Sharif Digital Repository

This paper studies denoising of images contaminated with additive white Gaussian noise (AWGN). In recent years, clustering-based methods have shown promising performances. In this paper we show that low-rank subspace clustering provides a suitable clustering problem that minimizes the lower bound on the MSE of the denoising, which is optimum for Gaussian noise. Solving the corresponding clustering problem is not easy. We study some global and local sub-optimal solutions already presented in the literature and show that those that solve a better approximation of our problem result in better performances. A simple image denoising method based on dictionary learning using the idea of... 

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... 

This letter presents a novel method based on sparsity, to solve the problem of deinterleaving pulse trains. The proposed method models the problem of deinterleaving pulse trains as an underdetermined system of linear equations. After determining the mixing matrix, we find sparsest solution of an underdetermined system of linear equations using basis pursuit denoising. This method is superior to previous ones in a number of aspects. First, spurious and missing pulses would not cause any performance reduction in the algorithm. Second, the algorithm works well despite the type of pulse repetition interval modulation that is used. Third, the proposed method is able to separate similar... 

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... 

This paper presents a modified sensor-based online method for mobile robot navigation generating paths in dynamic environments. The core of the navigation algorithm is based on the velocity obstacle avoidance method and the guidance-based tracking algorithm. A fuzzy decision maker is designed to combine the two mentioned algorithms intelligently. Hence the robot will be able to decide intelligently in various situations when facing the moving obstacles and moving target. A noble noise cancellation algorithm using Neural Network is designed to navigate the robot in an uncertain dynamic environment safely. The results show that the robot can track a moving target while maneuvering safely in... 

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... 

In this work, we present a new method of noise removal which is applied on images corrupted by impulse noise. This new algorithm has a good trade-off between quantitative and qualitative properties of the recovered image and the computation time. In this new method, the corrupted pixels are replaced by using a median filter or, they are estimated by their neighbors' values. Our proposed method shows better results especially in very high density noisy images than Standard Median Filter (SMF), Adaptive Median Filter (AMF) and some other well-known filters for removing impulse noise. Experimental results show the superiority of the proposed algorithm in measures of PSNR and SSIM, specifically... 

In order to access to an accurate detection of electrocardiogram signal in medical approaches especially mobile health and wearable medical devices, development of noise cancellation algorithms seems essential. In this study, the power line noise in ECG signal is filtered using several methods including DFT based, IIR, FIR, adaptive, Kalman, Wavelet and higher order statistics filters. Signal-to-noise ratio (SNR) improvements of the filters are then compared. It is found that FIR and IIR filtering show higher SNR improvement  

Electrocardiogram is one of the most prominent cardiac signals being capable to be utilized for medical uses such as arrhythmia detection. Over the years, the feasibility of using this signal for human identification issue has been investigated, and some methods have been proposed. In this Paper a novel approach is proposed for electrocardiogram (ECG) based human identification using Extended Kalman Filter (EKF). The innovation signal of EKF has been considered as feature which is used to classify different subjects. In this paper a general issue, human identification, is summarized to a classification problem in which the proposed features of each subject is calculated, and the... 

Adaptive Noise Cancellation (ANC) is an effective dual-channel technique for background noise reduction. Due to the presence of uncorrelated noise components at the two inputs in vehicular environments, ANC does not provide sufficient background noise reduction. To alleviate this problem, a complementary linear filter is added to ANC structure. Filter coefficients are determined to make the enhanced signal an MMSE estimation of speech signal. Therefore, the ANC structure is modified to a dual-channel Wiener structure. We prove that this structure is identical to the LMS type ANC which is followed by a Wiener post-filter. A new method is proposed for the noise spectrum estimation in the... 

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... 

Sparse representation is a powerful tool for image processing, including noise removal. It is an effective method for Gaussian noise removal by taking advantage of a fixed and learned dictionary. In this study, the variable distribution of Rician noise is reduced in magnetic resonance (MR) images by sparse representation based on reconstruction error sets. Standard deviation of Gaussian noise is used to find these errors locally. The proposed method represents two formulas for local error calculation using standard deviation of noise. The acquired results from the real and simulated images are comparable, and in some cases, better than the best Rician noise removal method due to the... 

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... 

In this paper, we address the problem of recovering degraded images using multivariate Gaussian mixture model (GMM) as a prior. The GMM framework in our method for image restoration is based on the assumption that the accumulation of similar patches in a neighborhood are derived from a multivariate Gaussian probability distribution with a specific covariance and mean. Previous methods of image restoration with GMM have not considered spatial (geometric) distance between patches in clustering. Our conducted experiments show that in the case of constraining Gaussian estimates into a finite-sized windows, the patch clusters are more likely to be derived from the estimated multivariate Gaussian... 

Due to the presence of excessive noise in the recorded partial discharge (PD) current signals, de-noising of these signals is a crucial task for performing any investigation on the subject. Meanwhile, to accelerate this de-noising process a single PD pulse can be extracted from the train of those recorded pulses, followed by its de-noising. In this paper a single PD pulse is extracted from the train of recorded PD pulses, using noisy recorded data cumulative energy. A de-noising technique based on adaptive neuro-fuzzy inference systems is proposed. To verify the validity of the proposed method, four different sources of PD signals are physically simulated. The proposed method is applied on...