上海交通大学学报

上海交通大学学报（自然版） ›› 2017, Vol. 51 ›› Issue (12): 1435-1442.doi: 10.16183/j.cnki.jsjtu.2017.12.005

• 学报（中文） • 上一篇    下一篇

迭代去噪收缩阈值算法重构压缩全息

白彩娟1，刘静1，蒋晓瑜2，张国贤1，黄开宇1   

  1. 1. 西安交通大学 电子与信息工程学院， 西安 710049； 2. 中国人民解放军装甲兵工程学院 信息工程系， 北京 100072
  • 出版日期:2017-11-30 发布日期:2017-11-30
  • 基金资助:
    国家自然科学基金项目（61573276），国家重点基础研究发展规划（973）项目(2013CB329405)，国家自然科学基金创新研究群体科学基金项目（61221063）

The Reconstruction of Digital Holography Based on Iterative De-Noising Shrinkage-Thresholding Algorithm

BAI Caijuan1,LIU Jing1,JIANG Xiaoyu2,ZHANG Guoxian1,HUANG Kaiyu1   

  1. 1. School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China; 2. Department of Information Engineering, Academy of Armored Force Engineering, Beijing 100072, China
  • Online:2017-11-30 Published:2017-11-30

摘要： 为了解决全息图像数据在传输过程中占用大量内存并在一定程度上增加设计成本的问题，在数字全息成像技术中，应用压缩感知理论，提出了一种基于迭代去噪收缩阈值算法(IDNST)的数字全息重构方法.IDNST算法引入了去噪迭代因子和正则化收缩因子，利用前2次迭代的值、不断更新的迭代参数以及不断收缩的正则化参数来获得新的迭代值，加快了收敛速度，提高了全息图像的重构精度.仿真结果表明，所提出方法能够高概率地恢复出原始图像.

关键词: 压缩感知, 数字全息, 全息图的稀疏表示, 观测矩阵, 迭代去噪收缩阈值算法

Abstract: A novel algorithm, namely iterative de-noising shrinkage-thresholding (IDNST) algorithm, is presented to reconstruct the original image from digital holography in a compressed sensing framework. The proposed algorithm can reduce the computational complexity in classical digital holography process, as well as the data in transmission. The proposed algorithm adopts two new factors, i.e., the de-noising iteration factor and the shrinkage factor of regularization. Furthermore, the proposed algorithm obtains a new iterative value using the previously updated iterative values, the iteration factor and the shrinking regularization parameter. This improves the convergence speed and the reconstruction accuracy. Simulation results show that the original image can be reconstructed from the digital hologram perfectly with high probability by the IDNST algorithm.

Key words: compressed sensing, digital holography, sparse representation of hologram, measurement matrix, iterative de-noising shrinkage-thresholding algorithm (IDNST)

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