稀疏表示和脉冲耦合神经网络（pulse coupled neural network，PCNN）的图像融合。利用非下采样剪切波变换（non-subsampled shearlet transform，NSST）对源图像进行分解变换，得到相应的低频子带和高频子带具有不同的信息。对于低频子带，采用改进的稀疏表示进行融合，利用K奇异值分解（K-singular value decomposition，K-SVD）算法，并对源图像进行自适应学习的多个子字典构造成联合词典。对于高频子带，则改进PCNN融合系数的选择方法，利用改进的空间频率作为神经元反馈输入来激励PCNN模型，并根据点火输出的总幅度最大的融合规则选择高频系数。最后，将融合后的低频子带和高频子带系数进行NSST逆变换，重构出融合图像。实验结果表明：该算法很好地保留了图像的边缘信息，并且得到的图像在相关的客观评价标准上也取得了良好的效果，表明了本算法的有效性。
- Abstract:
- To improve the clarity of image fusion, in this paper, we propose an image-fusion algorithm based on improved sparse representation and a pulse-coupled neural network (PCNN). First, using a non-subsampled shearlet transform (NSST), source images are decomposed into low-frequency and high-frequency sub-band coefficients, which contain different information. Then, we use the K-singular value decomposition algorithm to fuse the improved sparse representation with low-frequency sub-band coefficients and construct a joint dictionary from the adaptive learning multiple sub-dictionaries in the source images. The high-frequency sub-band coefficients are fused with the improved PCNN. To stimulate the PCNN model, we use the modified spatial frequency as neuron feedback input. The high-frequency coefficients are selected according to the fusion rule for the maximum amplitude of fire output. Finally, we reconstruct the fused image with the NSST inverse transform of the fused low-frequency and high-frequency sub-band coefficients. The experimental results show that the proposed algorithm preserves the edge information of the source images very well; additionally, the fused image achieves good results on the evaluation criteria, thus verifying the effectiveness of the proposed algorithm.
- 参考文献/References:
-
[1] 王卫卫, 水鹏朗, 宋国乡. 小波域多聚焦图像融合算法[J]. 系统工程与电子技术, 2004, 26(5):668-671 WANG Weiwei, SHUI Penglang, SONG Guoxiang. Multi-focus image fusion in wavelet domain[J]. Systems engineering and electronics, 2004, 26(5):668-671
[2] LI Shutao, KANG Xudong, FANG Leyuan, et al. Pixel-level image fusion:A survey of the state of the art[J]. Information fusion, 2017, 33:100-112.
[3] ROCKINGER O, FECHNER T. Pixel-level image fusion:the case of image sequences[C]//Proceedings of SPIE 3374, Signal Processing, Sensor Fusion, and Target Recognition VⅡ. Orlando, USA, 1998:378–388.
[4] EHLERS M. Multisensor image fusion techniques in remote sensing[J]. ISPRS journal of photogrammetry and remote sensing, 1991, 46(1):19-30.
[5] 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 Jingchao, QU Shiru. 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
[6] 张雄美, 李俊山, 易昭湘, 等. 非抽样轮廓波变换域图像融合方法研究[J]. 电光与控制, 2008, 15(7):16-19 ZHANG Xiongmei, LI Junshan, YI Zhaoxiang, et al. Image fusion based on nonsubsampled contourlet[J]. Electronics optics and control, 2008, 15(7):16-19
[7] EASLEY G R, LABATE D, LIM W Q. Optimally sparse image representations using shearlets[C]//Proceedings of 2006 Fortieth Asilomar Conference on Signals, Systems and Computers. Pacific Grove, USA, 2006:974–978.
[8] YANG Bin, LI Shutao. Multifocus image fusion and restoration with sparse representation[J]. IEEE transactions on instrumentation and measurement, 2010, 59(4):884-892.
[9] BROUSSARD R P, Rogers S K, Oxley M E, et al. Physiologically motivated image fusion for object detection using a pulse coupled neural network[J]. IEEE Transactions on neural networks, 1999, 10(3):554-563.
[10] 赵春晖, 郭蕴霆. 一种快速的基于稀疏表示和非下采样轮廓波变换的图像融合算法[J]. 电子与信息学报, 2016, 38(7):1773-1780 ZHAO Chunhui, GUO Yunting. Fast image fusion algorithm based on sparse representation and non-subsampled contourlet transform[J]. Journal of electronics and information technology, 2016, 38(7):1773-1780
[11] XIANG Fengtao, JIAN Zhang, LIANG Pan, et al. Robust image fusion with block sparse representation and online dictionary learning[J]. IET image processing, 2018, 12(3):345-353.
[12] 谢秋莹, 易本顺, 柯祖福, 等. 基于SML和PCNN的NSCT域多聚焦图像融合[J]. 计算机科学, 2017, 44(6):266-269, 282 XIE Qiuying, YI Benshun, KE Zufu, et al. Multi-focus image fusion based on SML and PCNN in NSCT domain[J]. Computer science, 2017, 44(6):266-269, 282
[13] 王红梅, 付浩. 脉冲耦合神经网络自适应图像融合算法研究[J]. 计算机工程与应用, 2017, 53(7):177-180 WANG Hongmei, FU Hao. Adaptive image fusion algorithm based on pulse coupled neural networks[J]. Computer engineering and applications, 2017, 53(7):177-180
[14] 张宝华, 吕晓琪. 一种复合型PCNN的NSCT域多聚焦图像融合方法[J]. 小型微型计算机系统, 2014, 35(2):393-396 ZHANG Baohua, LV Xiaoqi. Multi-focus image fusion algorithm based on compound PCNN in NSCT domain[J]. Journal of chinese computer systems, 2014, 35(2):393-396
[15] DING Shifei, ZHAO Xingyu, XU Hui, et al. NSCT-PCNN image fusion based on image gradient motivation[J]. IET computer vision, 2018, 12(4):377-383.
[16] 田娟秀, 刘国才. 基于NSST变换和PCNN的医学图像融合方法[J]. 中国医学物理学杂志, 2018, 35(8):914-920 TIAN Juanxiu, LIU Guocai. Medical image fusion method based on non-subsampled shearlet transform and pulse coupled neural network[J]. Chinese journal of medical physics, 2018, 35(8):914-920
[17] 夏景明, 陈轶鸣, 陈轶才, 等. 基于稀疏表示和NSCT-PCNN的红外与可见光图像融合[J]. 电光与控制, 2018, 25(6):1-6 XIA Jingming, CHEN Yiming, CHEN Yicai, et al. Fusion of infrared and visible images based on sparse representation and NSCT-PCNN[J]. Electronics optics and control, 2018, 25(6):1-6
[18] ZHANG Qiang, LI Baoxin. Discriminative K-SVD for dictionary learning in face recognition[C]//Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco, USA, 2010:2691–2698.
[19] HONG Richang. Objective image fusion performance measure[J]. Military technical courier, 2000, 56(2):181-193.
[20] 王峰, 程咏梅. 基于MNLMF和SF方向滤波的图像融合算法[J]. 控制与决策, 2017, 32(12):2183-2189 WANG Feng, CHENG Yongmei. Image fusion method based on multi-scale non-local mean filter and shear direction filter[J]. Control and decision, 2017, 32(12):2183-2189
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备注/Memo
收稿日期:2018-05-29。
基金项目:国家自然科学基金项目（61672265）.
作者简介:王建,男,1992年生,硕士研究生,主要研究方向为图像融合;吴锡生,男,1959年生,教授,博士,主要研究方向为图像处理和模式识别。曾获江苏省科技进步三等奖2次,中国纺织协会和无锡市科技进步奖3次,软件著作权授权1项,发明专利授权3项。发表学术论文40余篇。
通讯作者:吴锡生.E-mail:wxs@jiangnan.edu.cn
更新日期/Last Update:
1900-01-01