Latychevskaia Tatiana
Appl Opt. 2021 Feb 10;60(5):1304-1314. doi: 10.1364/AO.412736.
Methods of three-dimensional deconvolution (3DD) or volumetric deconvolution of optical complex-valued wavefronts diffracted by 3D samples with the 3D point spread function are presented. Particularly, the quantitative correctness of the recovered 3D sample distributions is addressed. Samples consisting of point-like objects can be retrieved from their 3D diffracted wavefronts with non-iterative (Wiener filter) 3DD. Continuous extended samples, including complex-valued (phase) samples, can be retrieved with iterative (Gold and Richardson-Lucy) 3DD algorithms. It is shown that quantitatively correct 3D sample distribution can be recovered only with iterative 3DD, and with the optimal protocols provided. It is demonstrated that 3DD can improve the lateral resolution to the resolution limit, and the axial resolution can be at least four times better than the resolution limit. The presented 3DD methods of complex-valued optical fields can be applied for 3D optical imaging and holography.
本文介绍了利用三维点扩散函数对三维样本衍射的光学复值波前进行三维去卷积(3DD)或体积去卷积的方法。特别地,讨论了恢复的三维样本分布的定量正确性。由点状物体组成的样本可以通过非迭代(维纳滤波器)3DD从其三维衍射波前中检索出来。连续扩展样本,包括复值(相位)样本,可以通过迭代(戈德和理查森-露西)3DD算法检索出来。结果表明,只有通过迭代3DD并采用所提供的最优协议,才能恢复定量正确的三维样本分布。结果表明,3DD可以将横向分辨率提高到分辨率极限,轴向分辨率至少比分辨率极限好四倍。所提出的复值光场3DD方法可应用于三维光学成像和全息术。