Bregman-EM-TV Methods with Application to Optical Nanoscopy
Bregman-EM-TV Methods with Application to Optical Nanoscopy
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DOI：
10.1007/978-3-642-02256-2_20
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发表时间：
2009-05
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影响因子：
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通讯作者：
C. Brune;Alex Sawatzky;M. Burger
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文献类型：
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作者：
C. Brune;Alex Sawatzky;M. Burger
纳米成像中的测量受到关于不同点扩散函数（PSF）的模糊效应的影响。一些装置甚至具有局部依赖于相移的PSF。此外，原始数据受到激光采样和荧光显微镜中“光子计数”产生的泊松噪声的影响。在这些应用中，标准重建方法（EM，滤波反投影）提供不令人满意的和嘈杂的结果。从MAP似然估计的统计建模开始，我们将联合收割机的迭代EM算法与TV正则化技术相结合，以有效地利用先验信息。典型地，基于TV的方法递送遭受对比度降低的重建的卡通图像。我们提出了一个扩展的EM-电视，基于Bregman迭代和逆尺度空间方法，以获得改善的成像结果，同时对比度增强。我们通过合成和实验生物数据来说明我们的技术。
Measurements in nanoscopic imaging suffer from blurring effects concerning different point spread functions (PSF). Some apparatus even have PSFs that are locally dependent on phase shifts. Additionally, raw data are affected by Poisson noise resulting from laser sampling and ”photon counts” in fluorescence microscopy. In these applications standard reconstruction methods (EM, filtered backprojection) deliver unsatisfactory and noisy results. Starting from a statistical modeling in terms of a MAP likelihood estimation we combine the iterative EM algorithm with TV regularization techniques to make an efficient use of a-priori information. Typically, TV-based methods deliver reconstructed cartoon-images suffering from contrast reduction. We propose an extension to EM-TV, based on Bregman iterations and inverse scale space methods, in order to obtain improved imaging results by simultaneous contrast enhancement. We illustrate our techniques by synthetic and experimental biological data.