Computational Optical Sectioning Microscopy Algorithms

描述（由申请人提供）：三维（3D）荧光 显微镜是研究活体标本的有力工具。3D图像 通常被失焦模糊所模糊，并且由于有限的 传统的荧光显微镜可以 形象这些问题可以通过光学和/或计算方法来改善。 方法.最近光学显微镜的复兴导致了各种各样的 克服离焦模糊和频带限制的方法（共焦 和部分共焦扫描，2-和3-光子荧光激发， 驻波、结构荧光激发显微镜）。这些 这些方法改善了成像特性，但是它们不是无模糊的， 计算方法进一步提高了图像质量。计算方法 作为光学方法的替代或补充， 它们的发展速度不及光学产品。 很少有商业上可用的反卷积软件包存在，并且大多数基于 为3D反卷积开发的前几种算法，包括ad-hoc和 不受约束的方法不能补偿显微镜的频带限制。 显著的改进是可能的方法， 图像形成和记录过程的数学模型。的 研究的长期目标是：1）进一步发展 计算算法，占最重要的来源， 图像的退化。2)开发计算图像估计方法 新开发的结构照明显微镜。3)提供 使用算法及其能力的准则， 局限性。为了实现这一目标，我们将首先推导出基于 精确的显微镜和光探测器模型，然后我们将做一个彻底的 评估这些算法和其他算法，以评估它们的相对优点， 提供使用指南。

DESCRIPTION (provided by applicant): Three dimensional (3D) fluorescence microscopy is a powerful tool for the study of living specimens. 3D images are normally obscured by out-of-focus blur and often artifactual due to the limited band of spatial frequencies that the conventional fluorescence microscope can image. These problems can be ameliorated by optical and/or computational methods. A recent renaissance in optical microscopy has led to a wide variety of approaches to overcome the out-of-focus blur and band-limitation (confocal and partially confocal scanning, 2- and 3-photon fluorescence excitation, standing wave, structured-fluorescence-excitation microscopes..). These approaches improve the imaging properties, but they are not blur-free and computational approaches further improve image quality. Computational methods have gained acceptance as an alternative or complement to optical methods, but their development has not enjoyed the fast pace of their optical counterparts. Few commercially-available deconvolution packages exist, and most are based on the first few algorithms developed for 3D deconvolution, including ad-hoc and unconstrained methods unable compensate for the band-limitation of microscopes. Significant improvements are possible with methods based on a thorough mathematical model of the process of image formation and recording. The long-term goals of the proposed research are: 1) To further develop computational algorithms that account for the most significant sources of degradation in the image. 2) To develop computational image estimation methods for the newly-developed structured illumination microscope. 3) To provide guidelines for the use of the algorithms and for their capabilities and limitations. To achieve this goal we will first derive algorithms based on an accurate model for the microscope and light detector then we will do a thorough evaluation of these and other algorithms to assess their relative merits and to provide guidelines for their use.