LIU Shuai, ZHU Yajie, XUE Lei. Remote Sensing Image Super-Resolution Method Using Sparse Representation and Classified Texture Patches[J]. Geomatics and Information Science of Wuhan University, 2015, 40(5): 578-582. DOI: 10.13203/j.whugis20130385
Citation: LIU Shuai, ZHU Yajie, XUE Lei. Remote Sensing Image Super-Resolution Method Using Sparse Representation and Classified Texture Patches[J]. Geomatics and Information Science of Wuhan University, 2015, 40(5): 578-582. DOI: 10.13203/j.whugis20130385

Remote Sensing Image Super-Resolution Method Using Sparse Representation and Classified Texture Patches

Funds: The National Natural Science Foundation of China,No.61303128;the Scientific Research and Development Programof Qinhuangdao,No.2012023A234.
More Information
  • Author Bio:

    LIU Shuai: 国家自然科学基金资助项目(61303128);秦皇岛市科学技术研究与发展计划资助项目(2012023A234)

  • Received Date: August 05, 2013
  • Revised Date: May 04, 2015
  • Published Date: May 04, 2015
  • A super-resolution method based on sparse representation and classified texture patches wasproposed,mainly using the priori knowledge and texture to reconstruct remote sensing images.First,extract image blocks for dictionary learning,the over-complete dictionary was learned from the highand low resolution remote sensing image blocks.Orthogonal match pursuit was used to calculate thesparse conefficients,then the coefficients were fixed,iterative method was used to update the diction-ary until the algorithm converges.Then,the training dictionary was used to reconstruct the remotesensing images.In this step,the image was divided into smooth patches and non-smooth patches,bicubic interpolation was used for smooth patches while sparse conefficients and over-complete diction-ary were used for non-smooth patches.Experiment shows that this method has a faster reconstructionspeed and can achieve satisfied super-resolution results in the visual effects and objective evaluation in-dicators.
  • [1]
    Huang X,Zhang L.An SVM Ensemble ApproachCombining Spectral,Structural,and Semantic Fea-tures for the Classification of High-Resolution Re-motely Sensed Imagery[J].IEEE Transactions onGeoscience and Remote Sensing,2013,51(1):257-272[2] Zhang Qian,Huang Xin,Zhang Liangpei.Multi-scale Image Segmentation and Classification withSupervised ECHO of High Spatial Resolution Re-motely Sensed Imagery[J].Geomatics and Infor-mation Science of Wuhan University,2011,36(1):117-121(张倩,黄昕,张良培.多尺度同质区域提取的高分辨率遥感影像分类研究[J].武汉大学学报·信息科学版,2011,36(1):117-121)[3] Dong W,Zhang L,Shi G,et al.Image Deblurringand Super-resolution by Adaptive Sparse DomainSelection and Adaptive Regularization[J].IEEETransactions on Image Processing,2011,20(7):1 838-1 857[4] Harris J.Diffraction and Resolving Power[J].Journal of the Optical Society of America,1964,54(7):931-936[5] Shen Huanfeng,Li Pingxiang,Zhang Liangpei.A-daptive Regularized MAP Super-Resolution Recon-struction Method[J].Geomatics and InformationScience of Wuhan University,2006,31(11):949-952(沈焕锋,李平湘,张良培.一种自适应正则MAP超分辨率重建方法[J].武汉大学学报·信息科学版,2006,31(11):949-952)[6] Irani M,Peleg S.Improving Resolution by ImageRegistration[J].CVGIP:Graphical models andImage Processing,1991,53(3):231-239[7] Stark H,Oskoui P.High-Resolution Image Recov-ery from Image-Plane Arrays,Using Convex Pro-jections[J].Journal of the Optical Society of A-merica A,1989,6(11):1 715-1 726[8] Lan Chengdong,Chen Liang,Lu Tao.Face Super-resolution Using Sparse Representation with Posi-tion Weights[J].Geomatics and Information Sci-ence of Wuhan University,2013,38(1):27-30(兰诚栋,陈亮,卢涛.利用位置权重稀疏表示的人脸超分辨率算法[J].武汉大学学报·信息科学版,2013,38(1):27-30)582武汉大学学报·信息科学版2015年5月[9] Song Xiangfa,Jiao Licheng.Classification of Hy-perspectral Remote Sensing Image Based on Sparse Representation and Spectral Information[J].Jour-nal of Electronics &Information Technology,2012,34(2):268-272(宋相法,焦李成.基于稀疏表示及光谱信息的高光谱遥感图像分类[J].电子与信息学报,2012,34(2):268-272)[10] Lian Qiusheng,Zhou Ting.Adaptive CompressedImaging Algorithm Combined the Sparse Represen-tation in the Dictionaries with Non-Local Similarity [J].Acta Electronica Sinica,2012,40(7):1 416-1 422(练秋生,周婷.结合字典稀疏表示和非局部相似性的自适应压缩成像算法[J].电子学报,2012,40(7):1 416-1 422)[11] Tropp J A.Just Relax:Convex ProgrammingMethods for Identifying Sparse Signals in Noise[J].IEEE Transactions on Information Theory,2006,52(3):1 030-1 051[12] Yang J,Wright J,Huang T,et al.Image Super-Resolution as Sparse Representation of Raw ImagePatches[C].IEEE Conference on Computer Visionand Pattern Recognition,Anchorage,AK,2008[13] Aharon M,Elad M,Bruckstein A.K-SVD:An Al-gorithm for Designing Overcomplete Dictionaries forSparse Representation[J].IEEE Transactions onSignal Processing,2006,54(11):4 311-4 322[14] Needell D,Vershynin R.Signal Recovery From In-complete and Inaccurate Measurements Via Regular-ized Orthogonal Matching Pursuit[J].IEEE Jour-nal of Selected Topics in Signal Processing,2010,4(2):310-316[15] Hong C,Dit-Yan Y,Yimin X.Super-Resolutionthrough Neighbor Embedding[C].IEEE ComputerSociety Conference on Computer Vision and PatternRecognition,Washington D C,USA,2004

Catalog

    Article views (3818) PDF downloads (1332) Cited by()
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return