A Compressed Sensing Algorithm of Images with Homogenized Sparse Representation

doi:10.7652/xjtuxb201902018

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A Compressed Sensing Algorithm of Images with Homogenized Sparse Representation

更新时间：2025-07-09

- A Compressed Sensing Algorithm of Images with Homogenized Sparse Representation
- Vol. 53, Issue 2, Pages: 136-141(2019)
- 作者机构：
  
  天津大学微电子学院,天津,300072
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb201902018
  CLC： TP391.41
- Online First：10 February 2019，
  
  Published：2019
- 稿件说明：
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A Compressed Sensing Algorithm of Images with Homogenized Sparse Representation[J]. 2019, 53(2): 136-141.
DOI：

A Compressed Sensing Algorithm of Images with Homogenized Sparse Representation[J]. 2019, 53(2): 136-141. DOI： 10.7652/xjtuxb201902018.

摘要

针对基于Reed-Solomon编码的压缩感知(RSCS)算法在采样过程中遇到的向量稀疏度阈值过大的问题

提出了一种均匀化稀疏表示的RSCS(H-RSCS)算法。首先

对待观测图像做多级离散小波变换(DWT)得到稀疏矩阵

然后按照其子带频率的高低顺序

将每个子带的系数重新按行排布成一个行数值固定的矩阵

矩阵中每一列数据组成一个新的待观测向量

最后采用奇偶校验矩阵对上述均匀化的稀疏矩阵进行观测

并通过译码算法实现图像重构。仿真实验结果表明:与4种经典的贪婪追踪类算法相比

所提出的H-RSCS算法对图像的重构效果更好

实用性更强; 当采样率为50%时

H-RSCS算法将重构图像的峰值信噪比提高了约9.5 dB

比正交匹配追踪算法多提高了约5.1 dB。

Abstract

A novel homogenized RSCS(H-RSCS)algorithm is proposed to deal with the problem of large vector sparseness threshold that exists in compressed sensing algorithm based on Reed-Solomon coding(RSCS). A multi-level discrete wavelet transform is applied to an observed image to obtain a sparse matrix. Then coefficients of each sub-band are rearranged into a matrix with fixed row values according to the order of the sub-band frequencies

and data in each column constitute a new vector to be observed. Finally

the above-mentioned homogenized sparse matrix is observed by the parity checking matrix

and image reconstruction is realized by the decoding algorithm. A comparison with the classic greedy tracking algorithm shows that the proposed H-RSCS algorithm obtains a more accurate reconstructed image. Experimental results show that when the sampling rate is 0.5

the H-RSCS algorithm increases the ratio of peak signal to noise of the reconstructed image by about 9.5 dB

which is 5.1 dB higher than that of the orthogonal matching pursuit algorithm.

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references

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