Publications de Sathish Ramani

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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 ...

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 ...

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 ...

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 ...

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 ...

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. ...

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 ...

Starting from the power spectral density of Matérn stochastic processes, we introduce a new family of splines that is defined in terms of the whitening operator of such processes. We show that these Matérn splines admit a stable representation in a B-splin ...

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 ...

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 ...

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 ...

Sathish Ramani

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Regularized Interpolation for Noisy Images

Nonideal sampling and regularized interpolation of noisy data

Sampling in Practice: Is the Best Reconstruction Space Bandlimited?

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

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

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

Regularized interpolation for noisy data

Nonideal Sampling and Regularization Theory

Matérn B-Splines and the Optimal Reconstruction of Signals

Multiframe SURE-LET denoising of timelapse fluorescence microscopy images

Deconvolution of 3D fluorescence micrographs with automatic risk minimization

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