Zhao Wanli, Lu Jing, Ma Jun, Yuan Caojin, Chang Chenliang, Zhu Rihong
Opt Lett. 2024 Mar 1;49(5):1385-1388. doi: 10.1364/OL.509688.
The Rayleigh-Sommerfeld diffraction integral (RSD) is a rigorous solution that precisely satisfies both Maxwell's equations and Helmholtz's equations. It seamlessly integrates Huygens' principle, providing an accurate description of the coherent light propagation within the entire diffraction field. Therefore, the rapid and precise computation of the RSD is crucial for light transport simulation and optical technology applications based on it. However, the current FFT-based Rayleigh-Sommerfeld integral convolution algorithm (CRSD) exhibits poor performance in the near field, thereby limiting its applicability and impeding further development across various fields. The present study proposes, to our knowledge, a novel approach to enhance the accuracy of the Rayleigh-Sommerfeld convolution algorithm by employing independent sampling techniques in both spatial and frequency domains. The crux of this methodology involves segregating the spatial and frequency domains, followed by autonomous sampling within each domain. The proposed method significantly enhances the accuracy of RSD during the short distance while ensuring computational efficiency.
瑞利 - 索末菲衍射积分（RSD）是一种精确解，它精确满足麦克斯韦方程组和亥姆霍兹方程。它无缝整合了惠更斯原理，对整个衍射场内的相干光传播提供了准确描述。因此，快速精确地计算RSD对于基于它的光传输模拟和光学技术应用至关重要。然而，当前基于快速傅里叶变换的瑞利 - 索末菲积分卷积算法（CRSD）在近场表现不佳，从而限制了其适用性，并阻碍了其在各个领域的进一步发展。据我们所知，本研究提出了一种通过在空间和频域采用独立采样技术来提高瑞利 - 索末菲卷积算法精度的新方法。该方法的关键在于将空间和频域分离，然后在每个域内进行自主采样。所提出的方法在确保计算效率的同时，显著提高了短距离内RSD的精度。
