Articolele autorului Alin Achim
Link la profilul stiintific al lui Alin Achim

SAR image filtering based on the heavy-tailed Rayleigh model

Synthetic aperture radar (SAR) images are inherently affected by a signal dependent noise known as speckle, which is due to the radar wave coherence. In this paper, we propose a novel adaptive despeckling filter and derive a maximum a posteriori (MAP) estimator for the radar cross section (RCS). We first employ a logarithmic transformation to change the multiplicative speckle into additive noise. We model the RCS using the recently introduced heavy-tailed

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Image Denoising Using Bivariate alpha-Stable Distributions in the Complex Wavelet Domain

Recently, the dual-tree complex wavelet transform has been proposed as a novel analysis tool featuring near shift-invariance and improved directional selectivity compared to the standard wavelet transform. Within this framework, we describe a novel technique for removing noise from digital images. We design a bivariate maximum a posteriori (MAP) estimator, which relies on the family of isotropic alpha-stable distributions. Using this relatively new

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Idioventricular low frequency oscillation in QT interval responds univocally to RR confusing kind of mental stress

Seventeen male subjects, aged nineteen to twenty, went through a protocol including, while supine, relaxation at rest (10 min) and mental stress (MS) by a Kraepelin (arithmetic) test (5 min), as part of a larger study. With a polygraphic analog recording set-up we also collected a 1 ms - digital facsimile of a lead II-like thoracic ECG with maximum T-wave (Codas, Dataq Instr). Twenty-three stress responses were assigned to three classes according

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SAR image denoising via Bayesian wavelet shrinkage based on heavy-tailed modeling

Synthetic aperture radar (SAR) images are inherently affected by multiplicative speckle noise, which is due to the coherent nature of the scattering phenomenon. This paper proposes a novel Bayesian-based algorithm within the framework of wavelet analysis, which reduces speckle in SAR images while preserving the structural features and textural information of the scene. First, we show that the subband decompositions of logarithmically transformed

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Novel Bayesian Multiscale Methods for Image Denoising using Alpha-Stable Distributions

Before launching into ultrasound research, it is important to recall that the ultimate goal is to provide the clinician with the best possible information needed to make an accurate diagnosis. Ultrasound images are inherently affected by speckle noise, which is due to image formation under coherent waves. Thus, it appears to be sensible to reduce speckle artifacts before performing image analysis, provided that image texture that might distinguish

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State recognition of RR spectra derived from high resolution Holter ECG using neural networks. Definition of subject-adapted parameter matrix
Novel Bayesian multiscale method for speckle removal in medical ultrasound images

A novel speckle suppression method for medical ultrasound images is presented. First, the logarithmic transform of the original image is analyzed into the multiscale wavelet domain. We show that the subband decompositions of ultrasound images have significantly non-Gaussian statistics that are best described by families of heavy-tailed distributions such as the alpha-stable. Then, we design a Bayesian estimator that exploits these statistics. We

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QT & RR variability spots the earliest autonomic deregulation in diabetes

27 consecutive insulin-dependent diabetic patients (pts), under 50 years, with blood glu-cose controlled within normal limits and no significant or multiple cardiovascular/neuro-logical complications in the lights of clinical tests, went through a protocol as follows: laid-down at relaxed rest for 10 min, then stood-up quietly for 7 min, and finally experienced a stress-interview for 10 min while supine. A thoracic ECG lead was digitized at 1 ms

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