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

• 学术论文 • 上一篇    下一篇

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   

  1. 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   

  1. 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
    • Supported by:
    • National Natural Science Foundation of China (No.61071200, No.60772079); Natural Science Foundation of Hebei Province,  China (No.F2010001294)

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

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