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Total variation versus wavelet-based methods for image denoising in fluorescence lifetime imaging microscopy

机译:基于小波的图像去噪的总变化与荧光寿命显微镜显微镜显微镜的图像去噪

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摘要

We report the first application of wavelet-based denoising (noise removal) methods to time-domain box-car fluorescence lifetime imaging microscopy (FLIM) images and compare the results to novel total variation (TV) denoising methods. Methods were tested first on artificial images and then applied to low-light live-cell images. Relative to undenoised images, TV methods could improve lifetime precision up to 10-fold in artificial images, while preserving the overall accuracy of lifetime and amplitude values of a single-exponential decay model and improving local lifetime fitting in live-cell images. Wavelet-based methods were at least 4-fold faster than TV methods, but could introduce significant inaccuracies in recovered lifetime values. The denoising methods discussed can potentially enhance a variety of FLIM applications, including live-cell, in vivo animal, or endoscopic imaging studies, especially under challenging imaging conditions such as low-light or fast video-rate imaging.
机译:我们报告了基于小波的去噪(噪声除外)方法的第一次应用于时域箱式荧光寿命成像显微镜(FLIM)图像,并将结果与​​新的总变化(TV)去噪方法进行比较。首先在人造图像上测试方法,然后施加到低光直播电池图像。相对于未缺乏的图像,电视方法可以提高人造图像中的寿命精度高达10倍,同时保留单指数衰减模型的寿命和幅度值的整体精度,并改善在实时细胞图像中的局部寿命拟合。基于小波的方法比电视方法快至少4倍,但可以在恢复的寿命值中引入显着的不准确性。所讨论的去噪方法可以潜在地增强各种FLIM应用,包括活细胞,体内动物或内窥镜成像研究,特别是在诸如低光或快速视频速率成像的挑战性成像条件下。

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