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Showing papers on "Noise reduction published in 2010"


Journal ArticleDOI
TL;DR: In this article, the authors proposed a new method where information regarding the local image noise level is used to adjust the amount of denoising strength of the filter, which is automatically obtained from the images using a new local noise estimation method.
Abstract: PURPOSE: To adapt the so-called nonlocal means filter to deal with magnetic resonance (MR) images with spatially varying noise levels (for both Gaussian and Rician distributed noise). MATERIALS AND METHODS: Most filtering techniques assume an equal noise distribution across the image. When this assumption is not met, the resulting filtering becomes suboptimal. This is the case of MR images with spatially varying noise levels, such as those obtained by parallel imaging (sensitivity-encoded), intensity inhomogeneity-corrected images, or surface coil-based acquisitions. We propose a new method where information regarding the local image noise level is used to adjust the amount of denoising strength of the filter. Such information is automatically obtained from the images using a new local noise estimation method. RESULTS: The proposed method was validated and compared with the standard nonlocal means filter on simulated and real MRI data showing an improved performance in all cases. CONCLUSION: The new noise-adaptive method was demonstrated to outperform the standard filter when spatially varying noise is present in the images.

871 citations


Proceedings ArticleDOI
13 Jun 2010
TL;DR: The robustness and effectiveness of the proposed Denoising algorithm on removing mixed noise, e.g. heavy Gaussian noise mixed with impulsive noise, is validated in the experiments and the proposed approach compares favorably against some existing video denoising algorithms.
Abstract: Most existing video denoising algorithms assume a single statistical model of image noise, e.g. additive Gaussian white noise, which often is violated in practice. In this paper, we present a new patch-based video denoising algorithm capable of removing serious mixed noise from the video data. By grouping similar patches in both spatial and temporal domain, we formulate the problem of removing mixed noise as a low-rank matrix completion problem, which leads to a denoising scheme without strong assumptions on the statistical properties of noise. The resulting nuclear norm related minimization problem can be efficiently solved by many recently developed methods. The robustness and effectiveness of our proposed denoising algorithm on removing mixed noise, e.g. heavy Gaussian noise mixed with impulsive noise, is validated in the experiments and our proposed approach compares favorably against some existing video denoising algorithms.

516 citations


Journal ArticleDOI
TL;DR: A general mathematical and experimental methodology to compare and classify classical image denoising algorithms and a nonlocal means (NL-means) algorithm addressing the preservation of structure in a digital image are defined.
Abstract: The search for efficient image denoising methods is still a valid challenge at the crossing of functional analysis and statistics. In spite of the sophistication of the recently proposed methods, most algorithms have not yet attained a desirable level of applicability. All show an outstanding performance when the image model corresponds to the algorithm assumptions but fail in general and create artifacts or remove fine structures in images. The main focus of this paper is, first, to define a general mathematical and experimental methodology to compare and classify classical image denoising algorithms and, second, to propose a nonlocal means (NL-means) algorithm addressing the preservation of structure in a digital image. The mathematical analysis is based on the analysis of the “method noise,” defined as the difference between a digital image and its denoised version. The NL-means algorithm is proven to be asymptotically optimal under a generic statistical image model. The denoising performance of all considered methods is compared in four ways; mathematical: asymptotic order of magnitude of the method noise under regularity assumptions; perceptual-mathematical: the algorithms artifacts and their explanation as a violation of the image model; quantitative experimental: by tables of $L^2$ distances of the denoised version to the original image. The fourth and perhaps most powerful evaluation method is, however, the visualization of the method noise on natural images. The more this method noise looks like a real white noise, the better the method.

445 citations


Journal ArticleDOI
TL;DR: A no-reference metric Q is proposed which is based upon singular value decomposition of local image gradient matrix, and provides a quantitative measure of true image content in the presence of noise and other disturbances, and is used to automatically and effectively set the parameters of two leading image denoising algorithms.
Abstract: Across the field of inverse problems in image and video processing, nearly all algorithms have various parameters which need to be set in order to yield good results. In practice, usually the choice of such parameters is made empirically with trial and error if no “ground-truth” reference is available. Some analytical methods such as cross-validation and Stein's unbiased risk estimate (SURE) have been successfully used to set such parameters. However, these methods tend to be strongly reliant on restrictive assumptions on the noise, and also computationally heavy. In this paper, we propose a no-reference metric Q which is based upon singular value decomposition of local image gradient matrix, and provides a quantitative measure of true image content (i.e., sharpness and contrast as manifested in visually salient geometric features such as edges,) in the presence of noise and other disturbances. This measure 1) is easy to compute, 2) reacts reasonably to both blur and random noise, and 3) works well even when the noise is not Gaussian. The proposed measure is used to automatically and effectively set the parameters of two leading image denoising algorithms. Ample simulated and real data experiments support our claims. Furthermore, tests using the TID2008 database show that this measure correlates well with subjective quality evaluations for both blur and noise distortions.

388 citations


Journal ArticleDOI
TL;DR: In this article, the authors provide a concise survey of the achievements in airframe noise source description and reduction over the last 40 years worldwide and provide examples but do not claim to be complete.
Abstract: With the advent of low noise high bypass ratio turbofan engines airframe noise gained significant importance with respect to the overall aircraft noise impact around airports. Already around 1970 airframe noise, originating from flow around the landing gears and high-lift devices, was recognized as a potential “lower aircraft noise barrier” at approach and landing. Since then, the outcome of extensive acoustic flight tests and aeroacoustic wind tunnel experiments enabled a detailed description and ranking of the major airframe noise sources and the development of noise reduction means. In the last decade advances in numerical and experimental tools led to a better understanding of complex noise source mechanisms. Efficient noise reduction technologies were developed for landing gears while the benefits of high-lift noise reduction means were often compensated by a simultaneous degradation in aerodynamic performance. The focus of this paper is not on the historical sequence of airframe noise research but rather aims to provide a concise survey of the achievements in airframe noise source description and reduction over the last 40 years worldwide. Due to the vast amount of work focused on a variety of airframe noise problems, this review can only provide examples but does not claim to be complete.

360 citations


Journal ArticleDOI
TL;DR: This work formally shows that the minimum variance distortionless response (MVDR) filter is a particular case of the PMWF by properly formulating the constrained optimization problem of noise reduction, and proposes new simplified expressions for thePMWF, the MVDR, and the generalized sidelobe canceller that depend on the signals' statistics only.
Abstract: Several contributions have been made so far to develop optimal multichannel linear filtering approaches and show their ability to reduce the acoustic noise. However, there has not been a clear unifying theoretical analysis of their performance in terms of both noise reduction and speech distortion. To fill this gap, we analyze the frequency-domain (non-causal) multichannel linear filtering for noise reduction in this paper. For completeness, we consider the noise reduction constrained optimization problem that leads to the parameterized multichannel non-causal Wiener filter (PMWF). Our contribution is fivefold. First, we formally show that the minimum variance distortionless response (MVDR) filter is a particular case of the PMWF by properly formulating the constrained optimization problem of noise reduction. Second, we propose new simplified expressions for the PMWF, the MVDR, and the generalized sidelobe canceller (GSC) that depend on the signals' statistics only. In contrast to earlier works, these expressions are explicitly independent of the channel transfer function ratios. Third, we quantify the theoretical gains and losses in terms of speech distortion and noise reduction when using the PWMF by establishing new simplified closed-form expressions for three performance measures, namely, the signal distortion index, the noise reduction factor (originally proposed in the paper titled ldquoNew insights into the noise reduction Wiener filter,rdquo by J. Chen (IEEE Transactions on Audio, Speech, and Language Processing, Vol. 15, no. 4, pp. 1218-1234, Jul. 2006) to analyze the single channel time-domain Wiener filter), and the output signal-to-noise ratio (SNR). Fourth, we analyze the effects of coherent and incoherent noise in addition to the benefits of utilizing multiple microphones. Fifth, we propose a new proof for the a posteriori SNR improvement achieved by the PMWF. Finally, we provide some simulations results to corroborate the findings of this work.

317 citations


Journal ArticleDOI
TL;DR: A set of experiments shows that the proposed method, which is named MIDAL (multiplicative image denoising by augmented Lagrangian), yields state-of-the-art results both in terms of speed and Denoising performance.
Abstract: Multiplicative noise (also known as speckle noise) models are central to the study of coherent imaging systems, such as synthetic aperture radar and sonar, and ultrasound and laser imaging. These models introduce two additional layers of difficulties with respect to the standard Gaussian additive noise scenario: (1) the noise is multiplied by (rather than added to) the original image; (2) the noise is not Gaussian, with Rayleigh and Gamma being commonly used densities. These two features of multiplicative noise models preclude the direct application of most state-of-the-art algorithms, which are designed for solving unconstrained optimization problems where the objective has two terms: a quadratic data term (log-likelihood), reflecting the additive and Gaussian nature of the noise, plus a convex (possibly nonsmooth) regularizer (e.g., a total variation or wavelet-based regularizer/prior). In this paper, we address these difficulties by: (1) converting the multiplicative model into an additive one by taking logarithms, as proposed by some other authors; (2) using variable splitting to obtain an equivalent constrained problem; and (3) dealing with this optimization problem using the augmented Lagrangian framework. A set of experiments shows that the proposed method, which we name MIDAL (multiplicative image denoising by augmented Lagrangian), yields state-of-the-art results both in terms of speed and denoising performance.

315 citations


Journal ArticleDOI
TL;DR: A nonparametric regression method for denoising 3-D image sequences acquired via fluorescence microscopy and an original statistical patch-based framework for noise reduction and preservation of space-time discontinuities are presented.
Abstract: We present a nonparametric regression method for denoising 3-D image sequences acquired via fluorescence microscopy. The proposed method exploits the redundancy of the 3-D+time information to improve the signal-to-noise ratio of images corrupted by Poisson-Gaussian noise. A variance stabilization transform is first applied to the image-data to remove the dependence between the mean and variance of intensity values. This preprocessing requires the knowledge of parameters related to the acquisition system, also estimated in our approach. In a second step, we propose an original statistical patch-based framework for noise reduction and preservation of space-time discontinuities. In our study, discontinuities are related to small moving spots with high velocity observed in fluorescence video-microscopy. The idea is to minimize an objective nonlocal energy functional involving spatio-temporal image patches. The minimizer has a simple form and is defined as the weighted average of input data taken in spatially-varying neighborhoods. The size of each neighborhood is optimized to improve the performance of the pointwise estimator. The performance of the algorithm (which requires no motion estimation) is then evaluated on both synthetic and real image sequences using qualitative and quantitative criteria.

299 citations


Journal ArticleDOI
TL;DR: The main advantage of this object-based method is its robustness to background artefacts such as ghosting, and within the validation on real data, the proposed method obtained very competitive results compared to the methods under study.

229 citations


Journal ArticleDOI
TL;DR: Using chaotic Lorenz data and calculating root-mean-square-error, Lyapunov exponent, and correlation dimension, it is shown that the adaptive algorithm more effectively reduces noise in the Chaos Lorenz system than wavelet denoising with three different thresholding choices.
Abstract: Time series measured in real world is often nonlinear, even chaotic. To effectively extract desired information from measured time series, it is important to preprocess data to reduce noise. In this Letter, we propose an adaptive denoising algorithm. Using chaotic Lorenz data and calculating root-mean-square-error, Lyapunov exponent, and correlation dimension, we show that our adaptive algorithm more effectively reduces noise in the chaotic Lorenz system than wavelet denoising with three different thresholding choices. We further analyze an electroencephalogram (EEG) signal in sleep apnea and show that the adaptive algorithm again more effectively reduces the Electrocardiogram (ECG) and other types of noise contaminated in EEG than wavelet approaches.

214 citations


Journal ArticleDOI
TL;DR: A novel speckle noise reduction algorithm that projects the imaging data into the logarithmic space and a general Bayesian least squares estimate of the noise-free data is found using a conditional posterior sampling approach is developed.
Abstract: An important image post-processing step for optical coherence tomography (OCT) images is speckle noise reduction. Noise in OCT images is multiplicative in nature and is difficult to suppress due to the fact that in addition the noise component, OCT speckle also carries structural information about the imaged object. To address this issue, a novel speckle noise reduction algorithm was developed. The algorithm projects the imaging data into the logarithmic space and a general Bayesian least squares estimate of the noise-free data is found using a conditional posterior sampling approach. The proposed algorithm was tested on a number of rodent (rat) retina images acquired in-vivo with an ultrahigh resolution OCT system. The performance of the algorithm was compared to that of the state-of-the-art algorithms currently available for speckle denoising, such as the adaptive median, maximum a posteriori (MAP) estimation, linear least squares estimation, anisotropic diffusion and wavelet-domain filtering methods. Experimental results show that the proposed approach is capable of achieving state-of-the-art performance when compared to the other tested methods in terms of signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), edge preservation, and equivalent number of looks (ENL) measures. Visual comparisons also show that the proposed approach provides effective speckle noise suppression while preserving the sharpness and improving the visibility of morphological details, such as tiny capillaries and thin layers in the rat retina OCT images.

Journal ArticleDOI
TL;DR: The results indicate that modulation frame durations, provide a good compromise between different types of spectral distortions, namely musical noise and temporal slurring, and given a proper selection of modulation frame duration, the proposed modulation spectral subtraction does not suffer from musical noise artifacts typically associated with acoustic spectral subtracted.

Journal ArticleDOI
17 Jun 2010-Sensors
TL;DR: Results showed that high noise reduction is the major advantage of the EEMD based filter, especially on arrhythmia ECGs.
Abstract: A novel noise filtering algorithm based on ensemble empirical mode decomposition (EEMD) is proposed to remove artifacts in electrocardiogram (ECG) traces. Three noise patterns with different power—50 Hz, EMG, and base line wander – were embedded into simulated and real ECG signals. Traditional IIR filter, Wiener filter, empirical mode decomposition (EMD) and EEMD were used to compare filtering performance. Mean square error between clean and filtered ECGs was used as filtering performance indexes. Results showed that high noise reduction is the major advantage of the EEMD based filter, especially on arrhythmia ECGs.

Journal ArticleDOI
TL;DR: The existence of a minimizer of the authors' specialized criterion being proven, the convergence of the minimization scheme is demonstrated, and the obtained numerical results clearly outperform the main alternative methods especially for images containing tricky geometrical structures.
Abstract: We address the denoising of images contaminated with multiplicative noise, e.g. speckle noise. Classical ways to solve such problems are filtering, statistical (Bayesian) methods, variational methods, and methods that convert the multiplicative noise into additive noise (using a logarithmic function), apply a variational method on the log data or shrink their coefficients in a frame (e.g. a wavelet basis), and transform back the result using an exponential function. We propose a method composed of several stages: we use the log-image data and apply a reasonable under-optimal hard-thresholding on its curvelet transform; then we apply a variational method where we minimize a specialized hybrid criterion composed of an ? 1 data-fidelity to the thresholded coefficients and a Total Variation regularization (TV) term in the log-image domain; the restored image is an exponential of the obtained minimizer, weighted in a such way that the mean of the original image is preserved. Our restored images combine the advantages of shrinkage and variational methods and avoid their main drawbacks. Theoretical results on our hybrid criterion are presented. For the minimization stage, we propose a properly adapted fast scheme based on Douglas-Rachford splitting. The existence of a minimizer of our specialized criterion being proven, we demonstrate the convergence of the minimization scheme. The obtained numerical results clearly outperform the main alternative methods especially for images containing tricky geometrical structures.

Journal ArticleDOI
TL;DR: The performance evaluation supports the theoretical analysis and demonstrates the tradeoff between speech dereverberation and noise reduction, and shows that maximum noise reduction is achieved when the MVDR beamformer is used for noise reduction only.
Abstract: The minimum variance distortionless response (MVDR) beamformer, also known as Capon's beamformer, is widely studied in the area of speech enhancement. The MVDR beamformer can be used for both speech dereverberation and noise reduction. This paper provides new insights into the MVDR beamformer. Specifically, the local and global behavior of the MVDR beamformer is analyzed and novel forms of the MVDR filter are derived and discussed. In earlier works it was observed that there is a tradeoff between the amount of speech dereverberation and noise reduction when the MVDR beamformer is used. Here, the tradeoff between speech dereverberation and noise reduction is analyzed thoroughly. The local and global behavior, as well as the tradeoff, is analyzed for different noise fields such as, for example, a mixture of coherent and non-coherent noise fields, entirely non-coherent noise fields and diffuse noise fields. It is shown that maximum noise reduction is achieved when the MVDR beamformer is used for noise reduction only. The amount of noise reduction that is sacrificed when complete dereverberation is required depends on the direct-to-reverberation ratio of the acoustic impulse response between the source and the reference microphone. The performance evaluation supports the theoretical analysis and demonstrates the tradeoff between speech dereverberation and noise reduction. When desiring both speech dereverberation and noise reduction, the results also demonstrate that the amount of noise reduction that is sacrificed decreases when the number of microphones increases.

Proceedings ArticleDOI
03 Dec 2010
TL;DR: An automatic setting is proposed to select parameters based on the minimization of the estimated risk (mean square error) of the MSE for NL means with Poisson noise and Newton's method to find the optimal parameters in few iterations.
Abstract: An extension of the non local (NL) means is proposed for images damaged by Poisson noise. The proposed method is guided by the noisy image and a pre-filtered image and is adapted to the statistics of Poisson noise. The influence of both images can be tuned using two filtering parameters. We propose an automatic setting to select these parameters based on the minimization of the estimated risk (mean square error). This selection uses an estimator of the MSE for NL means with Poisson noise and Newton's method to find the optimal parameters in few iterations.

Journal ArticleDOI
TL;DR: The tradeoff that has to be made between noise reduction and interference rejection is theoretically demonstrated and a new relationship between both filters in which the MVDR is decomposed into the LCMV and a matched filter (MVDR solution in the absence of interference).
Abstract: In real-world environments, the signals captured by a set of microphones in a speech communication system are mixtures of the desired signal, interference, and ambient noise. A promising solution for proper speech acquisition (with reduced noise and interference) in this context consists in using the linearly constrained minimum variance (LCMV) beamformer to reject the interference, reduce the overall mixture energy, and preserve the target signal. The minimum variance distortionless response beamformer (MVDR) is also commonly known to reduce the interference-plus-noise energy without distorting the desired signal. In either case, it is of paramount importance to accurately quantify the achieved noise and interference reduction. Indeed, it is quite reasonable to ask, for instance, about the price that has to be paid in order to achieve total removal of the interference without distorting the target signal when using the LCMV. Besides, it is fundamental to understand the effect of the MVDR on both noise and interference. In this correspondence, we investigate the performance of the MVDR and LCMV beamformers when the interference and ambient noise coexist with the target source. We demonstrate a new relationship between both filters in which the MVDR is decomposed into the LCMV and a matched filter (MVDR solution in the absence of interference). Both components are properly weighted to achieve maximum interference-plus-noise reduction. We investigate the performance of the MVDR, LCMV, and matched filters and elaborate new closed-form expressions for their output signal-to-interference ratio (SIR) and output signal-to-noise ratio (SNR). We theoretically demonstrate the tradeoff that has to be made between noise reduction and interference rejection. In fact, the total removal of the interference may severely amplify the residual ambient noise. Conversely, totally focussing on noise reduction leads to increased level of residual interference. The proposed study is finally supported by several numerical examples.

Journal ArticleDOI
TL;DR: In this article, a single step algorithm based on a mixed optical/mechanical cost function was proposed to identify a non-linear consitutive law, where no boundary conditions are needed.
Abstract: Constitutive parameter identification has been greatly improved by the achievement of full-field measurements. In this context, noise sensitivity has been shown to be of great importance. It is crucial to incorporate noise sensitivity minimization in the design of robust identification procedures. In this paper, we investigate noise sensitivity reduction techniques for constitutive parameter identification based on Finite Element Model Updating. After examining the existing strategies, we propose a single step algorithm based on a mixed optical/mechanical cost function. The key point of this novel procedure is that no boundary conditions are needed. A first example on a real case illustrates the advantages of the proposed methodology in terms of noise sensitivity. A second example shows its capabilities to identify a non-linear consitutive law. Copyright © 2010 John Wiley & Sons, Ltd.

Journal ArticleDOI
TL;DR: This correspondence establishes a new expression for speech presence probability when an array of microphones with an arbitrary geometry is used and proposes a new proposed multichannel approach that can significantly increase the detection accuracy.
Abstract: The knowledge of the target speech presence probability in a mixture of signals captured by a speech communication system is of paramount importance in several applications including reliable noise reduction algorithms. In this correspondence, we establish a new expression for speech presence probability when an array of microphones with an arbitrary geometry is used. Our study is based on the assumption of the Gaussian statistical model for all signals and involves the noise and noisy data statistics only. In comparison with the single-channel case, the new proposed multichannel approach can significantly increase the detection accuracy. In particular, when the additive noise is spatially coherent, perfect speech presence detection is theoretically possible, while when the noise is spatially white, a coherent summation of speech components is performed to allow for enhanced speech presence probability estimation.

Journal ArticleDOI
TL;DR: A proposal for impulse statistics estimation under severe conditions (i.e., for very rare impulse events, based on impulse detection over corrupted OFDM symbols) is made and a general scheme of automatic impulse mitigation is proposed, according to the disturbance ratio of the environment.
Abstract: In this paper, we address the general issue of asynchronous impulsive noise mitigation and its application over coded power-line communications (PLC). As is well known, PLC channels usually suffer from significant degradation due to impulsive interference generated by electrical appliances. The use of a level limiter is a simple and intuitive technique, widely used to mitigate the noxious effect of impulsive noise in these channels. However, the determination of the clipping threshold remains, most of the time, empirical, and the impulse statistics are usually assumed to be known. In a previous study, we proposed an original threshold determination based on signal detection theory, using the well-known false alarm and good detection tradeoff. Here, we further investigate the proposed optimization method in closed form. We compare two tradeoff criteria by means of a receiver operating curves analysis. We clearly show that the resulting decision fits well with encountered bit-error rate performances. We also evaluate the influence of impulsive noise statistics estimation on the performance of the proposed approach. To do so, we assess the robustness of the classical method of moments on Gaussian mixture estimation. It clearly appears that the estimation method is reliable for reasonable values of the impulse occurrence. As one of the main results of this paper, we make a proposal for impulse statistics estimation under ?severe? conditions (i.e., for very rare impulse events, based on impulse detection over corrupted OFDM symbols). Subsequently, we propose a general scheme of automatic impulse mitigation, according to the disturbance ratio of the environment. Finally, the performance of the approach is evaluated over the Home-Plug AV (HPAV) physical layer.

Journal ArticleDOI
TL;DR: Two design variables are selected to optimize the rotor shape especially geometry of flux barrier, and response surface methodology (RSM) is applied as an optimization method and quantity of the vibration and noise of optimized model are compared with prototype model and the noise measured in the vehicle engine bay was reduced.
Abstract: Integrated starter and generator (ISG) is working as starter after vehicle idle stopping and generator during vehicle driving. When ISG works as starter to operate engine, ISG produces maximum power at the operating speed 4000 rpm to remove vibration of initial starting of the engine. In that time ISG produces whine noise which can be detected inside a vehicle. There are two methods to reduce the vibration and noise. One is improving the stiffness of stator and the other is reduction of electromagnetic exciting force. Although improvement of stiffness using mechanical design is utilized effectively to reduce whine noise, the electromagnetic design using the reduction of exciting forces is more reasonable in the same motor size. Because ISG produces torque ripple, radial force and tangential force on operating condition, this paper deals with the reduction design of electromagnetic exciting forces which affect the noise and vibration. Two design variables are selected to optimize the rotor shape especially geometry of flux barrier, and response surface methodology (RSM) is applied as an optimization method. Finally, quantity of the vibration and noise of optimized model are compared with prototype model, and the noise measured in the vehicle engine bay was reduced from 27 dB(A) to 25 dB(A).

Journal ArticleDOI
TL;DR: In the proposed method median filter is modified by adding more features, and the quality of the output images is measured by the statistical quantity measures: peak signal-to-noise ratio (PSNR), signal- to-no noise ratio (SNR) and root mean square error (RMSE).
Abstract: In medical image processing, medical images are corrupted by different type of noises. It is very important to obtain precise images to facilitate accurate observations for the given application. Removing of noise from medical images is now a very challenging issue in the field of medical image processing. Most well known noise reduction methods, which are usually based on the local statistics of a medical image, are not efficient for medical image noise reduction. This paper presents an efficient and simple method for noise reduction from medical images. In the proposed method median filter is modified by adding more features. Experimental results are also compared with the other three image filtering algorithms. The quality of the output images is measured by the statistical quantity measures: peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR) and root mean square error (RMSE). Experimental results of magnetic resonance (MR) image and ultrasound image demonstrate that the proposed algorithm is comparable to popular image smoothing algorithms. Key words : Magnetic resonance image; Ultrasound image; PSNR; SNR; RMSE. © 2011 JSR Publications. ISSN: 2070-0237 (Print); 2070-0245 (Online). All rights reserved. doi:10.3329/jsr.v3i1.5544 J. Sci. Res. 3 (1), 81-89 (2011)

Journal ArticleDOI
TL;DR: A novel filter based on the NLM filter is proposed to improve the denoising effect and achieves betterDenoising performance over the other filters being compared.

Patent
22 Dec 2010
TL;DR: In this paper, a system for actively reducing noise at a listening point, including an earphone housing, a transmitting transducer, a receiving transducers and a controller, is described.
Abstract: A system for actively reducing noise at a listening point, includes an earphone housing, a transmitting transducer, a receiving transducer and a controller. The transmitting transducer converts a first electric signal into a first acoustic signal, and radiates the first acoustic signal along a first acoustic path having a first transfer characteristic and along a second acoustic path having a second transfer characteristic. The receiving transducer converts the first acoustic signal and ambient noise into a second electrical signal. The controller compensates for the ambient noise by providing a noise reducing electrical signal to the transmitting transducer. The noise reducing electrical signal is derived from a filtered electrical signal that is provided by filtering the second electrical signal with a third transfer characteristic. The second and the third transfer characteristics together model the first transfer characteristic.

Journal ArticleDOI
TL;DR: In this article, a fully automatic bilateral filter tailored to ultrasound images is proposed that is able to tackle the multiplicative behavior modulating the smoothing strength with respect to local statistics, yielding a more homogeneous smoothing.
Abstract: Speckle noise negatively affects medical ultrasound image shape interpretation and boundary detec- tion. Speckle removal filters are widely used to selectively remove speckle noise without destroying important image features to enhance object boundaries. In this article, a fully automatic bilateral filter tailored to ultrasound images is proposed. The edge preservation property is obtained by embedding noise statistics in the filter frame- work. Consequently, the filter is able to tackle the multiplicative behavior modulating the smoothing strength with respect to local statistics. The in silico experiments clearly showed that the speckle reducing bilateral filter (SRBF) has superior performances to most of the state of the art filtering methods. The filter is tested on 50 in vivo US images and its influence on a segmentation task is quantified. The results using SRBF filtered data sets show a supe- rior performance to using oriented anisotropic diffusion filtered images. This improvement is due to the adaptive support of SRBF and the embedded noise statistics, yielding a more homogeneous smoothing. SRBF results in a fully automatic, fast and flexible algorithm potentially suitable in wide ranges of speckle noise sizes, for different medical applications (IVUS, B-mode, 3-D matrix array US). (E-mail: balocco.simone@gmail.com) � 2010 World Federation for Ultrasound in Medicine & Biology.

Journal ArticleDOI
TL;DR: The proposed gain-peaking technique results in better wideband noise canceling and quick gain roll-off outside the desired signal band to reject interference.
Abstract: This paper presents a compact 0.18-?m CMOS wideband gain-flattened low noise amplifier (LNA). The low noise characteristic of the LNA is achieved by the noise canceling technique and the gain flatness is enhanced by the gate-inductive gain-peaking technique. In addition to extending flat-gain bandwidth, the proposed gain-peaking technique results in better wideband noise canceling and quick gain roll-off outside the desired signal band to reject interference. Without using any passive inductor, the core size of the fully-integrated CMOS LNA circuit is only 145 ? m × 247 ? m. The measured gain and noise figure of the CMOS LNA are 16.4 dB and 2.1 dB, respectively. The gain variation of the LNA is ±0.4 dB from 50 to 900 MHz. Operated at 1.8 V, the chip consumes 14.4 mW of power.

Journal ArticleDOI
TL;DR: In this article, the daily closing prices of several stock market indices are examined to analyse whether noise reduction matters in measuring dependencies of the financial series, and the effect of noise reduction on the linear and nonlinear measure of dependencies is considered.
Abstract: The daily closing prices of several stock market indices are examined to analyse whether noise reduction matters in measuring dependencies of the financial series. We consider the effect of noise reduction on the linear and nonlinear measure of dependencies. We also use singular spectrum analysis as a powerful method for filtering financial series. We compare the results with those obtained by ARMA and GARCH models as linear and nonlinear methods for filtering the series. We also examine the findings on an artificial data set namely the Henon map.

Proceedings ArticleDOI
18 Mar 2010
TL;DR: This paper presents a cost-effective low noise CMOS image sensor readout chain using pseudo-multiple sampling technique that reduces temporal noise of pixel and readout circuit by sampling the same pixel repeatedly and processing (generally averaging) the sampled data.
Abstract: The noise performance of CMOS image sensors has improved significantly. The most popular way to reduce readout circuit noise is amplifying pixel output using a preamplifier at the foremost stage of readout chain to suppress the noise of following readout chains in high analog gain [1–3]. Another approach is multiple sampling which can reduce temporal noise of pixel and readout circuit by sampling the same pixel repeatedly and processing (generally averaging) the sampled data [4, 5]. However, both approaches require additional circuitry in the column readout chain which requires extra silicon area and power consumption. Furthermore, it is hard to implement a decent per-column amplifier in a small pixel pitch sensor, such as 1.4µm pixel, because of narrow layout space. In addition, the second approach requires longer readout time proportional to the number of samples. This paper presents a cost-effective low noise CMOS image sensor readout chain using pseudo-multiple sampling technique.

Journal ArticleDOI
TL;DR: A two-phase image restoration method based upon total variation regularization combined with an L1-data-fitting term for impulse noise removal and deblurring that is significantly advance over several state-of-the-art techniques with respect to restoration capability and computational efficiency is proposed.
Abstract: A two-phase image restoration method based upon total variation regularization combined with an L1-data-fitting term for impulse noise removal and deblurring is proposed. In the first phase, suitable noise detectors are used for identifying image pixels contaminated by noise. Then, in the second phase, based upon the information on the location of noise-free pixels, images are deblurred and denoised simultaneously. For efficiency reasons, in the second phase a superlinearly convergent algorithm based upon Fenchel-duality and inexact semismooth Newton techniques is utilized for solving the associated variational problem. Numerical results prove the new method to be a significantly advance over several state-of-the-art techniques with respect to restoration capability and computational efficiency.

Journal ArticleDOI
TL;DR: A significant improvement in the signal-to-interference-plus-noise ratio is achieved using the proposed algorithm and it is shown that the phase noise vector can be estimated by maximizing a constrained quadratic form without requiring knowledge of the channel vector.
Abstract: A practical approach for detecting packet-based orthogonal-frequency-division multiplexing (OFDM) signals in the presence of phase noise is presented. An OFDM packet consists of several OFDM symbols with full-pilot symbols at the beginning followed by consecutive data symbols. Based on the full-pilot OFDM symbol, a frequency-domain joint phase noise and channel vector estimator is first derived. It is shown that the phase noise vector can be estimated by maximizing a constrained quadratic form without requiring knowledge of the channel vector. This estimated phase noise vector is then used to compute the least squares channel estimator. Assuming that the channel is constant during each packet, the estimated channel is used in subsequent data OFDM symbols for equalization and data detection. Since phase noise changes from one OFDM symbol to the next, the scattered pilots in each data OFDM symbol are used to non-iteratively estimate and mitigate the phase noise induced interference. A significant improvement in the signal-to-interference-plus-noise ratio is achieved using the proposed algorithm.