Search for: denoising
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    A study on clustering-based image denoising: from global clustering to local grouping

    , Article European Signal Processing Conference ; 10 November , 2014 , pp. 1657-1661 ; ISSN: 22195491 ; ISBN: 9780992862619 Joneidi, M ; Sadeghi, M ; Sahraee-Ardakan, M ; Babaie-Zadeh, M ; Jutten, C ; Sharif University of Technology
    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... 

    Comparison of different electrocardiogram signal power line denoising methods based on SNR improvement

    , Article 2012 19th Iranian Conference of Biomedical Engineering, ICBME 2012 ; 2012 , Pages 159-162 ; 9781467331302 (ISBN) Amiri, M ; Afzali, M ; Vahdat, B. V ; Sharif University of Technology
    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  

    Sparse Representation and its Application in Image Denoising

    , M.Sc. Thesis Sharif University of Technology Sadeghi, Mostafa (Author) ; Babaie Zadeh, Massoud (Supervisor)
    Sparse signal processing (SSP), as a powerful tool and an efficient alternative to traditional complete transforms, has become a focus of attention during the last decade. In this ap-proach, we want to approximate a given signal as a linear combination of as few as possible basis signals. Each basis signal is called an atom and their collection is called a dictionary. This problem is generally difficult and belongs to the NP-hard problems; since it requires a combinatorial search. In recent years however, it has been shown both theoretically and experimentally that the sparset possible representation of a signal in an overcomplete dictio-nary is unique under some conditions and can be found in... 

    Image Enhancement via Sparse Decomposition

    , M.Sc. Thesis Sharif University of Technology Sadeghipour Kermani, Zahra (Author) ; Babaiezadeh, Massoud (Supervisor)
    Sparse decomposition has recently attracted the attention of many researchers in dierent areas of signal processing. In mathematical viewpoint, sparse decomposition is nding a sparse solution of an Underdetermined System of Linear Equations (USLE). This concept has many applications in dierent signal processing elds including blind sourse separation, optical character recognition and image processing. In this thesis, we investigate the application of sparse decomposition in image denoising. One of the image denoising methods which is related to sparse decomposition concepts is the \transfrom domain method". In this method, the noisy image is rst transformed to another domain, and then noise... 

    Parameter Reduction of Wavelet Transformation for Increasing the Accuracy of Integrated and Automatic History Matching

    , M.Sc. Thesis Sharif University of Technology Dehghani, Amin (Author) ; Pishvaie, Mahmoud Reza (Supervisor) ; Bozorgmehry, Ramein (Supervisor)
    One of problemsin an inverse problem like history matching is there is no unique solution. This means that maybe several diferent permeability maps can correctly reproduced the history of fieldt but there is no garanttee that these maps accurately predidct reservoir production behavior. One way to face and deal with this problem, except manipulation of solution and optimization algorithm (inverse modeling) is to use other data sources like seismic data, Variogram, pore volume, and fracture density or any other parameter obtained from geostatistical investigations.Multi-resolution wavelet analysis can be an appropriate tool to gather the necessary information to characterize the inverse model... 

    Speech enhancement by adaptive noise cancellation in the wavelet domain

    , Article 2005 Fifth International Conference on Information, Communications and Signal Processing, Bangkok, 6 December 2005 through 9 December 2005 ; Volume 2005 , 2005 , Pages 719-723 ; 0780392833 (ISBN); 9780780392830 (ISBN) Akhaee, M. A ; Ameri, A ; Marvasti, F. A ; Sharif University of Technology
    Adaptive filtering has been used for speech denoising in the time domain. During the last decade, wavelet transform has been developed for speech enhancement. In this paper we are proposing to use adaptive filtering in the Wavelet transform domain. We propose a hybrid method of using adaptive filters on the lower scales of a wavelet transformed speech together with the conventional methods (Thresholding, Spectral Subtraction, and Wiener filtering) on the higher scale coefficients. Experimental results demonstrate that the suggested approach is computationally efficient and has a good performance. © 2005 IEEE  

    Modeling and Data Mining of Partial Discharge in Power Transformer Solid Insulation

    , M.Sc. Thesis Sharif University of Technology Jahangir, Hamid (Author) ; Vakilian, Mehdi (Supervisor)
    Transformers are one of the most important equipments in transmission and distribution networks. Transformer unplanned outages have severe impacts on the continuity of power system operation. To improve the reliability of transformers and to achieve an optimum operation cost, online condition monitoring of transformers is inevitable. Information about the quality of the transformers insulation system is known as the best parameter to be monitored in a transformer. Since partial discharge signals are initiated long before the beginning of a severe damage, partial discharge monitoring and its evaluation canbe employed to warn the operator.Data mining on the partial discharge signals extracts... 

    Dictionary Learning and its Application in Image Denoising

    , M.Sc. Thesis Sharif University of Technology Amini, Sajjad (Author) ; Babaiezadeh, Masoud (Supervisor)
    Over-complete transforms due to their maneuverability in signal representation have been under focus during the last decade. Different properties for the representation can be useful in different applications. These properties includes minimum ℓ2 representation, minimum ℓ1 representation, minimum ℓ0 representation and so on. Among these properties, minimum ℓ0 representation (also known as sparse representation) has been shown to be efficient in many applications including image denoising, data compression, blind source separation and so on, and create a new approach in signal processing area named sparse signal processing. Sparse signal processing is based on two principles, the first one is... 

    Rigid Registration using Sparse Representation Descriptor in MR Images

    , M.Sc. Thesis Sharif University of Technology Ebrahim Abdollahian (Author) ; Manzuri-Shalmani, Mohammad Taghi (Supervisor)
    In recent years, sparse representation has had a variety of applications in computer vision such as noise reduction, image reconstruction, classification and dimension reduction. In this project, we aim to provide a method of matching the keypoints obtained from the Scale Invariant feature Transform (SIFT) algorithm. In this algorithm is used descriptor instead of intensity . The proposed method, first, extracts the salient points from the images and learns a dictionary-based descriptors corresponding to the points. Then, using the dictionary, it obtains the sparse coefficients for each salient point by which, it determines the correspondence of the salient points in the two images using SVD... 

    ECG Denoising by Deterministic Approaches

    , M.Sc. Thesis Sharif University of Technology Taghavi Razavizadeh, Marjaneh (Author) ; Shamsollahi, Mohammad Bagher (Supervisor)
    The goal of the research presented in this thesis is removing noise from electrocardiogram (ECG) signals. The electrocardiogram is a test that measures the electrical activity of the heart. The information obtained from an electrocardiogram can be used to diagnose different types of heart disease. It may be useful for seeing how well the patient is responding to treatment. The extraction of high resolution ECG signals from noisy measurements is among the most tempting open problems of biomedical signal processing. Extracting useful clinical information from the real (noisy) ECG requires reliable signal processing techniques. Numerous methods have been reported to denoise ECG signals based on... 

    Applications of Sparse Representation in Image Processing

    , M.Sc. Thesis Sharif University of Technology Nayyer, Sara (Author) ; Babaie Zadeh, Massoud (Supervisor)
    The sparse decomposition problem or nding sparse solutions of underdetermined linear systems of equations is one of the fundamental issues in signal processing and statistics. In recent years, this issue has been of great interest to researches in various elds of signal processing and accordingly found to be greatly benecial in those elds. This thesis aims at the investigation of the applications of the sparse decomposition problem in image processing. Among dierent applications such as compression, reconstruction, separation and image denoising, this thesis mainly focuses on the last one. One of the methods of image denoising which is closely tied to the sparse decomposition, is the method... 

    Interictal Noise Cancellation Based on Combination of ICA-based and Wavelet-based Denoising Approaches

    , M.Sc. Thesis Sharif University of Technology Zakizadeh, Mohammad (Author) ; Shamsollahi, Mohammad Bagher (Supervisor)
    Interictal EEG signals are very critical in diagnosis of epilepsy. Analysis of interictal EEG signals is very challenging due to contamination by various undesired signals like background EEG, muscular activity, noise, etc. Thus denoising of interictal signals has been an active research field in recent years. Primary purpose of this thesis is to denoise interictal EEG signals by using different combinations of ICA-based and wavelet denoising approaches. Then a new direction is pursued by using Morphological Component Analysis (MCA) which is a method for solving source separation problems based on morphological diversity of sources. Afterward MCA is modified by considering more prior... 

    EEG Noise Cancellation by Stochastic and Deterministic Approaches

    , M.Sc. Thesis Sharif University of Technology Salsabili, Sina (Author) ; Shamsollahi, Mohammad Bagher (Supervisor)
    Noise contamination is inevitable in biomedical recordings. In some cases biomedical recordings are highly contaminated with artifacts which make the effective recovering process hard to achieve. Many different methods have been proposed for artifact removal from biomedical signals but introducing an effective method which can present valuable data for medical analysis, is still an ongoing process.
    This dissertation focuses on inter-ictal EEG denoising approaches including ICA-based and EMD-based methods and different combination of these methods. These methods are tested on simulated epileptic recordings which are contaminated with real muscle artifact and EEG signal. The denoised... 

    A novel method of deinterleaving pulse repetition interval modulated sparse sequences in noisy environments

    , Article IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences ; Vol. E97-A, issue. 5 , 2014 , pp. 1136-1139 ; ISSN: 17451337 Keshavarzi, M ; Amiri, D ; Pezeshk, A.M ; Farzaneh, F ; Sharif University of Technology
    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... 

    Denoising of genetic switches based on Parrondo's paradox

    , Article Physica A: Statistical Mechanics and its Applications ; Volume 493 , 2018 , Pages 410-420 ; 03784371 (ISSN) Fotoohinasab, A ; Fatemizadeh, E ; Pezeshk, H ; Sadeghi, M ; Sharif University of Technology
    Elsevier B.V  2018
    Random decision making in genetic switches can be modeled as tossing a biased coin. In other word, each genetic switch can be considered as a game in which the reactive elements compete with each other to increase their molecular concentrations. The existence of a very small number of reactive element molecules has caused the neglect of effects of noise to be inevitable. Noise can lead to undesirable cell fate in cellular differentiation processes. In this paper, we study the robustness to noise in genetic switches by considering another switch to have a new gene regulatory network (GRN) in which both switches have been affected by the same noise and for this purpose, we will use Parrondo's... 

    Image denoising using sparse representations

    , Article 8th International Conference on Independent Component Analysis and Signal Separation, ICA 2009, Paraty, 15 March 2009 through 18 March 2009 ; Volume 5441 , 2009 , Pages 557-564 ; 03029743 (ISSN) Valiollahzadeh, S ; Firouzi, H ; Babaie Zadeh, M ; Jutten, C ; Sharif University of Technology
    The problem of removing white zero-mean Gaussian noise from an image is an interesting inverse problem to be investigated in this paper through sparse and redundant representations. However, finding the sparsest possible solution in the noise scenario was of great debate among the researchers. In this paper we make use of new approach to solve this problem and show that it is comparable with the state-of-art denoising approaches. © Springer-Verlag Berlin Heidelberg 2009  

    Living near the edge: A lower-bound on the phase transition of total variation minimization

    , Article IEEE Transactions on Information Theory ; Volume 66, Issue 5 , 2020 , Pages 3261-3267 Daei, S ; Haddadi, F ; Amini, A ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2020
    This work is about the total variation (TV) minimization which is used for recovering gradient-sparse signals from compressed measurements. Recent studies indicate that TV minimization exhibits a phase transition behavior from failure to success as the number of measurements increases. In fact, in large dimensions, TV minimization succeeds in recovering the gradient-sparse signal with high probability when the number of measurements exceeds a certain threshold; otherwise, it fails almost certainly. Obtaining a closed-form expression that approximates this threshold is a major challenge in this field and has not been appropriately addressed yet. In this work, we derive a tight lower-bound on... 

    Wavelet image denoising based on improved thresholding neural network and cycle spinning

    , Article 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07, Honolulu, HI, 15 April 2007 through 20 April 2007 ; Volume 1 , 2007 , Pages I585-I588 ; 15206149 (ISSN); 1424407281 (ISBN); 9781424407286 (ISBN) Sahraeian, M. E ; Marvasti, F ; Sadati, N ; Sharif University of Technology
    In this paper we propose a new method for image noise reduction based on wavelet transform. In this method we: introduce an improved version of thresholding neural networks. (TNN) by utilizing a new class of smooth nonlinear thresholding functions as the activation function. Using this approach we will find the best thresholds in the sense of minimum mean square error (MMSE). Then using, TNN with obtained thresholds, we employ a cycle-spinningbased technique to reduce image artifacts. Experimental results indicate that the proposed method outperforms several other established wavelet denoising techniques, in terms of Peak-Signal-to-Noise-Ratio (PSNR) and visual quality. © 2007 IEEE  

    ECG denoising with adaptive bionic wavelet transform

    , Article 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06, New York, NY, 30 August 2006 through 3 September 2006 ; 2006 , Pages 6597-6600 ; 05891019 (ISSN); 1424400325 (ISBN); 9781424400324 (ISBN) Sayadi, O ; Shamsollahi, M. B ; Sharif University of Technology
    In this paper a new ECG denoising scheme is proposed using a novel adaptive wavelet transform, named bionic wavelet transform (BWT), which had been first developed based on a model of the active auditory system. There has been some outstanding features with the BWT such as nonlinearity, high sensitivity and frequency selectivity, concentrated energy distribution and its ability to reconstruct signal via inverse transform but the most distinguishing characteristic of BWT is that its resolution in the time-frequency domain can be adaptively adjusted not only by the signal frequency but also by the signal instantaneous amplitude and its first-order differential. Besides by optimizing the BWT... 

    Noise reduction from Magnetic Resonance images using nonseperable transforms

    , Article Medical Imaging 2006: Image Processing, San Diego, CA, 13 February 2006 through 16 February 2006 ; Volume 6144 III , 2006 ; 16057422 (ISSN); 0819464236 (ISBN); 9780819464231 (ISBN) Nezhadarya, E ; Shamsollahi, M. B ; Sharif University of Technology
    Multi-scale transforms have got a lot of applications in image processing, in recent years. Wavelet transform is a powerful multiscale transform for denoising noisy signals and images, but the usual two-dimensional separable wavelets are sub-optimal. These separable wavelet transforms can successfully identify zero dimensional singularities in images, but can weakly identify one dimensional singularities such as edges, curves and lines. In this sense, non-separable transforms such as Ridgelet and Curvelet transforms are proposed by Candes and Donoho. The coefficients produced by these non-separable transforms have shown to be sparser than wavelet coefficients. This fact results in better...