Compressive imaging based on Tetrolet-domain uHMT structured sparse prior and Turbo equalization

Xing YANG

doi:10.3788/OPE.20182607.1766

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Compressive imaging based on Tetrolet-domain uHMT structured sparse prior and Turbo equalization

Information Sciences | 更新时间：2020-08-13

- Compressive imaging based on Tetrolet-domain uHMT structured sparse prior and Turbo equalization
- Optics and Precision Engineering Vol. 26, Issue 7, Pages: 1766-1773(2018)
- 作者机构：
  
  国防科技大学电子对抗学院脉冲功率激光技术国家重点实验室, 安徽合肥 200037
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20182607.1766
  CLC： TN911.74
- Received：29 December 2017，
  
  Accepted：24 January 2018，
  
  Published：25 July 2018
- 稿件说明：
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Xing YANG. Compressive imaging based on Tetrolet-domain uHMT structured sparse prior and Turbo equalization[J]. Optics and precision engineering, 2018, 26(7): 1766-1773.
DOI：

Xing YANG. Compressive imaging based on Tetrolet-domain uHMT structured sparse prior and Turbo equalization[J]. Optics and precision engineering, 2018, 26(7): 1766-1773. DOI： 10.3788/OPE.20182607.1766.

摘要

基于Tetrolet变换系数的尺度间传递特性与按指数衰减特性，本文构建了一种Tetrolet域通用隐马尔科夫树结构稀疏先验模型，把Tetrolet变换系数的统计分布表示成二值高斯混合形式作为先验信息，并采用因子图方法估计后验状态概率。为了解决在有环路的因子图中消息不能稳定收敛的问题，利用Turbo均衡方法把压缩采样和结构先验部分分割成两个子图，分别进行状态估计并相互交换消息。最后依据最小均方误差准则估计得到重构图像，对128×128的测试图像重构的归一化均方误差可达-20.97 dB，运行时间为45.24 s。实验结果表明该算法在重构质量和运行速度上优于小波域隐马尔科夫树模型的各类算法。

Abstract

Based on the persistence and exponential decay across scales of Tetrolet coefficients

the Tetrolet-domain universal hidden Markov tree structured sparse prior model was established for compressive imaging. In this model

the statistic distribution of Tetrolet coefficients was presented as the prior with the Gaussian-mixture form

and then

the posterior probability density function (PDF) was estimated by using the factor graph method. In order to solve the problem that the messages passing through the loop factor graph cannot reach stable convergence

the Turbo equalization method was exploited to decouple the factor graph into two parts for estimating the states of compressive sampling and the structured sparse model. Then

the exchange of messages was performed mutually in the two sub-graphs until reaching convergence. Finally

the image was estimated based on the minimum mean-squared error criterion. The normalized mean-squared error of reconstructing the testing image with size 128×128 was -20.97 dB

and the run-time was 45.24 s. Experimental results demonstrate that the proposed algorithm outperforms the algorithms based on the wavelet-domain hidden Markov tree model in terms of reconstruction quality and speed.

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