资源三号卫星影像超分辨率重建

doi:10.3969/j.issn.0255-8297.2015.03.009

应用科学学报 ›› 2015, Vol. 33 ›› Issue (3): 309-316.doi: 10.3969/j.issn.0255-8297.2015.03.009

资源三号卫星影像超分辨率重建

贾永红1,2，吕臻1，周明婷1

1. 武汉大学遥感信息工程学院，武汉430079
2. 武汉大学测绘遥感信息工程国家重点实验室，武汉430079

收稿日期:2014-12-25 修回日期:2015-02-06 出版日期:2015-05-30 发布日期:2015-02-06
作者简介:贾永红，教授、博导，研究方向：遥感影像分析与应用、航天摄影测量、空间信息管理与更新等，E-mail: yhjia2000@sina.com；周明婷，硕士，研究方向：遥感影像分析与应用等，E-mail: 1949101245@qq.com
基金资助:
国家科技支撑计划课题基金(No.2011BAB01B05)资助

Super Resolution Reconstruction of ZY-3 Satellite Images

JIA Yong-hong1,2, LÜ Zhen1, ZHOU Ming-ting1

1. School of Remote Sensing and Information Engineering, Wuhan University,
Wuhan 430079, China
2. State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

Received:2014-12-25 Revised:2015-02-06 Online:2015-05-30 Published:2015-02-06

摘要/Abstract

摘要： 研究了迭代反投影重建的方法与基于稀疏表示和字典学习的重建方法，将两种重建
算法首次用于资源三号三线阵影像数据的重建试验，并从亮度均值、峰值信噪比、信息熵和清
晰度等四方面对实验结果进行客观分析. 重建影像结果表明：基于字典学习和稀疏表示的重建
方法获得的资源三号重建影像效果优于迭代反投影方法.

关键词: 超分辨率, 特征匹配, 迭代反投影, 字典学习, 稀疏表示

Abstract: Two refactoring approaches, one based on iterative back projection (IBP) and
the other based on sparse representation and dictionary learning, are discussed. Three
linear array images of the ZY-3 satellite are used to reconstruct super-resolution images.
The reconstruction results are evaluated according to four objective criteria, i.e., mean
brightness, PSNR, information entropy, and sharpness of images. The results obtained with
the two approaches show that the sparse representation and dictionary learning method is
better than the iterative back projection method.

Key words: super-resolution, feature matching, iterative back projection (IBP), dictionary learning, sparse representation

中图分类号:

P237.3

贾永红1,2，吕臻1，周明婷1. 资源三号卫星影像超分辨率重建[J]. 应用科学学报, 2015, 33(3): 309-316.

JIA Yong-hong1,2, Lü Zhen1, ZHOU Ming-ting1. Super Resolution Reconstruction of ZY-3 Satellite Images[J]. Journal of Applied Sciences, 2015, 33(3): 309-316.

参考文献

[1] TSAI R Y,HUANG T S. Multiframe Image Restoration and Registration [M]. Greenwich, Conn:JAI Press Inc. 1984, 317-739.

[2] TEKALP A M, OZKAN M K, SEZAN M I. High Resolution Image Reconstruction from Lower Resolution Image Sequences and Space Varying Image Restoration [C], ICASSP, San Francisco,CA,USA,1992.New York: IEEE, 1992, 3: 169-172.

[3]   KALTEMBACHER E, HARDIE R C. High Resolution Infrared Image Reconstruction Using Multiple Low Resolution Aliased Frames[C]. NAECON, Dayton, OH,USA, 1996.New York:IEEE,1996,2:702-709.

[4] BOSE N K, KIM H C, VALENZUELA H M. Recursive Implementation of Total Least Squares Algorithm for Image Reconstruction from Noisy, under sampled Multiframe[C].ICASSP,Minneapolis,MN,1993.New York: IEEE, 1993,5:269-272.

[5] GILMAN A, BAILEY D G, MARSLAND S. Least-Squares Optimal Interpolation for fast Image   Super-Resolution [C]. DELTA, Vietnam, 2010.New York: IEEE,2010: 29-35.

[6] STARK H, P. OSKOUI High-Resolution Image Recovery from Image-Plane Arrays Using Convex Projections[J], J.O.S.A., 1989, 6(11): 1715-1726.

[7] NGUYEN N, MILANFAR P. An Efficient Wavelet-Based Algorithm for Image Super Resolution[C].ICIP, Canada,2000.New York:IEEE,2000,2:351-354.

[8]   HSRDIE R C, K. J, BARNARD, ARMSTRONG E E. Joint MAP Registration and High Resolution Image Estimation Using a Sequence of under sampled Images [J]. IEEE Transaction on Image Processing,1997, 6(12): 1620-1632.

[9] ELAND M, FEUER A. Restoration of a Single Super Resolution Image from Several Blurred, Noisy and under sampled Measured Images [J]. IEEE Transactions on Image Processing, 1997, 6(12):1646-165.

[10] ZHANG K X, CHAN Y H, SIU W C. An Improved Adaptive Wiener Filtering Algorithm for Super Resolution[C]. APSIPA ASC, Sapporo Japan, 2009. Japan: Hokkaido, 2009: 821-824.

[11] BAHY R M, SALAMA G I, MAHMOUD T A. Adaptive regularization based super resolution reconstruction technique for multi-focus low resolution images.[J] Journal of Signal Processing. 2014, 103(10): 155–167

[12] 沈焕锋,李平湘,张良培.一种自适应正则化MAP超分辨率重建方法[J].武汉大学学报.信息科学版，2006，30 (11) : 949-953.

SHEN Huanfeng, LI Pingxiang, ZHANG Liangpei. An Adaptive-regularized MAP Image Super-resolution[J]. Geomatics and Information Science of Wuhan University,2006,30(11): 949-953.

[13] 张新明，沈兰荪．基于多尺度边缘保持正则化的超分辨率复原[J]. 软件学报，2003，14(6)：1075-1081.

ZHANG Xinming, SHEN Lansun. Super-Resolution Restoration with Multi-Scale Edge-Preserving Regularization[J]. Journal of Software, 2003,14(6):1075-1081

[14] CHEN WeiLong, LI Guo; XIA Wenlong. A novel super-resolution reconstruction algorithm based on subspace projection[J]. Journal of Computers (Finland), 2013,8(8) :1893-1897,

[15] 孙广玲，沈宙彪. 基于多组耦合字典及交替学习的图像超分辨率重建[J]. 应用科学学报， 2012，30：642-648.

SUN Guangling, SHEN Zhoubiao. Image Super Resolution Reconstruction Based on Multi-groups of Coupled Dictionary and Alternative Learning[J]. Journal of Applied Sciences, 2012,30:642-648.

[16]   刘治州.基于字典学习的超分辨率图像重构[D].西安：西安电子科技大学，2011.

LIU Zhizhou. Dictionary Learning based Super-Resolution Image Reconstruction [D]. Xian: Xidian University, 2011

[17] DONOHO D L. For most large underdetermined systems of equations, the minimal 11-norm near-solution approximates the sparsest near-solution. Communications on Pure and Applied Mathematics, 2006, 59(7):907-934.

[18] CANDES E, TAO T. Decoding by linear programming[J]. IEEE Transaction on Information Theory,2005,51:4203-4215.

[19] CANDES E, TAO T. Near optimal signal recovery from random projections: Universal encoding strategies[J]. IEEE Transactions on Information Theory,2006,52(12):5406-5425.

[20] RUBINSTEIN R, ZIBULEVSKY M, ELAD M. Double sparsity: learning sparse dictionaries for sparse signal approximation[J]. IEEE transactions on signal processing, 2009, 58(2):1553一1564.

[21] MAIRAL J, BACH F, PONCE J. Supervised dictionary learning[J]. Neural information processing systems foundation, 2009: 1-8.

[22] MAIRAL J, BACH F, PONCE J. Online learning for matrix factorization and sparse coding[J]. Journal of Machine Learning Research, 2010, 11:19-60.

[23] MAIRAL J, BACH F, PONCE J. Online dictionary learning for sparse coding. International Conference on Machine Learning (ICML)[J]. Montreal, Quebec, Canada: ACM Press, 2009:689-696.

资源三号卫星影像超分辨率重建

Super Resolution Reconstruction of ZY-3 Satellite Images

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