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Enhanced principle component method for fringe removal in cold atom images

机译:冷原子图像中的边缘移除的增强原理成分方法

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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. (C) 2020 Optical Society of America
机译:用于冷原子的许多强大的成像技术基于通过将通过原子云的光束图像与在没有原子的类似条件下拍摄的参考图像中的光束图像来确定光学密度。 在实践中,光束轮廓通常包含干涉条纹,其相位在相机曝光之间不稳定。 为了减少计算光学密度的这些条纹的误差,通常采用基于原理分量分析(PCA)的算法。 然而,PCA是通用的,而不是针对干扰条纹的特定情况量身定制。 在这里,我们展示了一种算法,其利用干涉条纹的傅里叶空间结构,以进一步减小光密度的残余条纹签名。 (c)2020美国光学学会

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