Chen Yemeng, Chen Mengmeng, Zhu Li, Wu Jane Y, Du Sidan, Li Yang
Opt Express. 2018 May 28;26(11):14375-14391. doi: 10.1364/OE.26.014375.
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 new deblurring algorithm showed a significantly better signal-to-noise ratio and higher image quality than those of the conventional space-invariant algorithm.
传统的反卷积方法假定显微镜系统在空间上是不变的，这会引入相当大的误差。我们开发了一种方法，可从稀疏测量中更精确地估计空间变化的点扩散函数。为此，我们开发了一种空间变化版本的去模糊算法，并将其与全变差正则化相结合。通过模拟和真实数据进行的验证表明，我们的点扩散函数（PSF）模型比分段不变模型和混合模型更准确。与基于正交基分解的PSF模型相比，我们提出的模型也有显著改进。我们还评估了所提出的去模糊算法。我们的新去模糊算法显示出比传统的空间不变算法显著更好的信噪比和更高的图像质量。
