PARAMETRIC BLIND DECONVOLUTION OF MICROSCOPIC IMAGES

描述：（来自摘要）计算光学切片显微镜 （COSM）作为一种替代方案和补充方案， 共焦扫描显微镜（CSM）和双光荧光激发 显微镜（TPFEM）。COSM算法从宽视场中去除失焦光 显微镜图像，还可以提高CSM和TPFEM的分辨率。的 算法通常需要图像形成过程的知识 由点扩散函数（PSF）表示， 光在现代扫描显微镜中， PSF是最好的。在这些情况下，有必要估计PSF 和样品荧光分布的图像; 这种方法称为盲解卷积（BD）。我们发明了一种方法 解卷积，其中PSF被约束为遵循数学模型 这取决于少量未知参数。参数BD（PBD） 方法确定这些参数与样本函数。我们有 发现了令人鼓舞的结果，但PBD仍然是计算密集型的。 本研究的目的是使这种方法在日常使用中切实可行 并将其应用扩展到CSM和TOFEMS。该方法将得到优化 并对其能力和局限性进行彻底评估，以便能够提供 指导其使用。此外，该方法将被纳入一个 反卷积软件包。

DESCRIPTION: (From the Abstract) Computational optical sectioning microscopy (COSM) is gaining acceptance as an alternative as well as a complement to confocal scanning microscopes (CSM) and two-photo fluorescence excitation microscopes (TPFEM). COSM algorithms remove out-of-focus light from wide-field microscope images and can also improve the resolution in CSMs and TPFEMs. The algorithms require knowledge of the process of image formation usually represented by the point-spread function (PSF), the image of a point source of light. In modern scanning microscopes, the measurement of computation of the PSF is difficult at best. In these cases, it is necessary to estimate the PSF and the specimen fluorescence distribution simultaneously from the image; an approach called blind deconvolution (BD). We developed a method for blind deconvolution in which the PSF is constrained to follow a mathematical model that depends on a small number of unknown parameters. The parametric BD (PBD) method determines these parameters together with the specimen function. We have found encouraging results but the PBD is still computationally intensive. The purpose of this research is to9 make this method practical for routine use and to extend its application to CSMs and TOFEMS. The method will be optimized and its capabilities and limitations thoroughly assessed to be able to provide guidelines for its use. In addition, the method will be incorporated into a deconvolution package that our group developed.