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Random Illumination Microscopy from Variance Images

机译:来自variance图像的随机照明显微镜

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We propose a reconstruction algorithm called algoRIM for super-resolution fluorescence microscopy, based on speckle illuminations and image variance matching. Superresolution with a factor two or close can be achieved under realistic conditions in terms of number of images and signal to noise ratio. Here, our key result is an approximation of the statistical variance equation, leading to a drastic reduction of the computational complexity. Moreover, we demonstrate that the unmodulated out-of-focus light does not contribute to the data variance, and that the statistical component due to noise can be estimated and removed in an unsupervised way, which is a crucial contribution to the practical robustness of algoRIM.
机译:我们提出了一种称为超分辨率荧光显微镜的算法的重建算法,基于散斑照明和图像方差匹配。在图像数量和信噪比中,可以在现实条件下实现具有因子的超级度或关闭。这里,我们的关键结果是统计方案方程的近似,导致计算复杂性的急剧降低。此外,我们证明未调节的焦点光不会有助于数据方差,并且可以以无监督的方式估计和去除由于噪声引起的统计分量,这是对allim的实际稳健性的关键贡献。

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