Computer Science ›› 2019, Vol. 46 ›› Issue (9): 254-258.doi: 10.11896/j.issn.1002-137X.2019.09.038

• Graphics,Image & Pattern Recognition • Previous Articles     Next Articles

Multi-focus Image Fusion Based on Latent Sparse Representation and Neighborhood Information

ZHANG Bing1,2, XIE Cong-hua2, LIU Zhe3   

  1. (School of Computer Science and Technology,Soochow University,Suzhou,Jiangsu 215006,China)1;
    (School of Computer Science and Engineering,Changshu Institute of Technology,Suzhou,Jiangsu 215500,China)2;
    (School of Computer Science and Communication Engineering,Jiangsu University,Zhenjiang,Jiangsu 212013,China)3
  • Received:2018-07-03 Online:2019-09-15 Published:2019-09-02

Abstract: This paper presented a novel fusion method based on latent sparse representation model for edge blurring and ghost in multi-focus image fusion.Firstly,it decomposes the image into public features,unique features and detail information by using latent sparse representation.Secondly,it combines unique features and detail information to determine the focused and defocused regions.Finally,it uses source information fused multi-focus images based on context information.A large number of experimental results show that the proposed method can effectively fuse multi-focus images.Compared with the most advanced methods,the images processed by this algorithm retain more information of the source image.At the same time,the ghost of the unregistered images is reduced,and the fusion effect of the image is greatly improved.

Key words: Multi-focus image fusion, Sparse representation, Neighborhood information, Unregistered images

CLC Number: 

  • TP391
[1]LI S,KANG X,HU J.Image fusion with guided filtering[J].IEEE Transactions on Image Processing,2013,22(7):2864-2875.
[2]LIU Y,LIU S,WANG Z.Multi-focus image fusion with denseSIFT[J].Information Fusion,2015,23(C):139-155.
[3]CAO C H,ZHANG J H,LI L F.Multi-focus Image FusionBased on Twin--generation Differential Evolution and Adaptive Block Mechanism[J].Computer Science,2016,43(7):67-72,110(in Chinese)曹春红,张建华,李林峰.基于双子代差分演化和自适应分块机制的多聚焦图像融合算法[J].计算机科学,2016,43(7):67-72,110.
[4]YANG B,LI S.Multifocus image fusion and restoration withsparse representation[J].IEEE Transactions on Instrumentation & Measurement,2010,59(4):884-892.
[5]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.
[6] LIU Y,WANG Z.Simultaneous image fusion and denoising with adaptive sparse representation[J].IET Image Processing,2014,9(5):347-357.
[7]YAN C M,GUO B L,YI M.Multi-focus image fusion usingadaptive dictionary learning method[J].Journal of Image and Graphics,2012,17(9):1144-1149.(in Chinese)严春满,郭宝龙,易盟.自适应字典学习的多聚焦图像融合[J].中国图象图形学报,2012,17(9):1144-1149.
[8]YIN H,LI Y,CHAI Y,et al.A novel sparse-representation-based multi-focus image fusion approach[J].Neurocomputing,2016,216(C):216-229.
[9]GAO R,VOROBYOV A.Multi-Focus Image Fusion Via Coupled Sparse Representation and DictionaryLearning[OL].http://pdfs.semanticscholar.org/35a1/30a141b29b20866c8d85db8431489eddcb71.pdf.
[10]ZHANG Q,MARTIN D L.Robust Multi-Focus Image Fusion Using Multi-Task Sparse Representation and Spatial Context[J].IEEE Press,2016,25(5):2045-2058.
[11] DUARTE M F,SARVOTHAM S,BARON D,et al.Distributed compressed sensing of jointly sparse signals[C]//39th Asilomar Conference on Signals,Systems and Computers.2005:1537-1541.
[12]LIN Z,LIU R,SU Z.Linearized alternating direction method with adaptive penalty for low-rank representation[OL].http://www.cis.pku.edu.cn/faculty/vision/zlin/Publications/2011-NIPS-LADM.pdf.
[13]ZHANG Q,GUO B L.Multifocus image fusion using the nonsubsampled contourlet transform[J].Signal Processing,2009,89(7):1334-1346.
[14]HAGHIGHAT M B A,NAGHAGOLZADEH A,SEYEDARABI H.A non-reference image fusion metric based on mutual information of image features[J].Computers and Electrical Engineering,2011,37(5):744-756.
[15]XYDEAS C S,PETROVIC V.Objective image fusion perfor-mance measure[J].Military Technical Courier,2000,56(2):181-193.
[16]HAN Y,CAI Y,CAO Y,et al.A new image fusion performance metric based on visual information fidelity[J].Information Fusion,2013,14(2):127-135.
[1] TIAN Xu, CHANG Kan, HUANG Sheng, QIN Tuan-fa. Single Image Super-resolution Algorithm Using Residual Dictionary and Collaborative Representation [J]. Computer Science, 2020, 47(9): 135-141.
[2] CHENG Zhong-Jian, ZHOU Shuang-e and LI Kang. Sparse Representation Target Tracking Algorithm Based on Multi-scale Adaptive Weight [J]. Computer Science, 2020, 47(6A): 181-186.
[3] WU Qing-hong, GAO Xiao-dong. Face Recognition in Non-ideal Environment Based on Sparse Representation and Support Vector Machine [J]. Computer Science, 2020, 47(6): 121-125.
[4] LI Xiao-yu,GAO Qing-wei,LU Yi-xiang,SUN Dong. Image Fusion Method Based on Image Energy Adjustment [J]. Computer Science, 2020, 47(1): 153-158.
[5] LI Gui-hui,LI Jin-jiang,FAN Hui. Image Denoising Algorithm Based on Adaptive Matching Pursuit [J]. Computer Science, 2020, 47(1): 176-185.
[6] SONG Xiao-xiang,GUO Yan,LI Ning,YU Dong-ping. Missing Data Prediction Algorithm Based on Sparse Bayesian Learning in Coevolving Time Series [J]. Computer Science, 2019, 46(7): 217-223.
[7] ZHANG Fu-wang, YUAN Hui-juan. Image Super-resolution Reconstruction Algorithm with Adaptive Sparse Representationand Non-local Self-similarity [J]. Computer Science, 2019, 46(6A): 188-191.
[8] DU Xiu-li, ZUO Si-ming, QIU Shao-ming. Adaptive Dictionary Learning Algorithm Based on Image Gray Entropy [J]. Computer Science, 2019, 46(5): 266-271.
[9] RU Feng, XU Jin, CHANG Qi, KAN Dan-hui. High Order Statistics Structured Sparse Algorithm for Image Genetic Association Analysis [J]. Computer Science, 2019, 46(4): 66-72.
[10] WANG Ying, LIU Fan, CHEN Ze-hua. Image Fusion Algorithm Based on Improved Weighted Method and AdaptivePulse Coupled Neural Network in Shearlet Domain [J]. Computer Science, 2019, 46(4): 261-267.
[11] MAO Yi-ping, YU Lei, GUAN Ze-jin. Multi-focus Image Fusion Based on Fractional Differential [J]. Computer Science, 2019, 46(11A): 315-319.
[12] MAO Xia, WANG Lan, LI Jian-jun. Human Action Recognition Framework with RGB-D Features Fusion [J]. Computer Science, 2018, 45(8): 22-27.
[13] GAN Ling, ZHAO Fu-chao, YANG Meng. Self-adaptive Group Sparse Representation Method for Image Inpainting [J]. Computer Science, 2018, 45(8): 272-276.
[14] JIA Xu, SUN Fu-ming, LI Hao-jie, CAO Yu-dong. Vein Recognition Algorithm Based on Supervised NMF with Two Regularization Terms [J]. Computer Science, 2018, 45(8): 283-287.
[15] ZHANG Yu-xue,TANG Zhen-min ,QIAN Bin ,XU Wei. Pavement Crack Detection Based on Sparse Representation and Multi-feature Fusion [J]. Computer Science, 2018, 45(7): 271-277.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75 .
[2] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[3] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[4] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[5] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99 .
[6] ZHOU Yan-ping and YE Qiao-lin. L1-norm Distance Based Least Squares Twin Support Vector Machine[J]. Computer Science, 2018, 45(4): 100 -105 .
[7] LIU Bo-yi, TANG Xiang-yan and CHENG Jie-ren. Recognition Method for Corn Borer Based on Templates Matching in Muliple Growth Periods[J]. Computer Science, 2018, 45(4): 106 -111 .
[8] GENG Hai-jun, SHI Xin-gang, WANG Zhi-liang, YIN Xia and YIN Shao-ping. Energy-efficient Intra-domain Routing Algorithm Based on Directed Acyclic Graph[J]. Computer Science, 2018, 45(4): 112 -116 .
[9] CUI Qiong, LI Jian-hua, WANG Hong and NAN Ming-li. Resilience Analysis Model of Networked Command Information System Based on Node Repairability[J]. Computer Science, 2018, 45(4): 117 -121 .
[10] WANG Zhen-chao, HOU Huan-huan and LIAN Rui. Path Optimization Scheme for Restraining Degree of Disorder in CMT[J]. Computer Science, 2018, 45(4): 122 -125 .