Boulanger Jérôme, Pustelnik Nelly, Condat Laurent, Sengmanivong Lucie, Piolot Tristan
CNRS UMR144, F-75248 Paris, France.
Institut Curie, F-75248 Paris, France.
Inverse Probl. 2018 Sep;34(9):095004. doi: 10.1088/1361-6420/aaccca. Epub 2018 Jul 12.
In this paper, we propose a new approach for structured illumination microscopy image reconstruction. We first introduce the principles of this imaging modality and describe the forward model. We then propose the minimization of nonsmooth convex objective functions for the recovery of the unknown image. In this context, we investigate two data-fitting terms for Poisson-Gaussian noise and introduce a new patch-based regularization method. This approach is tested against other regularization approaches on a realistic benchmark. Finally, we perform some test experiments on images acquired on two different microscopes.
在本文中，我们提出了一种用于结构照明显微镜图像重建的新方法。我们首先介绍这种成像方式的原理并描述正向模型。然后，我们提出通过最小化非光滑凸目标函数来恢复未知图像。在此背景下，我们研究了针对泊松 - 高斯噪声的两个数据拟合项，并引入了一种新的基于补丁的正则化方法。该方法在一个真实的基准上与其他正则化方法进行了测试。最后，我们对在两台不同显微镜上采集的图像进行了一些测试实验。
