遥感影像超分辨率重建的字典学习类算法

doi:10.13474/j.cnki.11-2246.2017.0182

测绘通报 ›› 2017, Vol. 0 ›› Issue (6): 21-25.doi: 10.13474/j.cnki.11-2246.2017.0182

遥感影像超分辨率重建的字典学习类算法

杨振胤¹, 隋立春^1,2, 李丽¹, 康军梅¹, 丁明涛¹

1. 长安大学地质工程与测绘学院, 陕西西安 710054;
2. 地理国情监测国家测绘地理信息局工程技术研究中心, 陕西西安 710054

收稿日期:2017-03-15 出版日期:2017-06-25 发布日期:2017-07-03
作者简介:杨振胤(1990-),男,硕士,主要研究方向为遥感影像超分辨率重建。E-mail:yangzy007@chd.edu.cn
基金资助:
国家自然科学基金（41372330）；国家自然科学基金青年基金（41601345）

Super-resolution Methods Based on Dictionary Learning for Remote Sensing Images

YANG Zhenyin¹, SUI Lichun^1,2, LI Li¹, KANG Junmei¹, DING Mingtao¹

1. College of Geology Engineering and Geomatics, Chang'an University, Xi'an 710054, China;
2. Engineering Research Center, Geographical Conditions Monitoring National Administration of Surveying, Mapping and Geoinformation, Xi'an 710054, China

Received:2017-03-15 Online:2017-06-25 Published:2017-07-03

摘要/Abstract

摘要： 近年来基于字典学习的超分辨率重建技术已成为图像处理领域的研究热点，相比基于重建的超分辨率方法，基于学习的方法充分利用了先验知识，在放大倍数较高时，仍可取得较好的效果，因此被公认为一种非常有前途的方法。本文对国内外已有的基于字典学习的超分辨率重建方法进行了系统研究，梳理了3种基于字典学习超分重建算法的基本原理及优缺点。此外，本文根据遥感影像的特点，使用同一数据源进行字典学习，利用不同字典学习算法分别生成高、低联合字典对，采用不同尺寸大小及缩放倍数的测试图像，进行超分辨率重建，对各种算法的重建性能、鲁棒性和复杂度进行综合分析，进一步研究了各种算法对遥感影像不同应用需求的适用性。

关键词: 超分辨率重建, 稀疏表示, 遥感影像, 字典学习

Abstract: In recent years, super-resolution reconstruction technology based on dictionary learning has obtained much attention and has been intensively studied. Compared with the super-resolution method based on reconstruction, the learning-based method makes full use of prior knowledge. This learning-based method can get better results when magnification is high, which has been recognized as an extremely promising method. The properties of the existed learning-based super-resolution reconstruction algorithms are firstly analyzed systematically. Then this paper reviews the theory of three learning-based algorithms and combs their advantages and disadvantages. Finally, according to characteristics of remote sensing image, the same data sources are used for dictionary learning. We select these three algorithms mentioned to generate high and low resolution joint dictionary and adopt test images of different sizes and zoom and complete reconstruction. The reconstruction performance, robustness and complexity of various algorithms are analyzed comprehensively by experimental results. What's more, aiming at different application requirements of remote sensing image, the applicability of different algorithms is further studied.

Key words: super-resolution reconstruction, sparse representation, remote sensing imagery, dictionary learning

中图分类号:

P237

杨振胤, 隋立春, 李丽, 康军梅, 丁明涛. 遥感影像超分辨率重建的字典学习类算法[J]. 测绘通报, 2017, 0(6): 21-25.

YANG Zhenyin, SUI Lichun, LI Li, KANG Junmei, DING Mingtao. Super-resolution Methods Based on Dictionary Learning for Remote Sensing Images[J]. 测绘通报, 2017, 0(6): 21-25.

参考文献

[1] 李德仁,童庆禧,李荣兴,等. 高分辨率对地观测的若干前沿科学问题[J]. 中国科学(地球科学),2012(6):805-813.
[2] 钟九生.基于稀疏表示的光学遥感影像超分辨率重建算法研究[D]. 南京:南京师范大学,2013:1-14.
[3] FREEMAN W T,JONES T R,PASZTOR E C. Example-based Super-resolution[J]. IEEE Computer Graphics and Applications,2002, 22(2):56-65.
[4] BISHOP C M, BLAKE A, MARTHI B. Super-resolution Enhancement of Video[C]//AISTATS.[S.l.]:[s.n.], 2003.
[5] JEONG S C,SONG B C. Fast Super-resolution Algorithm Based on Dictionary Size Reduction Using k-means Clustering[J]. ETRI Journal, 2010, 32(4):596-602.
[6] KIM C, CHOI K, RA J B. Improvement on Learning-based Super-resolution by Adopting Residual Information and Patch Reliability[C]//2009 16th IEEE International Conference on Image Processing (ICIP).[S.l.]:IEEE, 2009:1197-1200.
[7] PU J, ZHANG J, GUO P, et al. Interactive Super-resolution through Neighbor Embedding[C]//Asian Conference on Computer Vision. Berlin:Springer Berlin Heidelberg, 2009:496-505.
[8] YANG J, WRIGHT J, HUANG T, et al. Image Super-resolution as Sparse Representation of Raw Image Patches[C]//IEEE Conference on CVPR 2008.[S.l.]:IEEE,2008:1-8.
[9] YANG J, WRIGHT J, HUANG T S, et al. Image Super-resolution via Sparse Representation[J]. IEEE Transactions on Image Processing, 2010, 19(11):2861-2873.
[10] ZEYDE R, ELAD M, PROTTER M. On Single Image Scale-up Using Sparse-representations[C]//International Conference on Curves and Surfaces. Berlin:Springer Berlin Heidelberg, 2010:711-730.
[11] MAIRAL J, BACH F, PONCE J, et al. Online Learning for Matrix Factorization and Sparse Coding[J]. Journal of Machine Learning Research, 2010, 11(1):19-60.
[12] ENGAN K, AASE S O, HUSOY J H. Method of Optimal Directions for Frame Design[C]//IEEE International Conference on Acoustics, Speech, and Signal Processing.[S.l.]:IEEE, 1999, 5:2443-2446.
[13] AHARON M, ELAD M, BRUCKSTEIN A.The K-SVD:An Algorithm for Designing of Overcomplete Dictionaries for Sparse Representation[J]. IEEE Transactions on Signal Processing, 2006, 54(11):4311-4322.
[14] LEE H, BATTLE A, RAINA R, et al. Efficient Sparse Coding Algorithms[C]//Advances in Neural Information Processing Systems.[S.l.]:[s.n.],2006:801-808.
[15] MAIRAL J,BACH F,PONCE J.Task-driven Dictionary Learning[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(4):791-804.
[16] 李珅. 基于稀疏表示的图像去噪和超分辨率重建研究[D].西安:西安光学精密机械研究所, 2014.
[17] 杨真真,杨震,孙林慧. 信号压缩重构的正交匹配追踪类算法综述[J]. 信号处理, 2013, 29(4):486-496.
[18] VIDAL R, MA Y, SASTRY S. Generalized Principal Component Analysis (GPCA)[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(12):1945-1959.
[19] 倪浩,阮若林,刘芳华. 基于双正则化参数的在线字典学习超分辨率重建[J]. 计算机应用研究, 2016, 33(3):277-281.
[20] ZHANG J,ZHAO C, XIONG R, et al. Image Super-resolution via Dual-dictionary Learning and Sparse Representation[C]//2012 IEEE International Symposium on Circuits and Systems.[S.l.]:IEEE, 2012:1688-1691.

遥感影像超分辨率重建的字典学习类算法

Super-resolution Methods Based on Dictionary Learning for Remote Sensing Images

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