Super-resolution algorithm for remote sensing images based on compressive sensing in wavelet domain

doi:10.11772/j.issn.1001-9081.2017.05.1430

Journal of Computer Applications ›› 2017, Vol. 37 ›› Issue (5): 1430-1433.DOI: 10.11772/j.issn.1001-9081.2017.05.1430

Previous Articles Next Articles

Super-resolution algorithm for remote sensing images based on compressive sensing in wavelet domain

YANG Xuefeng, CHENG Yaoyu, WANG Gao

School of Information and Communication Engineering, North University of China, Taiyuan Shanxi 030051, China

Received:2016-09-27 Revised:2016-12-12 Online:2017-05-16 Published:2017-05-10
Supported by:
This work is supported by the National Defense Key Laboratory Fund Program of China (9140C120402120C1208).

基于小波域压缩感知的遥感图像超分辨算法

杨学峰, 程耀瑜, 王高

中北大学信息与通信工程学院, 太原 030051

通讯作者: 杨学峰
作者简介:杨学峰(1976-),男,河南商丘人,讲师,博士,主要研究方向:图像复原与超分辨重建、模式识别、计算机视觉;程耀瑜(1966-),男,山西平遥人,教授,博士,主要研究方向:信息获取与处理、图像处理、测控系统;王高(1973-),男,山西襄汾人,教授,博士,主要研究方向:光电信息处理。
基金资助:
国防测试重点实验室基金资助项目（9140C120402120C1208）。

Abstract

Abstract: Focused on the issue that complex image texture can not be fully expressed by single dictionary in image Super-Resolution (SR) reconstruction, a remote sensing image super-resolution algorithm based on compressive sensing and wavelet theory using multiple dictionaries was proposed. Firstly, the K-Singular Value Decomposition (K-SVD) algorithm was used to establish the different dictionaries in the different frequency bands in wavelet domain. Secondly, the initial solution of SR image was obtained by using global limited condition. Finally, the sparse solution of multiple dictionaries in wavelet domain was implemented using Orthogonal Matching Pursuit (OMP) algorithm. The experimental results show that the proposed algorithm presents the better subjective visual effect compared with the single dictionary based algorithm. The Peak Signal-to-Noise Ratio (PSNR) and the Structural SIMilarity (SSIM) index increase more than 2.8 dB and 0.01 separately. The computation time is reduced as the dictionaries can be used once again.

Key words: remote sensing image, super-resolution, compressive sensing, dictionary learning, wavelet

摘要： 针对单字典表达复杂多样的图像纹理存在一定的局限性的问题，利用压缩感知和小波理论建立了一种多字典遥感图像超分辨算法。首先，对训练图像在小波域的不同频带利用K-奇异值分解（K-SVD）算法建立不同的字典；然后，利用全局限制求取高分辨率图像的初始解；最后，利用正交匹配追踪算法（OMP）对初始解在小波域进行多字典稀疏求解。实验结果表明，相比基于单字典的超分辨重建算法，结果图像的主观视觉效果有很大提高，客观评价指标的峰值信噪比（PSNR）和结构相似度（SSIM）分别提高2.8 dB以上和0.01以上。字典可一次建立重复使用，降低了运算时间。

关键词: 遥感图像, 超分辨, 压缩感知, 字典学习, 小波

CLC Number:

TP751.1

YANG Xuefeng, CHENG Yaoyu, WANG Gao. Super-resolution algorithm for remote sensing images based on compressive sensing in wavelet domain[J]. Journal of Computer Applications, 2017, 37(5): 1430-1433.

杨学峰, 程耀瑜, 王高. 基于小波域压缩感知的遥感图像超分辨算法[J]. 计算机应用, 2017, 37(5): 1430-1433.

References

[1] ZHANG L P, ZHANG H Y, SHEN H F, et al. A super-resolution reconstruction algorithm for surveillance images[J]. Signal Processing, 2010,90(3):848-859.
[2] LI F, JIA X P, DONALD F, et al. Super resolution for remote sensing images based on a universal hidden Markov tree model[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(3):1270-1278.
[3] YUAN Q Q, ZHANG L P, SHEN H F. Regional spatially adaptive total variation super-resolution with spatial information filtering and clustering[J]. IEEE Transactions on Image Processing, 2013,22(6):2327-2342.
[4] LI X, ORCHARD M T. New edge-directed interpolation[J]. IEEE Transactions on Image Processing, 2001, 10(10):1521-1527.
[5] GUO K, YANG X, ZHA H, et al. Multiscale semilocal interpolation with antialiasing[J]. IEEE Transactions on Image Processing, 2012,21(2):615-625.
[6] 杨学峰, 王高, 程耀瑜. 基于径向基函数的多帧图像超分辨重建算法[J]. 计算机应用, 2014,34(1):142-144.(YANG X F, WANG G, CHENG Y Y. Multi-frame image super-resolution reconstruction algorithm with radial basis function neural network[J]. Journal of Computer Applications, 2014, 34(1):142-144.)
[7] 袁其平, 林海杰, 陈志宏,等. 用支持向量回归法实现单帧图像超分辨率重建[J]. 光学精密工程, 2016, 24(9):2302-2309. (YUAN Q P, LIN H J, CHEN Z H, et al. Single image super-resolution reconstruction using support vector regression[J]. Optics and Precision Engineering, 2016, 24(9):2302-2309.)
[8] CHAO D, CHEN C L, HE K, et al. Image super-resolution using deep convolutional networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(2):295-307.
[9] SUN Y C, GU G H, SUI X B, et al. Single image super-resolution using compressive sensing with a redundant dictionary[J]. IEEE Photonics Journal,2015, 7(2):6900411.
[10] YANG S Y, SUN F H, WANG M, et al. Novel super resolution restoration of remote sensing images based on compressive sensing and example patches-aided dictionary learning[C]//Proceedings of the 2011 International Workshop on Multi-Platform/Multi-Sensor Remote Sensing and Mapping. Piscataway, NJ:IEEE,2011:1-6.
[11] 陈伟业, 孙权森. 结合压缩感知与非局部信息的图像超分辨率重建[J]. 计算机应用, 2016, 36(9):2570-2575. (CHEN W Y, SUN Q S. Image super-resolution reconstruction combined with compressed sensing and nonlocal information[J]. Journal of Computer Applications, 2016, 36(9):2570-2575.)
[12] NAVEEN K, PRADEEP N, RAHUL G, et al. Understanding compressive sensing and sparse representation-based super-resolution[J]. IEEE Transactions on Circuits & Systems for Video Technology, 2012, 22(5):778-789.
[13] PAN Z X, YU J, HUANG H J, et al. Super-resolution based on compressive sensing and structural self-similarity for remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(9):4864-4876.
[14] YEGANLI F, NAZZAL M, UNAL M, et al. Image super-resolution via sparse representation over multiple learned dictionaries based on edge sharpness[J]. Signal, Image and Video Processing, 2015, 10(3):535-542.
[15] WU W, YANG X M, LIU K, et al. A new framework for remote sensing image super-resolution:sparse representation-based method by processing dictionaries with multi-type features[J]. Journal of Systems Architecture, 2016, 64:63-75.
[16] SURYANARAYANA G, DHULI R. Image resolution enhancement using wavelet domain transformation and sparse signal representation[C]//Proceedings of the 2nd International Conference on Intelligent Computing, Communication & Convergence. Amsterdam:Elsevier, 2016:311-316.
[17] DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4):1289-1306.
[18] HOLGER R, KARIN S, PIERRE V. Compressed sensing and redundant dictionaries[J]. IEEE Transactions on Information Theory, 2008, 54(5):2210-2219.
[19] EMMANUEL J, MICHAEL B. An introduction to compressive sampling[J]. IEEE Signal Processing Magazine, 2008, 25(2):21-30.
[20] MICHAL A, MICHAEL E, ALFRED B. K-SVD: an algorithm for designing of overcomplete dictionaries for sparse representation[J]. IEEE Transactions on Signal Process, 2006, 54(11):4311-4322.
[21] YANG J Y, PENG Y G, XU W L, et al. Ways to sparse representation: an overview[J]. Science in China Series F: Information Sciences,2009, 52(4):695-703.
[22] PATI Y C, REZAIFAR R, KRISHNAPRAS P S. Orthogonal matching pursuit:recursive function approximation with applications to wavelet decomposition[C]// Proceedings of the 27th Asilomar Conference on Signals, Systems and Computers. Piscataway, NJ: IEEE,1993:40-44. This work is supported by the National Defense Key Laboratory Fund Program of China (9140C120402120C1208).YANG Xuefeng, born in 1976, Ph. D., lecturer. His research interests include image restoration and super-resolution reconstruction, pattern recognition, computer vision.CHENG Yaoyu, born in 1966, Ph. D., professor. His research interests include information acquisition and processing, image processing, measurement and control system. WANG Gao, born in 1973, Ph. D., professor. His research interests include optoelectronic information processing.

Super-resolution algorithm for remote sensing images based on compressive sensing in wavelet domain

基于小波域压缩感知的遥感图像超分辨算法

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

[1]	Mu LI, Yu LUO, Xizheng KE. Human vital signs detection algorithm based on frequency modulated continuous wave radar [J]. Journal of Computer Applications, 2024, 44(6): 1978-1986.
[2]	Zihan LIU, Dengwen ZHOU, Yukai LIU. Image super-resolution network based on global dependency Transformer [J]. Journal of Computer Applications, 2024, 44(5): 1588-1596.
[3]	Lin GUO, Kunhu LIU, Chenyang MA, Youxue LAI, Yingfen XU. Image super-resolution reconstruction based on residual attention network with receptive field expansion [J]. Journal of Computer Applications, 2024, 44(5): 1579-1587.
[4]	Yang LIU, Rong LIU, Ke FANG, Xinyue ZHANG, Guangxu WANG. Video super-resolution reconstruction network based on frame straddling optical flow [J]. Journal of Computer Applications, 2024, 44(4): 1277-1284.
[5]	Xue LI, Guangle YAO, Honghui WANG, Jun LI, Haoran ZHOU, Shaoze YE. Remote sensing image classification based on sample incremental learning [J]. Journal of Computer Applications, 2024, 44(3): 732-736.
[6]	Ning WU, Yangyang LUO, Huajie XU. Semantic segmentation method for remote sensing images based on multi-scale feature fusion [J]. Journal of Computer Applications, 2024, 44(3): 737-744.
[7]	Yuqiu LI, Liping HOU, Jian XUE, Ke LYU, Yong WANG. Remote sensing image recommendation method based on content interpretation [J]. Journal of Computer Applications, 2024, 44(3): 722-731.
[8]	Jingxian ZHOU, Xina LI. UAV detection and recognition based on improved convolutional neural network and radio frequency fingerprint [J]. Journal of Computer Applications, 2024, 44(3): 876-882.
[9]	Jingxin LIU, Wenjing HUANG, Liangsheng XU, Chong HUANG, Jiansheng WU. Unsupervised feature selection model with dictionary learning and sample correlation preservation [J]. Journal of Computer Applications, 2024, 44(12): 3766-3775.
[10]	Ziyi LI, Tingting QU, Qianpeng CHONG, Jindong XU. Remote sensing image segmentation network based on fuzzy multiscale features [J]. Journal of Computer Applications, 2024, 44(11): 3581-3586.
[11]	Lishuo YE, Zhixue HE. Multiscale time series anomaly detection incorporating wavelet decomposition [J]. Journal of Computer Applications, 2024, 44(10): 3300-3306.
[12]	Feng CAO, Xiaoling YANG, Jianbing YI, Jun LI. Contradiction separation super-deduction method and application [J]. Journal of Computer Applications, 2024, 44(10): 3074-3080.
[13]	Hao CHEN, Zhenping XIA, Cheng CHENG, Xing LIN-LI, Bowen ZHANG. Lightweight image super-resolution reconstruction network based on Transformer-CNN [J]. Journal of Computer Applications, 2024, 44(1): 292-299.
[14]	Kunting LU, Rongrong FEI, Xuande ZHANG. Remote sensing image pansharpening by convolutional neural network [J]. Journal of Computer Applications, 2023, 43(9): 2963-2969.
[15]	Min LIANG, Jiayi LIU, Jie LI. Image super-resolution reconstruction method based on iterative feedback and attention mechanism [J]. Journal of Computer Applications, 2023, 43(7): 2280-2287.