计算机科学 ›› 2021, Vol. 48 ›› Issue (9): 181-186.doi: 10.11896/jsjkx.200800064
黄晓生, 徐静
HUANG Xiao-sheng, XU Jing
摘要： 近年来,基于深度学习模型的图像融合方法备受关注。而传统的深度学习模型通常需要耗时长和复杂的训练过程,并且涉及参数众多。针对这些问题,文中提出了一种基于简单的深度学习模型PCANet的非下采样剪切波(Non-Subsanmpled Shearlet Transform,NSST)域多聚焦图像融合方法。首先,利用多聚焦图像训练两阶段PCANet,用于提取图像特征。然后,对输入源图像进行NSST分解,得到源图像的多尺度和多方向表示。低频子带利用训练好的PCANet提取其图像特征,并利用核范数构造有效的特征空间进行图像融合。高频子带利用区域能量取大的融合规则进行融合。最后对根据不同融合规则融合后的频率系数进行NSST重构,获取清晰的目标图像。实验结果表明,所提算法的训练和融合速度比基于CNN的方法提高了43%,该算法的平均梯度、空间频率、熵等融合性能分别为5.744,15.560和7.059,可以与现有融合方法相媲美或优于现有的融合方法。
中图分类号:
| [1]LI S,KANG X,FANG L,et al.Pixel-level image fusion:A survey of the state of the art[J].Information Fusion,2016,33:100-112. [2]DOU J F,QIN Q,TU Z M.Image fusion based on wavelettransform with genetic algorithms and human visual system[J].Multimedia Tools and Applications,2019,78(9):12491-12517. [3]MAO R,FU X,NIU P,et al.Multi-directional Laplacian Pyramid Image Fusion Algorithm[C]//2018 3rd International Conference on Mechanical,Control and Computer Engineering (ICMCCE).IEEE Computer Society,2018. [4]DO M N,VETTERLI M.The Contourlet Transform:An Efficient Directional Multiresolution Image Representation[J].IEEE Transactions on Image Processing,2006,14(12):2091-2106. [5]ZHU D R,XU L,WANG F B,et al.Multi-focus image fusion Algorithm based on Fast Finite Shear Wave Transform and Guided Filter[J].Advances in Laser and Optoelectronics,2018,55(1):190-197. [6]LIU Y,LIU S,WANG Z.A general framework for image fusion based on multi-scale transform and sparse representation[J].Information Fusion,2015,24:147-164. [7]LIU Y,CHEN X,WANG Z,et al.Deep learning for pixel-level image fusion:Recent advances and future prospects[J].Information Fusion,2018,42:158-173. [8]LI P,WANG H,LI X,et al.A novel Image Fusion Framework based on Non-Subsampled Shearlet Transform (NSST) Domain[C]//2019 Chinese Control And Decision Conference (CCDC).2019. [9]YIN M,LIU X,LIU Y,et al.Medical Image Fusion With Pa-rameter-Adaptive Pulse Coupled Neural Network in Nonsubsampled Shearlet Transform Domain[J].Instrumentation & Measurement IEEE Transactions on,2019,68(1):49-64. [10]WU Y,TAO F.Multispectral and panchromatic image fusionbased on improved projected gradient NMF in NSST domain[J].Guangxue Xuebao/acta Optica Sinica,2015,35(4):0410005. [11]LIU Y,CHEN X,PENG H,et al.Multi-focus image fusion with a deep convolutional neural network[J].Information Fusion,2017,36:191-207. [12]DU J K,LONG H L,DING D D,et al.CNN image fusion algorithm based on non-subsampled Shearlet transform[J].Audio Engineering,2019,43(3):37-41,73. [13]CHAN T H,JIA K,GAO S,et al.PCANet:A Simple DeepLearning Baseline for Image Classification?[J].IEEE Transactions on Image Processing,2015,24(12):5017-5032. [14]ZHANG D D,LI L.Face detection system based on PCANET-RF[J].Computer Technology and Development,2016(2):31-34. [15]SONG X,WU X J.Multi-focus Image Fusion with PCA Filters of PCANet[M]//Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction.2019. [16]LABATE D,LIM W Q,KUTYNIOK G,et al. Sparse multidimensional representation using shearlets[C]//2005 Proceedings of SPIE－The International Society for Optical Engineering.Bellingham,WA,USA,2005. [17]EASLEY G,LABATE D,LIM W Q.Sparse directional image representations using the discrete shearlet transform[J].Applied & Computational Harmonic Analysis,2008,25(1):25-46. [18]ZHANG L T,LV X L,SUN M X,et al.Image Fusion Method Based on Wavelet Transform[J].Electro-Optic Technology Application,2016:204-210. [19]LIU J S,JIANG W.Improved image fusion algorithm based on nonsubsampled Contourlet transform[J].Journal of Computer Applications,2018,38(z1):194-197. [20]WANG H,AHMED M S.Multi-focus image fusion algorithmbased on non-subsampled shearlet transform and focus measure[C]//International Conference on Robotics & Machine Vision.2017. [21]LIU S Q,WANG J,AN Y L,et al.Multi-focus image fusion in non-subsampled shear Wave domain based on CNN[J].Journal of Zhengzhou University:Engineering Science,2019,40(4):36-41. |
| [1] | 饶志双, 贾真, 张凡, 李天瑞. 基于Key-Value关联记忆网络的知识图谱问答方法 Key-Value Relational Memory Networks for Question Answering over Knowledge Graph 计算机科学, 2022, 49(9): 202-207. https://doi.org/10.11896/jsjkx.220300277 |
| [2] | 汤凌韬, 王迪, 张鲁飞, 刘盛云. 基于安全多方计算和差分隐私的联邦学习方案 Federated Learning Scheme Based on Secure Multi-party Computation and Differential Privacy 计算机科学, 2022, 49(9): 297-305. https://doi.org/10.11896/jsjkx.210800108 |
| [3] | 徐涌鑫, 赵俊峰, 王亚沙, 谢冰, 杨恺. 时序知识图谱表示学习 Temporal Knowledge Graph Representation Learning 计算机科学, 2022, 49(9): 162-171. https://doi.org/10.11896/jsjkx.220500204 |
| [4] | 王剑, 彭雨琦, 赵宇斐, 杨健. 基于深度学习的社交网络舆情信息抽取方法综述 Survey of Social Network Public Opinion Information Extraction Based on Deep Learning 计算机科学, 2022, 49(8): 279-293. https://doi.org/10.11896/jsjkx.220300099 |
| [5] | 郝志荣, 陈龙, 黄嘉成. 面向文本分类的类别区分式通用对抗攻击方法 Class Discriminative Universal Adversarial Attack for Text Classification 计算机科学, 2022, 49(8): 323-329. https://doi.org/10.11896/jsjkx.220200077 |
| [6] | 姜梦函, 李邵梅, 郑洪浩, 张建朋. 基于改进位置编码的谣言检测模型 Rumor Detection Model Based on Improved Position Embedding 计算机科学, 2022, 49(8): 330-335. https://doi.org/10.11896/jsjkx.210600046 |
| [7] | 孙奇, 吉根林, 张杰. 基于非局部注意力生成对抗网络的视频异常事件检测方法 Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection 计算机科学, 2022, 49(8): 172-177. https://doi.org/10.11896/jsjkx.210600061 |
| [8] | 侯钰涛, 阿布都克力木·阿布力孜, 哈里旦木·阿布都克里木. 中文预训练模型研究进展 Advances in Chinese Pre-training Models 计算机科学, 2022, 49(7): 148-163. https://doi.org/10.11896/jsjkx.211200018 |
| [9] | 周慧, 施皓晨, 屠要峰, 黄圣君. 基于主动采样的深度鲁棒神经网络学习 Robust Deep Neural Network Learning Based on Active Sampling 计算机科学, 2022, 49(7): 164-169. https://doi.org/10.11896/jsjkx.210600044 |
| [10] | 苏丹宁, 曹桂涛, 王燕楠, 王宏, 任赫. 小样本雷达辐射源识别的深度学习方法综述 Survey of Deep Learning for Radar Emitter Identification Based on Small Sample 计算机科学, 2022, 49(7): 226-235. https://doi.org/10.11896/jsjkx.210600138 |
| [11] | 胡艳羽, 赵龙, 董祥军. 一种用于癌症分类的两阶段深度特征选择提取算法 Two-stage Deep Feature Selection Extraction Algorithm for Cancer Classification 计算机科学, 2022, 49(7): 73-78. https://doi.org/10.11896/jsjkx.210500092 |
| [12] | 程成, 降爱莲. 基于多路径特征提取的实时语义分割方法 Real-time Semantic Segmentation Method Based on Multi-path Feature Extraction 计算机科学, 2022, 49(7): 120-126. https://doi.org/10.11896/jsjkx.210500157 |
| [13] | 祝文韬, 兰先超, 罗唤霖, 岳彬, 汪洋. 改进Faster R-CNN的光学遥感飞机目标检测 Remote Sensing Aircraft Target Detection Based on Improved Faster R-CNN 计算机科学, 2022, 49(6A): 378-383. https://doi.org/10.11896/jsjkx.210300121 |
| [14] | 王建明, 陈响育, 杨自忠, 史晨阳, 张宇航, 钱正坤. 不同数据增强方法对模型识别精度的影响 Influence of Different Data Augmentation Methods on Model Recognition Accuracy 计算机科学, 2022, 49(6A): 418-423. https://doi.org/10.11896/jsjkx.210700210 |
| [15] | 毛典辉, 黄晖煜, 赵爽. 符合监管合规性的自动合成新闻检测方法研究 Study on Automatic Synthetic News Detection Method Complying with Regulatory Compliance 计算机科学, 2022, 49(6A): 523-530. https://doi.org/10.11896/jsjkx.210300083 |
|
||
首页