13 March 2013 Robust blind deconvolution for fluorescence microscopy using GEM algorithm
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Proceedings Volume 8669, Medical Imaging 2013: Image Processing; 86692L (2013) https://doi.org/10.1117/12.2007688
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States
Abstract
Fluorescence microscopies have been used as an essential tool in biomedical research, because of better signal to noise ratio compared to other microscopies. Among the various kinds of fluorescence microscopies, wide field fluorescence microscopy (WFFM) and confocal fluorescence microscopy are generally most widely used. While confocal microscopy image has higher clarity than WFFM, it is not suitable for live cells because of a number of major drawbacks such as photo-bleaching and low image acquisition speed. The purpose of this paper is to obtain clearer live cell images by restoring degraded WFFM image. Many studies have been carried out for the purpose of obtaining clearer live cell images by restoring degraded WFFM images, while most of them are not based on regularized MLE (Maximum likelihood estimator) which restores the image by maximizing Poisson likelihood. However, the MLE method is not robust to noise because of ill posed problems. Actually, Gaussian as well as Poisson noise exists in the WFFM image. There are some approaches to improve noise robustness, but these methods cannot guarantee the convergence of likelihood. The purpose of this paper is to obtain clearer live cell images by restoring degraded WFFM images utilizing a robust deconvolution method for WFFM using generalized expectation maximization (GEM) algorithm that guarantees the convergence of a regularized likelihood. Moreover, we actualized a blind deconvolution that can restore the images and estimate point spread function (PSF) simultaneously, while most other researches assume that the PSF is previously known. We performed the proposed algorithm on fluorescent bead and cell images. Our results show that the proposed method restores more accurately than existing methods.
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Boyoung Kim, Boyoung Kim, Takeshi Naemura, Takeshi Naemura, } "Robust blind deconvolution for fluorescence microscopy using GEM algorithm", Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 86692L (13 March 2013); doi: 10.1117/12.2007688; https://doi.org/10.1117/12.2007688
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