Many powerful imaging techniques for cold atoms are based on determining the optical density by comparing a beam image having passed through the atom cloud to a reference image taken under similar conditions with no atoms. In practice, the beam profile typically contains interference fringes whose phase is not stable between camera exposures. To reduce the error of these fringes in the computed optical density, an algorithm based on principle component analysis (PCA) is often employed. However, PCA is general purpose and not tailored to the specific case of interference fringes. Here we demonstrate an algorithm that takes advantage of the Fourier space structure of interference fringes to further reduce the residual fringe signatures in the optical density.

中文翻译：

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增强主成分法去除冷原子图像中的条纹

许多强大的冷原子成像技术都基于通过将穿过原子云的光束图像与在没有原子的相似条件下拍摄的参考图像进行比较来确定光密度。实际上，光束轮廓通常包含干涉条纹，其相位在相机两次曝光之间不稳定。为了减少这些条纹在计算的光密度中的误差，通常采用基于主成分分析（PCA）的算法。但是，PCA是通用的，并不适合于干涉条纹的特定情况。在这里，我们演示了一种算法，该算法利用干涉条纹的傅立叶空间结构来进一步减少光密度中的残余条纹特征。