Sharif Digital Repository

The performance of most image denoising systems depends on some parameters which should be set carefully based on noise distribution and its variance. As in some applications noise characteristics are unknown, in this research, a criterion which its minimization leads to the best parameter set up is introduced. The proposed criterion is evaluated for the wavelet shrinkage image denoising algorithm using the cross validation procedure. The criterion is tested for some different values of thresholds, and the output leading to the minimum criterion value is selected as the final denoised output. The resulting outputs of our method and the previous threshold selection scheme for the wavelet... 

Denoising is an important preprocessing stage in some Electroencephalography (EEG) applications such as epileptic source localization. In this paper, we propose a new algorithm for denoising the interictal EEG data. The proposed algorithm is based on Generalized Eigenvalue Decomposition of two covariance matrices of the observations. Since one of these matrices is related to the spike durations, we should estimate the occurrence time of the spike peaks and the exact spike durations. To this end, we propose a spike detection algorithm which is based on the available spike detection methods. The comparison of the results of the proposed algorithm with the results of two well-known ICA... 

In this paper, an optical fiber multi-intruder sensor based on polarization maintaining optical fiber (PMF), without any interferometric fiber loop, is introduced. To map the local coordinates of intruders on the beating spectrum of the output modes, radiation from a ramp frequency modulated laser is injected at the input of PMF optical fiber sensor. It is shown that the local coordinates and some characteristics of intruders can be obtained by the measurement of the frequencies and amplitudes of the output mode beating spectrum. Generally the number of beating frequencies is more than the number of intruders. Among the beating frequencies, a group with maximum signal to noise ratio is... 

Epilepsy is a brain disorder that 1% of people population are suffering from. One of the proper non-invasive equipment for diagnosis and analysis of this disease is electroencephalogram (EEG) recordings. However, EEG signals are often contaminated with noises and artifacts that hide epileptic signals of interest. Independent Component Analysis (ICA) is a common Blind Source Separation (BSS) method to denoise EEG signals. ICA has been proved as a worthwhile method to separate the signals of interest from noise and artifacts; nevertheless, it also has some weaknesses. In this work, to improve ICA performance in denoising context, we present an algorithm based on combination of ICA and Time... 

A general deflation framework is described for the separation of a desired signal subspace of arbitrary dimensions from noisy multichannel observations. The method simultaneously uses single and multichannel priors to split the desired and undesired subspaces, even for coplanar (intersecting) subspaces. By appropriate use of signal priors, it can even extract signals from degenerate mixtures of signals and noise recorded from a few number of channels in low SNR scenarios, without the reduction of the data dimensions. As a case study, the performance of the proposed method is studied for the problem of extracting fetal cardiac signals from maternal abdominal recordings, over simulated and... 

In practice, photoacoustic (PA) waves generated with cost-effective, low-energy laser diodes, are weak and almost buried in noise. Reconstruction of an artifact-free PA image from noisy measurements requires an effective denoising technique. Averaging techniques are widely used to increase the signal-to-noise ratio (SNR) of the weak PA signals but the process is time-consuming and in case of very low SNR measurements, hundreds/thousands of data acquisition epochs needed to provide the required data In this study, we propose to use adaptive denoising methodology in which adaptive line enhancers (ALE) has been embedded for increasing the SNR of PA signals in very low-cost PA systems. Our... 

Dictionary learning (DL) for sparse representation has been widely investigated during the last decade. A DL algorithm uses a training data set to learn a set of basis functions over which all training signals can be sparsely represented. In practice, training signals may contain a few outlier data, whose structures differ from those of the clean training set. The presence of these unpleasant data may heavily affect the learning performance of a DL algorithm. In this paper we propose a robust-to-outlier formulation of the DL problem. We then present an algorithm for solving the resulting problem. Experimental results on both synthetic data and image denoising demonstrate the promising... 

Noise reduction of speech signals plays an important role in telecommunication systems. Various types of speech additive noise can be introduced such as babble, crowd, large city, and highway which are the main factor of degradation in perceived speech quality. There are some cases on the receiver side of telecommunication systems, where the direct value of interfering noise is not available and there is just access to noisy speech. In these cases the noise cannot be cancelled totally but it may be possible to reduce the noise in a sensible way by utilizing the statistics of the noise and speech signal. In this paper the proposed method for noise reduction is Bayesian recursive state-space... 

The electrocardiogram (ECG) signal is widely used for diagnosis of various types of cardiac diseases. However, in practical cases, the signal is corrupted by artifacts through the recording process. Thus, denoising of this type of biological signals seems necessary. Several methods have been suggested in recent years for the purpose of ECG denoising; some of which have been based on Empirical Mode Decomposition (EMD). In this paper, an EMD-based approach is proposed which uses the time interval between two adjacent zero crossings within an Intrinsic Mode Function (IMF), defined as Instantaneous Half Period (IHP), to distinguish noise components from the main ECG signal. Noisy signal is... 

In this paper, we suggest a general model for the fixed-valued impulse noise and propose a two-stage method for high density noise suppression while preserving the image details. In the first stage, we apply an iterative impulse detector, exploiting the image entropy, to identify the corrupted pixels and then employ an Adaptive Iterative Mean filter to restore them. The filter is adaptive in terms of the number of iterations, which is different for each noisy pixel, according to the Euclidean distance from the nearest uncorrupted pixel. Experimental results show that the proposed filter is fast and outperforms the best existing techniques in both objective and subjective performance measures... 

In this paper we propose a new algorithm for learning low-dimensional linear subspaces. Our proposed algorithm performs by sequentially finding some low-dimensional subspaces on which a set of training data lies. Each subspace is found in such a way that the number of signals lying on (or near to) it is maximized. Once we found a subset of the training data that is sufficiently close to a subspace, then we omit these signals from the set of training signals and repeat the procedure for the remaining signals until all training signals are assigned to a subspace. This data reduction procedure results in a significant improvement to the runtime of our algorithm. We then propose a robust version... 

In this paper an efficient filtering procedure based on Extended Kalman Filter (EKF) has been proposed. The method is based on a modified nonlinear dynamic model, previously introduced for the generation of synthetic ECG signals. The proposed method considers the angular velocity of ECG signal, as one of the states of an EKF. We have considered two cases for observation equations, in one case we have assumed a corresponding observation to angular velocity state and in the other case, we have not assumed any observations for it. Quantitative evaluation of the proposed algorithm on the MIT-BIH Normal Sinus Rhythm Database (NSRDB) shows that an average SNR improvement of 8 dB is achieved for an...