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Tracking algorithms are traditionally based on either a variational approach or a Bayesian one. In the variational case, a cost function is established between two consecutive frames and minimized by standard optimization algorithms. In the Bayesian case, a stochastic motion model is used to maintain temporal consistency. Among the Bayesian methods we focus on the particle filter, which is especially...
We propose a fast algorithm for the detection of cells in fluorescence images. The algorithm, which estimates the number of cells and their respective centers and radii, relies on the fast computation of intensity-based correlations between the cells and a near-isotropic Mexican-hat-like detector. The attractive features of our algorithm are its speed and accuracy. The former attribute is derived...
Fluorescence localization microscopy (i.e., PALM, STORM) has enabled optical imaging at nanometer-scale resolutions. The localization algorithms used in these techniques rely on fitting a 2-D Gaussian to the in-focus image of individual fluorophores. For fixed fluorophores, however, the observed diffraction pattern depends on the orientation of the underlying molecular dipole and does not necessarily...
We propose a novel denoising algorithm to reduce the Poisson noise that is typically dominant in fluorescence microscopy data. To process large datasets at a low computational cost, we use the unnormalized Haar wavelet transform. Thanks to some of its appealing properties, independent unbiased MSE estimates can be derived for each subband. Based on these Poisson unbiased MSE estimates, we then optimize...
Accurate knowledge of an imaging system's point spread function (PSF) is crucial for successful deconvolution. For fluorescence microscopy, PSF estimations based on either theoretical models or experimental measurements are available. However, due to the axially shift-variant nature of the PSF, neither method guarantees an estimate that is valid for the entire object space. In this work, we present...
We present a fast variational deconvolution algorithm that minimizes a quadratic data term subject to a regularization on the -norm of the wavelet coefficients of the solution. Previously available methods have essentially consisted in alternating between a Landweber iteration and a wavelet-domain soft-thresholding operation. While having the advantage of simplicity, they are known to converge slowly...
We present a modified version of the deconvolution algorithm introduced by Figueiredo and Nowak, which leads to a substantial acceleration. The algorithm essentially consists in alternating between a Landweber-type iteration and a wavelet-domain denoising step. Our key innovations are 1) the use of a Shannon wavelet basis, which decouples the problem across subbands, and 2) the use of optimized, subband-dependent...
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