Computational Optical Sectioning Microscopy Algorithms

描述(申请人提供)：三维(3D)荧光 显微镜是研究活标本的有力工具。3D图像是 通常被散焦模糊遮挡，并且由于有限的 传统荧光显微镜所能达到的空间频率范围 图像。这些问题可以通过光学和/或计算来改善 方法：研究方法。最近光学显微镜的复兴导致了各种各样的 克服焦外模糊和频带限制的方法(共焦 和部分共聚焦扫描，双光子和三光子荧光激发， 驻波结构荧光激发显微镜)。这些 方法改善了成像性能，但它们并不是无模糊的 计算方法进一步提高了图像质量。计算方法 已经被接受为光学方法的替代或补充，但 它们的发展没有享受到光学同行的快速步伐。 商业上可用的反卷积程序包很少，大多数都是基于 最初开发的几种用于3D去卷积的算法，包括即席和 不受约束的方法无法弥补显微镜的频带限制。 使用基于彻底的 图像形成和记录过程的数学模型。这个 拟议研究的长期目标是：1)进一步发展 计算算法解释了最重要的来源 图像质量下降。2)开发计算图像估计方法 用于新开发的结构照明显微镜。3)提供 算法的使用指南及其功能和 限制。为了实现这一目标，我们将首先基于 对于显微镜和光探测器的准确模型，那么我们将做一次彻底的 评估这些算法和其他算法，以评估它们的相对优点并 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.