相位成像的高效高斯推理算法

Jingshan Zhang, J. Dauwels, M. A. Vázquez, L. Waller
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引用次数: 7

摘要

本文提出了一种新的高效算法,可以从不同离焦距离下拍摄的一系列强度图像中推断出复杂光场的相位。非线性观测模型用线性模型近似。在傅里叶域中通过迭代卡尔曼平滑来推断复光场:卡尔曼递归的前向扫描和后向扫描交替进行,并且在每次扫描中对近似线性模型进行改进。通过限制迭代次数,可以在准确性与复杂性之间进行权衡。本文算法每次迭代的复杂度为N logN的数量级,其中N为每张图像的像素数。相比之下,现有相位推断算法的复杂度随N3的增加而增加,所需的存储空间随N2的增加而增加。所提出的算法可以实现从噪声强度图像中实时估计光场。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Efficient Gaussian inference algorithms for phase imaging
Novel efficient algorithms are developed to infer the phase of a complex optical field from a sequence of intensity images taken at different defocus distances. The non-linear observation model is approximated by a linear model. The complex optical field is inferred by iterative Kalman smoothing in the Fourier domain: forward and backward sweeps of Kalman recursions are alternated, and in each such sweep, the approximate linear model is refined. By limiting the number of iterations, one can trade off accuracy vs. complexity. The complexity of each iteration in the proposed algorithm is in the order of N logN, where N is the number of pixels per image. The storage required scales linearly with N. In contrast, the complexity of existing phase inference algorithms scales with N3 and the required storage with N2. The proposed algorithms may enable real-time estimation of optical fields from noisy intensity images.
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