Multiframe sure-let denoising of timelapse fluorescence microscopy images

Delpretti, Saskia; Luisier, Florian; Ramani, Sathish; Blu, Thierry; Unser, Michaël

doi:10.1109/isbi.2008.4540954

Cited by 33 publications

(38 citation statements)

References 7 publications

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“…The Gaussian nature of the distribution of noise in the spatial domain justifies the use of noise reduction methods based on minimizing the Stein's unbiased estimator [15] as has been done in the case of natural images [16][17][18].…”

Section: Discussionmentioning

confidence: 99%

Statistical Characterization of Portal Images and Noise from Portal Imaging Systems

et al. 2012

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In this paper, we consider the statistical characteristics of the so-called portal images, which are acquired prior to the radiotherapy treatment, as well as the noise that present the portal imaging systems, in order to analyze whether the well-known noise and image features in other image modalities, such as natural image, can be found in the portal imaging modality. The study is carried out in the spatial image domain, in the Fourier domain, and finally in the wavelet domain. The probability density of the noise in the spatial image domain, the power spectral densities of the image and noise, and the marginal, joint, and conditional statistical distributions of the wavelet coefficients are estimated. Moreover, the statistical dependencies between noise and signal are investigated. The obtained results are compared with practical and useful references, like the characteristics of the natural image and the white noise. Finally, we discuss the implication of the results obtained in several noise reduction methods that operate in the wavelet domain.

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Section: Discussionmentioning

confidence: 99%

Statistical Characterization of Portal Images and Noise from Portal Imaging Systems

et al. 2012

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“…This method has been previously sketched in [42] and we provide here additional details and some improvements. A similar approach has been since described in [24], [43]. From (1), we have…”

Section: ) Parameter Estimationmentioning

confidence: 99%

“…Wavelet shrinkage [19], [20], Wiener filtering [21] or PDE-based methods [22] are typical examples of such methods. Some of them have been successfully adapted to video-microscopy [23], [24]. Recently, an extension of the non-local means filter [1] also related to the universal denoising (DUDE) algorithm [25] and the entropy-based UINTA filter [26], has been proposed to process image sequences.…”

Section: Introductionmentioning

confidence: 99%

“…As already mentioned, patch-based methods have been proposed for denoising image sequences, but, to our knowledge, only anisotropic diffusion and wavelet shrinkage have been applied to 2D+time fluorescence video-microscopy [23], [24]. The main features of the proposed method have already been presented in a discrete setting at the IEEE-ISBI'08 conference [37].…”

Section: Introductionmentioning

confidence: 99%

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Patch-Based Nonlocal Functional for Denoising Fluorescence Microscopy Image Sequences

Boulanger

Kervrann

Bouthémy

et al. 2010

IEEE Trans. Med. Imaging

268

262

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Abstract-We present a non-parametric regression method for denoising 3D image sequences acquired via fluorescence microscopy. The proposed method exploits the redundancy of the 3D+time information to improve the signal-to-noise ratio of images corrupted by PoissonGaussian noise. A variance stabilization transform is first applied to the image-data to remove the dependence between the mean and variance of intensity values. This preprocessing requires the knowledge of parameters related to the acquisition system, also estimated in our approach. In a second step, we propose an original statistical patchbased framework for noise reduction and preservation of space-time discontinuities. In our study, discontinuities are related to small moving spots with high velocity observed in fluorescence video-microscopy. The idea is to minimize an objective non-local energy functional involving spatiotemporal image patches. The minimizer has a simple form and is defined as the weighted average of input data taken in spatially-varying neighborhoods. The size of each neighborhood is optimized to improve the performance of the pointwise estimator. The performance of the algorithm (which requires no motion estimation) is then evaluated on both synthetic and real image sequences using qualitative and quantitative criteria.Index Terms-Video-microscopy, fluorescence, image sequence denoising, patch-based approach, Poisson noise, variance stabilization, adaptive estimation, energy minimization.

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“…Despite constant improvements in data acquisition devices, electronic noise usually cannot be neglected. Among existing works dealing with Poisson-Gaussian noise, a number of methods have addressed noise identification problems [5][6][7][8][9][10][11], as well as denoising [9,[12][13][14][15][16] and reconstruction [17][18][19][20][21]. The developed algorithms are useful in various areas such as digital photography [8], medicine [22], biology [23] and astronomy [18].…”

Section: Introductionmentioning

confidence: 99%

An EM Approach for Time-Variant Poisson-Gaussian Model Parameter Estimation

Jezierska

Chaux

Pesquet

et al. 2014

IEEE Trans. Signal Process.

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International audienceThe problem of estimating the parameters of a Poisson-Gaussian model from experimental data has recently raised much interest in various applications, for instance in confocal fluorescence microscopy. In this context, a field of independent random variables is observed, which is varying both in time and space. Each variable is a sum of two components, one following a Poisson and the other a Gaussian distribution. In this paper, a general formulation is considered where the associated Poisson process is nonstationary in space and also exhibits an exponential decay in time, whereas the Gaussian component corresponds to a stationary white noise with arbitrary mean. To solve the considered parametric estimation problem, we follow an iterative Expectation-Maximization (EM) approach. The parameter update equations involve deriving finite approximation of infinite sums. Expressions for the maximum error incurred in the process are also given. Since the problem is non-convex, we pay attention to the EM initialization, using a moment-based method where recent optimization tools come into play. We carry out a performance analysis by computing the Cramer-Rao bounds on the estimated variables. The practical performance of the proposed estimation procedure is illustrated on both synthetic data and real fluorescence macroscopy image sequences. The algorithm is shown to provide reliable estimates of the mean/variance of the Gaussian noise and of the scale parameter of the Poisson component, as well as of its exponential decay rate. In particular, the mean estimate of the Poisson component can be interpreted as a good-quality denoised version of the data

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Multiframe sure-let denoising of timelapse fluorescence microscopy images

Cited by 33 publications

References 7 publications

Statistical Characterization of Portal Images and Noise from Portal Imaging Systems

Statistical Characterization of Portal Images and Noise from Portal Imaging Systems

Patch-Based Nonlocal Functional for Denoising Fluorescence Microscopy Image Sequences

An EM Approach for Time-Variant Poisson-Gaussian Model Parameter Estimation

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