Publications de Sathish Ramani

Regularized Interpolation for Noisy Images

Michaël Unser, Philippe Thévenaz, Sathish Ramani

Interpolation is the means by which a continuously defined model is fit to discrete data samples. When the data samples are exempt of noise, it seems desirable to build the model by fitting them exactly. In medical imaging, where quality is of paramount im ...

Institute of Electrical and Electronics Engineers2010

Nonideal sampling and regularized interpolation of noisy data

Sathish Ramani

Conventional sampling (Shannon's sampling formulation and its approximation-theoretic counterparts) and interpolation theories provide effective solutions to the problem of reconstructing a signal from its samples, but they are primarily restricted to the ...

EPFL2009

Multiframe SURE-LET denoising of timelapse fluorescence microscopy images

Michaël Unser, Thierry Blu, Saskia Joëlle Delpretti Anex, Florian Luisier, Sathish Ramani

Due to the random nature of photon emission and the various internal noise sources of the detectors, real timelapse fluorescence microscopy images are usually modeled as the sum of a Poisson process plus some Gaussian white noise. In this paper, we propose ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2008

Deconvolution of 3D fluorescence micrographs with automatic risk minimization

Michaël Unser, Cédric René Jean Vonesch, Sathish Ramani

We investigate the problem of automatic tuning of a deconvolution algorithm for three-dimensional (3D) fluorescence microscopy; specifically, the selection of the regularization parameter lambda. For this, we consider a realistic noise model for data obtai ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2008

Nonideal Sampling and Regularization Theory

Michaël Unser, Dimitri Nestor Alice Van De Ville, Thierry Blu, Sathish Ramani

Shannon's sampling theory and its variants provide effective solutions to the problem of reconstructing a signal from its samples in some “shift-invariant” space, which may or may not be bandlimited. In this paper, we present some further justification for ...

IEEE2008

Recursive Risk Estimation For Non-Linear Image Deconvolution With A Wavelet-Domain Sparsity Constraint

Michaël Unser, Cédric René Jean Vonesch, Sathish Ramani

We propose a recursive data-driven risk-estimation method for non-linear iterative deconvolution. Our two main contributions are 1) a solution-domain risk-estimation approach that is applicable to non-linear restoration algorithms for ill-conditioned inver ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2008

Monte-Carlo SURE: A Black-Box Optimization of Regularization Parameters for General Denoising Algorithms

Michaël Unser, Thierry Blu, Sathish Ramani

We consider the problem of optimizing the parameters of a given denoising algorithm for restoration of a signal corrupted by white Gaussian noise. To achieve this, we propose to minimize Stein's unbiased risk estimate (SURE) which provides a means of asses ...

IEEE2008

Blind optimization of algorithm parameters for signal denoising by Monte-Carlo sure

Michaël Unser, Thierry Blu, Sathish Ramani

We consider the problem of optimizing the parameters of an arbitrary denoising algorithm by minimizing Stein's Unbiased Risk Estimate (SURE) which provides a means of assessing the true mean-squared-error (MSE) purely from the measured data assuming that i ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2008

Regularized interpolation for noisy data

Michaël Unser, Philippe Thévenaz, Sathish Ramani

Interpolation is a vital tool in biomedical signal processing. Although there exists a substantial literature dedicated to noise-free conditions, much less is known in the presence of noise. Here, we document the breakdown of standard interpolation for noi ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2007

Non-Ideal Sampling and Adapted Reconstruction Using the Stochastic Matérn Model

Michaël Unser, Dimitri Nestor Alice Van De Ville, Sathish Ramani

The Matérn class is a parametric family of autocorrelation functions that is commonly used in geostatistics. We argue that a generalized, anisotropic version of this model is suitable for capturing the correlation structure of a variety of natural images. ...

IEEE2006