相干光学和全息术中的三维体积反卷积
Three-dimensional volumetric deconvolution in coherent optics and holography
摘要：
本文介绍了用三维点扩散函数对三维样品衍射的光学复值波前进行三维反褶积或体积反褶积的方法。特别是，恢复的3D样本分布的定量正确性得到解决。由点状物体组成的样本可以用非迭代（维纳滤波器）3DD从其3D衍射波前中检索。连续扩展样本，包括复值（相位）样本，可以用迭代（Gold和Richardson-Lucy）3DD算法检索。结果表明，定量正确的3D样本分布只能恢复与迭代3DD，并提供了最佳的协议。结果表明，3DD可以将横向分辨率提高到分辨率极限，轴向分辨率至少可以提高到分辨率极限的4倍。本文提出的复值光场三维离散方法可用于三维光学成像和全息术。
英文摘要：
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 only recovered 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
