基于图像块整体稀疏性与流形投影的压缩成像

doi:10.3969/j.issn.0372-2112.2013.05.012

电子学报 ›› 2013, Vol. 41 ›› Issue (5): 905-911.DOI: 10.3969/j.issn.0372-2112.2013.05.012

基于图像块整体稀疏性与流形投影的压缩成像

练秋生, 张红卫, 陈书贞

燕山大学信息科学与工程学院,河北秦皇岛066004

收稿日期:2012-05-04 修回日期:2012-11-18 出版日期:2013-05-25
- 作者简介:
- 练秋生男,1969年8月生于江西遂川.博士,现为燕山大学信息科学与工程学院教授,博士生导师.主要研究方向为图像处理,稀疏表示,压缩感知及多尺度几何分析等. E-mail:lianqs@ysu.edu.cn
- 基金资助:
- 国家自然科学基金 (No.61071200,No.60772079); 河北省自然科学基金 (No.F2010001294)

Compressive Imaging Algorithm Based on the Integrated Sparse Property of Image Patches and Manifold Projection

LIAN Qiu-sheng, ZHANG Hong-wei, CHEN Shu-zhen

Institute of Information Science and Technology,Yanshan University,Qinhuangdao,Hebei 066004,China

Received:2012-05-04 Revised:2012-11-18 Online:2013-05-25 Published:2013-05-25

摘要/Abstract

摘要： 如何利用自然图像本身固有的先验知识来提高重构图像质量是压缩成像系统的一个关键问题.本文在压缩成像系统中融合图像块整体稀疏性与流形特性,提出了一种高质量压缩成像算法.在该算法中,图像块由字典稀疏表示,同时还可由一组与图像块位于同一低维流形上的近邻点线性逼近,从而使稀疏重建信号分布在原始信号所处的流形附近.另外本文充分利用了图像中任意位置处图像块的稀疏性先验知识,使得压缩成像算法在低采样率下能重构出质量较高的图像.

关键词: 压缩传感, 压缩成像, 稀疏表示, 流形投影

Abstract: How to improve the reconstructed image quality using inherent prior knowledge of natural image is still a crucial issue in compressive imaging.In this paper,an efficient compressive imaging algorithm is proposed,which combines the sparse property of the entire image patches and the manifold property.In the algorithm,image patches are represented sparsely by a dictionary and approximated simultaneously by a set of neighbor points.These neighbor points are embedded in the same manifold with the original signal,ensuring that the recovered signal using sparse approximation is close with its manifold.In addition,the proposed compressive imaging algorithm which makes full use of the sparse properties of patches in any position of the image can reconstruct the high quality image in low sampling rate.

Key words: compressed sensing, compressive imaging, sparse representation, manifold projection

中图分类号:

TN911.73

练秋生, 张红卫, 陈书贞. 基于图像块整体稀疏性与流形投影的压缩成像[J]. 电子学报, 2013, 41(5): 905-911.

LIAN Qiu-sheng, ZHANG Hong-wei, CHEN Shu-zhen. Compressive Imaging Algorithm Based on the Integrated Sparse Property of Image Patches and Manifold Projection[J]. Acta Electronica Sinica, 2013, 41(5): 905-911.

参考文献

[1] E J Candes,M B Wakin.An introduction to compressive sampling[J].IEEE Signal Processing Magazine,2008,25(2):21-30.
[2] D L Donoho.Compressed sensing[J].IEEE Transactions on Information Theory,2006,52(4):1289-1306.
[3] 石光明,刘丹华,高大化,刘哲,林杰,王良君.压缩感知理论及其研究进展[J].电子学报,2009,37(5):1070-1081. Shi Guang-ming,Liu Dan-hua,Gao Da-hua,Liu Zhe,Lin Jie,Wang Liang-jun.Advances in theory and application of compressed sensing[J].Acta Electronica Sinica,2009,37(5):1071-1081.(in Chinese)
[4] 杨海蓉,张成,丁大为,韦穗.压缩传感理论与重构算法[J].电子学报,2011,39(1):142-148. Yang Hai-rong,Zhang Cheng,Ding Da-wei,Wei Sui.The theory of compressed sensing and reconstruction algorithm[J].Acta Electronica Sinica 2011,39(1),142-148.(in Chinese)
[5] E J Candes,J Romberg,T Tao.Robust uncertainty principles:exact signal reconstruction from highly incomplete frequency information[J].IEEE Transactions on Information Theory,2006,52(2):489-509.
[6] M Aharon,M Elad,A Bruckstein.The K-SVD:An algorithm for designing of overcomplete dictionaries for sparse representation[J].IEEE Transactions on Signal Processing,2006,54(11):4311-4322.
[7] K Labusch,E Barth,T Martinetz.Sparse coding neural gas:learning of overcomplete data representations[J].Neurocomputing,2009,7(72):1547-1555.
[8] S Mun,J E Fowler.Block compressed sensing of images using directional transforms .IEEE International Conference on Image Processing .Cairo,Egypt,2009.3021-3024.
[9] G Carlsson,T Ishkhanov,V D Silva,A Zomorodian.On the local behavior of spaces of natural images[J].International Journal of Computer Vision,2008,76(1):1-12.
[10] 练秋生,张红卫,陈书贞,李林.融合图像块低维流形特性与解析轮廓波稀疏性的压缩成像算法[J].电子与信息学报,2012,34(1):207-212. Lian Qiu-sheng,Zhang Hong-wei,Chen Shu-zhen,Li Lin.Compressive imaging algorithm combined the low dimensional manifold property of image patch with the sparse representation of analytic contourlet[J].Journal of Electronics & Information Technology,2012,34(1):207-212.(in Chinese)
[11] K N Ramamurthy,J J Thiagarajan,A Spanias.Improved sparse coding using manifold projections .IEEE International Conference on Image Processing .Brussels,Belgium,2011.1237-1240.
[12] 罗四维,等著.视觉感知系统信息处理理论[M].北京:电子工业出版社,2006:3-175. Luo Si-wei,et al.Information processing theory based on visual perception system[M].Beijing:Electronics Industry Press,2006.3-175.(in Chinese)
[13] J B Tenenbaum,V de Silva,J C Langford.A global geometric framework for nonlinear dimensionality reduction[J].Science,2000,290(5500):2319-2323.
[14] S T Roweis,L K Saul.Nonlinear dimensionality reduction by locally linear embedding[J].Science,2000,290(5500):2323-2326.
[15] M Belkin,P Niyogi.Laplacian eigenmaps for dimensionality reduction and data representation[J].Neural Computation,2003,15(6):1373-1396.
[16] G Peyre.Manifold models for signals and images[J].Computer Vision and Image Understanding,2009,113(2):249-260.
[17] M Grant,S Boyd.CVX:matlab software for disciplined convex programming,version 1.21 .http://cvxr.com/cvx,2011.
[18] J M Duarte-Carvajalino,G Sapiro.Learning to sense sparse signals:simultaneous sensing matrix and sparsifying dictionary optimization[J].IEEE Transactions on Image Processing,2009,18(7):1395-1408.
[19] J J Thiagarajan,K N Ramamurthy,A spanias.Multilevel dictionary learning for sparse representation of images . Digital Signal Processing Workshop and IEEE Signal Processing Education Workshop .Sedona,AZ,2011.271-276.

基于图像块整体稀疏性与流形投影的压缩成像

Compressive Imaging Algorithm Based on the Integrated Sparse Property of Image Patches and Manifold Projection

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	冯西安, 寇思玮, 谭伟杰, 毕杨. 水声信号处理中的稀疏表示理论及应用[J]. 电子学报, 2021, 49(9): 1840-1851.
[2]	禤韵怡, 杨春玲. 基于帧间组稀疏的两阶段递归增强视频压缩感知重构网络[J]. 电子学报, 2021, 49(3): 435-442.
[3]	夏楠, 高紫俊, 李博, 王珏. 一种基于循环稀疏表示的同频带干扰抑制及发射源定位方法[J]. 电子学报, 2021, 49(1): 8-13.
[4]	郑学炜, 杨春玲, 禤韵怡. CVS中基于视频运动特征的多假设-双稀疏重构算法[J]. 电子学报, 2020, 48(2): 249-257.
[5]	脱婷, 马慧芳, 李志欣, 赵卫中. 熵权约束稀疏表示的短文本分类算法[J]. 电子学报, 2020, 48(11): 2131-2137.
[6]	周浦城, 张杰, 薛模根, 尹璋堃. 基于卷积分析稀疏表示和相位一致性的低照度图像增强[J]. 电子学报, 2020, 48(1): 180-188.
[7]	田文飚, 芮国胜, 董道广, 康健. 基于盲自适应KLT的蒸发波导压缩感知方法[J]. 电子学报, 2018, 46(9): 2068-2074.
[8]	王鹏, 邱天爽, 金芳晓, 夏楠, 李景春. 脉冲噪声下基于稀疏表示的韧性DOA估计方法[J]. 电子学报, 2018, 46(7): 1537-1544.
[9]	代晓婷, 龚敬, 聂生东. 基于结构联合字典的肺部LDCT图像降噪[J]. 电子学报, 2018, 46(6): 1445-1453.
[10]	和志杰, 杨春玲, 汤瑞东. 视频压缩感知中基于结构相似的帧间组稀疏表示重构算法研究[J]. 电子学报, 2018, 46(3): 544-553.
[11]	李鑫, 孙晋, 肖甫. 基于弹性网稀疏表示的芯片参数成品率估算方法[J]. 电子学报, 2017, 45(12): 2917-2924.
[12]	陈小龙, 关键, 于晓涵, 何友. 雷达动目标短时稀疏分数阶傅里叶变换域检测方法[J]. 电子学报, 2017, 45(12): 3030-3036.
[13]	秦晓燕, 袁广林, 李从利, 张旭. 一种快速鲁棒的视频序列运动目标检测方法[J]. 电子学报, 2017, 45(10): 2355-2361.
[14]	唐意东, 黄树彩, 薛爱军. 面向目标检测基于稀疏表示的波段选择方法[J]. 电子学报, 2017, 45(10): 2368-2374.
[15]	詹曙, 方琪, 杨福猛, 常乐乐, 闫婷. 基于耦合特征空间下改进字典学习的图像超分辨率重建[J]. 电子学报, 2016, 44(5): 1189-1195.