迭代去噪收缩阈值算法重构压缩全息

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

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.

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.