Soosan Beheshti

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The problem of signal denoising with an orthogonal basis is considered. The existing approaches convert the considered problem into one of finding a threshold for estimates of basis coefficients. In this paper, a new solution to the denoising problem is proposed. The method is based on the description length of the noiseless data in subspaces of the bases.(More)
A denoising technique based on noise invalidation is proposed. The adaptive approach derives a noise signature from the noise order statistics and utilizes the signature to denoise the data. The novelty of this approach is in presenting a general-purpose denoising in the sense that it does not need to employ any particular assumption on the structure of the(More)
Two promising classes of techniques are developed for e cient multiuser detection in codedivision multiple-access (CDMA) communication systems subject to fading due to time-varying multipath propagation. Both are designed to jointly suppress both intersymbol and multipleaccess interference inherent in such systems, and exploit all available time and(More)
A method of noise variance estimation in BayesShrink image denoising is presented. The proposed approach competes with the well known MAD-based method and outperforms this method in more than 99% of our experimental results. The approach, called Residual Autocorrelation Power (RAP), provides a more accurate noise variance estimate and results in a smaller(More)
Improving image quality is a critical objective in low dose computed tomography (CT) imaging and is the primary focus of CT image denoising. State-of-the-art CT denoising algorithms are mainly based on iterative minimization of an objective function, in which the performance is controlled by regularization parameters. To achieve the best results, these(More)
Hyperspectral imaging analysis aims at the estimation of the number of constituent substances, known as endmembers, their spectral signatures as well as their abundance fractions. Due to the nature of hyperspectral sensors, output data is mostly associated with correlated noise rather than with the white Gaussian noise considered in most of the analysis. In(More)
We present a novel approach to estimating the mean square error (MSE) associated with any given threshold level in both hard and soft thresholding. The estimate is provided by using only the data that is being thresholded. This adaptive approach provides probabilistic confidence bounds on the MSE. The MSE bounds can be used to evaluate the denoising method.(More)
Hyperspectral imaging sensors simultaneously acquire data in hundreds of spectral bands, facilitating detailed study of a scanned object. Unmixing the hyperspectral data as well as estimating the intrinsic dimension of hypercube requires an accurate evaluation of the noise structure. Existing methods mostly simplify the evaluation by considering a white(More)
Pre-shock waveform analysis for optimizing the timing of shock delivery could be immensely helpful to emergency medical personnel in treating ventricular fibrillation. For this purpose, our proposed method resolves the pre-shock surface electrocardiogram into independent sources using a blind source separation approach. The electrocardiogram pre-shock(More)