Sampling and processing for compressive holography [Invited]
Sampling and processing for compressive holography [Invited]
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DOI：
10.1364/ao.50.000h75
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发表时间：
2011-12-01
期刊：
影响因子：
1.9
通讯作者：
Brady, David J.
中科院分区：
文献类型：
--
作者：
Lim, Sehoon;Marks, Daniel L.;Brady, David J.
压缩全息术将稀疏先验应用于通过数字全息术获取的数据，以从稍大数量的离散测量推断少量的对象特征或基向量。压缩全息术可应用于从二维（2D）测量重构三维（3D）图像或从稀疏孔径重构2D图像。本文是一个教程，涵盖实际的压缩全息程序，包括场传播，参考滤波，和压缩全息逆问题。我们提出的例子3D断层扫描从2D全息图，2D图像重建从稀疏孔径，和扩散对象估计从不同的斑点实现。(C)2011年美国光学学会
Compressive holography applies sparsity priors to data acquired by digital holography to infer a small number of object features or basis vectors from a slightly larger number of discrete measurements. Compressive holography may be applied to reconstruct three-dimensional (3D) images from two-dimensional (2D) measurements or to reconstruct 2D images from sparse apertures. This paper is a tutorial covering practical compressive holography procedures, including field propagation, reference filtering, and inverse problems in compressive holography. We present as examples 3D tomography from a 2D hologram, 2D image reconstruction from a sparse aperture, and diffuse object estimation from diverse speckle realizations. (C) 2011 Optical Society of America