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

电子学报 ›› 2013, Vol. 41 ›› Issue (5) : 905-911.

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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

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
作者信息 +

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

  • LIAN Qiu-sheng, ZHANG Hong-wei, CHEN Shu-zhen
Author information +
文章历史 +

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.

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.

关键词

压缩传感 / 压缩成像 / 稀疏表示 / 流形投影

Key words

compressed sensing / compressive imaging / sparse representation / manifold projection

引用本文

导出引用
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. https://doi.org/10.3969/j.issn.0372-2112.2013.05.012
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. https://doi.org/10.3969/j.issn.0372-2112.2013.05.012
中图分类号: TN911.73   

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基金

国家自然科学基金 (No.61071200,No.60772079); 河北省自然科学基金 (No.F2010001294)
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