Structured additive regression models are perhaps the most commonly used class of models
in statistical applications. It includes, among others,(generalized) linear …

We propose a general methodology (PURE-LET) to design and optimize a wide class of
transform-domain thresholding algorithms for denoising images corrupted by mixed Poisson …

Under consideration is the large body of signal recovery problems that can be formulated as
the problem of minimizing the sum of two (not necessarily smooth) lower semicontinuous …

We propose a new variational model to denoise an image corrupted by Poisson noise. Like
the ROF model described in [1] and [2], the new model uses total-variation regularization …

In order to denoise Poisson count data, we introduce a variance stabilizing transform (VST)
applied on a filtered discrete Poisson process, yielding a near Gaussian process with …

Techniques for calcium imaging were first demonstrated in the mid-1970s, whilst tools to
analyse these markers of cellular activity are still being developed and improved today. For …

We present a fast algorithm for image restoration in the presence of Poisson noise. Our
approach is based on (1) the minimization of an unbiased estimate of the MSE for Poisson …

Recently augmented Lagrangian method has been successfully applied to image
restoration. We extend the method to total variation (TV) restoration models with non …

This article introduces a new method for the estimation of the intensity of an inhomogeneous
one-dimensional Poisson process. The Haar-Fisz transformation transforms a vector of …

Medical images are often noisy owing to the physical mechanisms of the acquisition
process. The great majority of the denoising algorithms assume additive white Gaussian …