Measure and model a 3-D space-variant PSF for fluorescence microscopy image deblurring
Measure and model a 3-D space-variant PSF for fluorescence microscopy image deblurring
复制标题
测量和建模用于荧光显微镜图像去模糊的 3-D 空间变化 PSF
DOI：
10.1364/oe.26.014375
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发表时间：
2018-05-28
期刊：
影响因子：
3.8
通讯作者：
Li, Yang
中科院分区：
文献类型：
--
作者：
Chen, Yemeng;Chen, Mengmeng;Li, Yang
传统的去卷积方法假设显微镜系统是空间不变的，引入了相当大的误差。我们开发了一种方法来更精确地估计空间变化的点扩散函数从稀疏测量。为此，开发了一种空间变版本的去模糊算法，并将其与全变分正则化相结合。仿真和真实的数据的验证表明，我们的PSF模型是更准确的分段不变模型和混合模型。与基于正交基分解的PSF模型相比，该模型的性能也有了很大的提高。我们还评估了所提出的去模糊算法。我们的新的去模糊算法表现出显着更好的信噪比和更高的图像质量比传统的空间不变算法。(C)2018年美国光学学会根据OSA开放获取出版协议的条款
Conventional deconvolution methods assume that the microscopy system is spatially invariant, introducing considerable errors. We developed a method to more precisely estimate space-variant point-spread functions from sparse measurements. To this end, a space-variant version of deblurring algorithm was developed and combined with a total-variation regularization. Validation with both simulation and real data showed that our PSF model is more accurate than the piecewise-invariant model and the blending model. Comparing with the orthogonal basis decomposition based PSF model, our proposed model also performed with a considerable improvement. We also evaluated the proposed deblurring algorithm. Our newdeblurring algorithm showed a significantly better signal-to-noise ratio and higher image quality than those of the conventional space-invariant algorithm. (C) 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement