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Convex Total Variation Denoising of Poisson Fluorescence Confocal Images With Anisotropic Filtering

机译:各向异性滤波的泊松荧光共聚焦图像的凸全变色去噪

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Fluorescence confocal microscopy (FCM) is now one of the most important tools in biomedicine research. In fact, it makes it possible to accurately study the dynamic processes occurring inside the cell and its nucleus by following the motion of fluorescent molecules over time. Due to the small amount of acquired radiation and the huge optical and electronics amplification, the FCM images are usually corrupted by a severe type of Poisson noise. This noise may be even more damaging when very low intensity incident radiation is used to avoid phototoxicity. In this paper, a Bayesian algorithm is proposed to remove the Poisson intensity dependent noise corrupting the FCM image sequences. The observations are organized in a 3-D tensor where each plane is one of the images acquired along the time of a cell nucleus using the fluorescence loss in photobleaching (FLIP) technique. The method removes simultaneously the noise by considering different spatial and temporal correlations. This is accomplished by using an anisotropic 3-D filter that may be separately tuned in space and in time dimensions. Tests using synthetic and real data are described and presented to illustrate the application of the algorithm. A comparison with several state-of-the-art algorithms is also presented.
机译:荧光共聚焦显微镜(FCM)现在是生物医学研究中最重要的工具之一。实际上,通过追踪荧光分子随时间的运动,有可能准确地研究细胞及其核内发生的动态过程。由于所获得的辐射量少,并且光学和电子器件的放大很大,因此FCM图像通常会受到严重的泊松噪声的破坏。当使用非常低强度的入射辐射来避免光毒性时,这种噪声甚至更具破坏性。在本文中,提出了一种贝叶斯算法来去除破坏FCM图像序列的依赖于泊松强度的噪声。观察结果以3-D张量进行组织,其中每个平面都是使用光漂白(FLIP)技术中的荧光损失沿细胞核时间获取的图像之一。该方法通过考虑不同的空间和时间相关性来同时去除噪声。这是通过使用各向异性的3D滤波器实现的,该滤波器可以在空间和时间维度上分别进行调整。描述并展示了使用合成数据和真实数据进行的测试,以说明该算法的应用。还介绍了与几种最新算法的比较。

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