Showing papers on "Impulse noise published in 2013"
TL;DR: Three iterative algorithms with different complexity vs. performance trade-offs are proposed to mitigate asynchronous impulsive noise, exploit its sparsity in the time domain, and apply sparse Bayesian learning methods to estimate and subtract the noise impulses.
Abstract: Asynchronous impulsive noise and periodic impulsive noises limit communication performance in OFDM powerline communication systems. Conventional OFDM receivers that assume additive white Gaussian noise experience degradation in communication performance in impulsive noise. Alternate designs assume a statistical noise model and use the model parameters in mitigating impulsive noise. These receivers require training overhead for parameter estimation, and degrade due to model and parameter mismatch. To mitigate asynchronous impulsive noise, we exploit its sparsity in the time domain, and apply sparse Bayesian learning methods to estimate and subtract the noise impulses. We propose three iterative algorithms with different complexity vs. performance trade-offs: (1) we utilize the noise projection onto null and pilot tones; (2) we add the information in the date tones to perform joint noise estimation and symbol detection; (3) we use decision feedback from the decoder to further enhance the accuracy of noise estimation. These algorithms are also embedded in a time-domain block interleaving OFDM system to mitigate periodic impulsive noise. Compared to conventional OFDM receivers, the proposed methods achieve SNR gains of up to 9 dB in coded and 10 dB in uncoded systems in asynchronous impulsive noise, and up to 6 dB in coded systems in periodic impulsive noise.
244 citations
TL;DR: Wang et al. as discussed by the authors proposed two methods based on blind inpainting and $\ell_0$ minimization that can simultaneously find the damaged pixels and restore the image by iteratively restoring the image and updating the set of damaged pixels.
Abstract: This article studies the problem of image restoration of observed images corrupted by impulse noise and mixed Gaussian impulse noise. Since the pixels damaged by impulse noise contain no information about the true image, how to find this set correctly is a very important problem. We propose two methods based on blind inpainting and $\ell_0$ minimization that can simultaneously find the damaged pixels and restore the image. By iteratively restoring the image and updating the set of damaged pixels, these methods have better performance than other methods, as shown in the experiments. In addition, we provide convergence analysis for these methods; these algorithms will converge to coordinatewise minimum points. In addition, they will converge to local minimum points (or with probability one) with some modifications in the algorithms.
135 citations
TL;DR: Two methods based on blind inpainting and $\ell_0$ minimization are proposed that can simultaneously find the damaged pixels and restore the image by iteratively restoring the image and updating the set of damaged pixels.
Abstract: This article studies the problem of image restoration of observed images corrupted by impulse noise and mixed Gaussian impulse noise Since the pixels damaged by impulse noise contain no information about the true image, how to find this set correctly is a very important problem We propose two methods based on blind inpainting and $\ell_0$ minimization that can simultaneously find the damaged pixels and restore the image By iteratively restoring the image and updating the set of damaged pixels, these methods have better performance than other methods, as shown in the experiments In addition, we provide convergence analysis for these methods, these algorithms will converge to coordinatewise minimum points In addition, they will converge to local minimum points (or with probability one) with some modifications in the algorithms
129 citations
TL;DR: Rather than optimizing the likelihood functional derived from a mixture distribution, this paper presents a new weighting data fidelity function, which has the same minimizer as the original likelihood functional but is much easier to optimize.
Abstract: This paper proposes a general weighted l2-l0 norms energy minimization model to remove mixed noise such as Gaussian-Gaussian mixture, impulse noise, and Gaussian-impulse noise from the images. The approach is built upon maximum likelihood estimation framework and sparse representations over a trained dictionary. Rather than optimizing the likelihood functional derived from a mixture distribution, we present a new weighting data fidelity function, which has the same minimizer as the original likelihood functional but is much easier to optimize. The weighting function in the model can be determined by the algorithm itself, and it plays a role of noise detection in terms of the different estimated noise parameters. By incorporating the sparse regularization of small image patches, the proposed method can efficiently remove a variety of mixed or single noise while preserving the image textures well. In addition, a modified K-SVD algorithm is designed to address the weighted rank-one approximation. The experimental results demonstrate its better performance compared with some existing methods.
112 citations
TL;DR: A new impulsive noise model is introduced which is, in fact, a Hidden Markov Model, whose realizations exactly follow a Middleton Class A distribution and optimum and suboptimum detections for a coded transmission impaired by the proposed noise model are evaluated.
Abstract: Transmission over channels impaired by impulsive noise, such as in power substations, calls for peculiar mitigation techniques at the receiver side in order to cope with signal deterioration. For these techniques to be effective, a reliable noise model is usually required. One of the widely accepted models is the Middleton Class A, which presents the twofold advantage to be canonical (i.e., invariant of the particular physical source mechanisms) and to exhibit a simple probability density function (PDF) that only depends on three physical parameters, making this model very attractive. However, such a model fails in replicating bursty impulsive noise, where each impulse spans over several consecutive noise samples, as usually observed (e.g., in power substations). Indeed, the Middleton Class A model only deals with amplitude or envelope statistics. On the other hand, for models based on Markov chains, although they reproduce the bursty nature of impulses, the determination of the suitable number of states and the noise distribution associated with each state can be challenging. In this paper, 1) we introduce a new impulsive noise model which is, in fact, a Hidden Markov Model, whose realizations exactly follow a Middleton Class A distribution and 2) we evaluate optimum and suboptimum detections for a coded transmission impaired by the proposed noise model.
100 citations
TL;DR: The proposed median filter restores corrupted images with 1-99% levels of salt-and-pepper impulse noise to satisfactory ones by intuitively and simply recognizing impulse noises, while keeping the others intact as nonnoises.
96 citations
TL;DR: Experimental evaluation shows the effectiveness of the proposed modifications to the filtering step of the BDND algorithm in producing sharper images than theBDND algorithm.
Abstract: Switching median filters are known to outperform standard median filters in the removal of impulse noise due to their capability of filtering candidate noisy pixels and leaving other pixels intact. The boundary discriminative noise detection (BDND) is one powerful example in this class of filters. However, there are some issues related to the filtering step in the BDND algorithm that may degrade its performance. In this paper, we propose two modifications to the filtering step of the BDND algorithm to address these issues. Experimental evaluation shows the effectiveness of the proposed modifications in producing sharper images than the BDND algorithm.
86 citations
TL;DR: A diffusion least-mean P-power (LMP) algorithm is proposed for distributed estimation in alpha-stable noise environments, which is one of the widely used models that appears in various environments.
Abstract: A diffusion least-mean P-power (LMP) algorithm is proposed for distributed estimation in alpha-stable noise environments, which is one of the widely used models that appears in various environments. Compared with the diffusion least-mean squares algorithm, better performance is obtained for the diffusion LMP methods when the noise is with alpha-stable distribution.
80 citations
TL;DR: An efficient denoising scheme and its VLSI architecture for the removal of random-valued impulse noise is proposed and can obtain better performances in terms of both quantitative evaluation and visual quality than the previous lower complexity methods.
Abstract: Images are often corrupted by impulse noise in the procedures of image acquisition and transmission. In this paper, we propose an efficient denoising scheme and its VLSI architecture for the removal of random-valued impulse noise. To achieve the goal of low cost, a low-complexity VLSI architecture is proposed. We employ a decision-tree-based impulse noise detector to detect the noisy pixels, and an edge-preserving filter to reconstruct the intensity values of noisy pixels. Furthermore, an adaptive technology is used to enhance the effects of removal of impulse noise. Our extensive experimental results demonstrate that the proposed technique can obtain better performances in terms of both quantitative evaluation and visual quality than the previous lower complexity methods. Moreover, the performance can be comparable to the higher,- complexity methods. The VLSI architecture of our design yields a processing rate of about 200 MHz by using TSMC 0.18 μm technology. Compared with the state-of-the-art techniques, this work can reduce memory storage by more than 99 percent. The design requires only low computational complexity and two line memory buffers. Its hardware cost is low and suitable to be applied to many real-time applications.
75 citations
TL;DR: This brief introduces the concept of a step-size scaler by investigating and modifying the tanh cost function for adaptive filtering with impulsive measurement noise, and shows the improvement of robustness against impulsive noise.
Abstract: This brief introduces the concept of a step-size scaler by investigating and modifying the tanh cost function for adaptive filtering with impulsive measurement noise. The step-size scaler instantly scales down the step size of gradient-based adaptive algorithms whenever impulsive measurement noise appears, which eliminates a possibility of updating weight vector estimates based on wrong information due to impulsive noise. The most attractive feature of the step-size scaler is that this is easily applicable to various gradient-based adaptive algorithms. Several representative gradient-based adaptive algorithms are performed without or with the step-size scaler in impulsive-noise environments, which shows the improvement of robustness against impulsive noise.
70 citations
TL;DR: In this article, the authors consider orthogonal frequency division multiplexing (OFDM)-based power-line communications and propose a method for finding the optimal blanking threshold (OBT) without requiring any knowledge about the IN.
Abstract: Impulsive noise (IN) is one of the most dominant factors responsible for degrading the performance of power-line communication systems. One of the common techniques for mitigating IN is blanking which is applied at the front end of the receiver to zero the incoming signal when it exceeds a certain threshold. Determining the optimal blanking threshold (OBT) is, however, key for achieving the best performance. Most reported work to find the OBT is based on the availability of the long-term characteristics of IN at the receiver. In this paper, we consider orthogonal frequency-division multiplexing (OFDM)-based power-line communications and propose a method for finding the OBT without requiring any knowledge about the IN. We show that there is a direct relationship between the OBT and the peak-to-average power value of the OFDM symbol and utilize it to identify the OBT. The results reveal that the proposed technique not only eliminates the need to prior knowledge about the characteristics of IN but also achieves a gain between 0.5-2.5 dB depending on the accuracy of the signal peak-to-average estimate. It will also be shown how the performance of the proposed method can be further enhanced by employing some basic signal per processing at the transmitter.
TL;DR: Simulation results demonstrate that the Lorentzian-based IHT algorithm significantly outperform commonly employed sparse reconstruction techniques in impulsive environments, while providing comparable performance in less demanding, light-tailed environments.
Abstract: Commonly employed reconstruction algorithms in compressed sensing (CS) use the L2 norm as the metric for the residual error. However, it is well-known that least squares (LS) based estimators are highly sensitive to outliers present in the measurement vector leading to a poor performance when the noise no longer follows the Gaussian assumption but, instead, is better characterized by heavier-than-Gaussian tailed distributions. In this paper, we propose a robust iterative hard Thresholding (IHT) algorithm for reconstructing sparse signals in the presence of impulsive noise. To address this problem, we use a Lorentzian cost function instead of the L2 cost function employed by the traditional IHT algorithm. We also modify the algorithm to incorporate prior signal information in the recovery process. Specifically, we study the case of CS with partially known support. The proposed algorithm is a fast method with computational load comparable to the LS based IHT, whilst having the advantage of robustness against heavy-tailed impulsive noise. Sufficient conditions for stability are studied and a reconstruction error bound is derived. We also derive sufficient conditions for stable sparse signal recovery with partially known support. Theoretical analysis shows that including prior support information relaxes the conditions for successful reconstruction. Simulation results demonstrate that the Lorentzian-based IHT algorithm significantly outperform commonly employed sparse reconstruction techniques in impulsive environments, while providing comparable performance in less demanding, light-tailed environments. Numerical results also demonstrate that the partially known support inclusion improves the performance of the proposed algorithm, thereby requiring fewer samples to yield an approximate reconstruction.
20 Jun 2013
TL;DR: A combined approach for the suppression of high density impulse noise followed by the contrast enhancement of mammographic breast lesions is presented.
Abstract: The categorisation of breast lesions as either benign or malignant if done accurately; would greatly reduce the mortality rate due to breast cancer all around the world. But the process is very challenging as the noise particles are generally detected as false positives which can be minimised only by the selective enhancement of the features of the mammogram indicative of cancer. This paper presents a combined approach for the suppression of high density impulse noise followed by the contrast enhancement of mammographic breast lesions. The application of the proposed denoising method is done iteratively to effectively remove the impulse noise. The non-linear enhancement operator with multistate adaptive gain is then passed over the denoised image for mammographic feature enhancement. Results of simulation show a marked improvement in the restoration quality of the contaminated images, preserving the finer features at high noise densities requiring not more than three iterations. With the optimal tuning of parameters, the enhancement of the targeted region of interest (ROI) is obtained with reasonable suppression of the background.
TL;DR: A Bayesian framework and its particle filtering implementation for DOA tracking in the presence of complex symmetric alpha-stable noise process are developed and the results show that the proposed algorithm significantly outperforms the existing PF tracking approach and the traditional localization approaches in DOA estimation.
Abstract: NonGaussian impulsive noises distort the source signal and cause problems for direction of arrival (DOA) estimation of an acoustic source. In this paper, a Bayesian framework and its particle filtering (PF) implementation for DOA tracking in the presence of complex symmetric alpha-stable noise process are developed. A constant velocity model is employed to model the source dynamics, and spatial spectra are exploited to formulate a pseudo likelihood of particles. Since the second-order statistics of alpha-stable processes do not exist, the fractional lower order moment matrix of the received data is used to replace the covariance matrix in calculating the spatial spectra. The noise usually spreads and distorts the mainlobe of the likelihood function and the particles cannot be weighted accurately. Hence, the likelihood function is exponentially weighted to emphasize the particles in a high likelihood area and thus enhance the resampling efficiency. The performance of the proposed tracking algorithm is extensively studied under simulated alpha-stable noise environments. The results show that the proposed algorithm significantly outperforms the existing PF tracking approach and the traditional localization approaches in DOA estimation.
TL;DR: The results suggest that DL-INR has a better ability to suppress impulse noise than other six algorithms and can produce restored images with higher peak signal-to-noise ratio (PSNR).
10 Jun 2013
TL;DR: This paper derives the analytical expression for probability of error considering the presence of GG noise in the UWA channel, and considers three different communication systems using BPSK, QPSK and M-ary PAM constellations, and observes that if noise kurtosis is greater than the Gaussian k Kurtosis, the system performance degrades.
Abstract: Noise in an underwater acoustic (UWA) channel does not necessarily follow Gaussian statistics, especially in a shallow water environment which is dominated by impulsive noise sources. However most of the receivers are designed with the assumption that the channel noise is additive white Gaussian (AWGN). Such receivers may not be the optimum ones to deal with the non-Gaussian UWA noise. Several non-Gaussian statistics have been proposed in previous literature to model UWA noise among which the Generalized Gaussian (GG) model has been very popular due to its flexible parametric form. However, to the best of our knowledge, no analytical error analysis with the assumption of channel noise being generalized Gaussian has yet been reported. In this paper, we derive the analytical expression for probability of error considering the presence of GG noise in the UWA channel. We also try to study the performance of an AWGN receiver in the presence of non-Gaussian noise. We consider three different communication systems using BPSK, QPSK and M-ary PAM constellations, and observe, for all three of them, that if noise kurtosis is greater than the Gaussian kurtosis, the system performance degrades. Thus here is an initial phase of designing UWA system with GG noise assumptions instead of the traditional Gaussian receiver. It is still a matter of argument, due to all possible redesign complexities, whether it is worth taking all the redesign complexities or the performance degradation can be tolerated as per the requirements of system application.
TL;DR: A modification of standard compressive sensing algorithms for sparse signal reconstruction in the presence of impulse noise is proposed based on the L-estimate statistics which is used to provide appropriate initial conditions that lead to improved performance and efficient convergence of the reconstruction algorithms.
TL;DR: In this article, a bias-compensated error-modified normalised least-mean-square algorithm is proposed to improve robustness against impulsive measurement noise, and an unbiasedness criterion is introduced to eliminate the bias due to noisy inputs in an impulsive measuring noise environment.
Abstract: A bias-compensated error-modified normalised least-mean-square algorithm is proposed. The proposed algorithm employs nonlinearity to improve robustness against impulsive measurement noise, and introduces an unbiasedness criterion to eliminate the bias due to noisy inputs in an impulsive measurement noise environment. To eliminate the bias properly, a new estimation method for the input noise variance is also derived. Simulations in a system identification context show that the proposed algorithm outperforms the other algorithms because of the improved adaptability to impulsive measurement noise and input noise in the system.
TL;DR: An algorithm for single-channel transient interference suppression that exploits the unique spectral structure of the transients along with their impulsive temporal nature to distinct them from speech.
Abstract: A transient is an abrupt or impulsive sound followed by decaying oscillations, e.g., keyboard typing and door knocking. Such sounds often arise as interference in everyday applications, e.g., hearing aids, hands-free accessories, mobile phones, and conference-room devices. In this paper, we present an algorithm for single-channel transient interference suppression. The main component of the proposed algorithm is the estimation of the spectral variance of the interference. We propose a statistical model of the transient interference and combine it with non-local filtering. We exploit the unique spectral structure of the transients along with their impulsive temporal nature to distinct them from speech. A particular attention is given to handling both short- and long-duration transients. Experimental results show that the proposed algorithm enables significant transient suppression for a variety of transient types.
24 Mar 2013
TL;DR: In this paper, the cyclic structure of power line noise observed in a narrowband OFDM Power Line Communication (NB-OFDM PLC) system operating in the CENELEC 3-148.5 kHz band is analyzed.
Abstract: Narrowband OFDM Power Line Communication (NB-OFDM PLC) systems are a key component of current and future smart grids. NB-OFDM PLC systems enable next-generation smart metering, distributed control, and monitoring applications over existing power delivery infrastructure. It has been shown that the performance of these systems is severely limited by impulsive, non-Gaussian additive noise. A substantial component of this noise has time-periodic statistics (i.e. it is cyclostationary) synchronous to the AC mains cycle. In this work, we analyze the cyclic structure of power line noise observed in a G3 PLC system operating in the CENELEC 3-148.5 kHz band. Our contributions include: (i) the characterization of noise measurements in several urban usage environments, (ii) the development of a cyclic bit loading method for G3, and (iii) the quantification of its throughput gains over measured noise. Through this analysis, we confirm strong cyclostationarity in power lines and identify several sources of the cyclic noise.
TL;DR: The experimental results confirm that the TPFF attains an excellent quality of restored images in terms of peak signal-to-noise ratio, mean square error, and mean absolute error even when the noise rate is above 0.5 and without the aid of noise-free images.
Abstract: Digital images are often corrupted by impulsive noise during data acquisition, transmission, and processing. This paper presents a turbulent particle swarm optimization (PSO) (TPSO)-based fuzzy filtering (or TPFF for short) approach to remove impulse noise from highly corrupted images. The proposed fuzzy filter contains a parallel fuzzy inference mechanism, a fuzzy mean process, and a fuzzy composition process. To a certain extent, the TPFF is an improved and online version of those genetic-based algorithms which had attracted a number of works during the past years. As the PSO is renowned for its ability of achieving success rate and solution quality, the superiority of the TPFF is almost for sure. In particular, by using a no-reference Q metric, the TPSO learning is sufficient to optimize the parameters necessitated by the TPFF. Therefore, the proposed fuzzy filter can cope with practical situations where the assumption of the existence of the “ground-truth” reference does not hold. The experimental results confirm that the TPFF attains an excellent quality of restored images in terms of peak signal-to-noise ratio, mean square error, and mean absolute error even when the noise rate is above 0.5 and without the aid of noise-free images.
TL;DR: Novel Sorted Switching Median Filter for effectively denoising extremely corrupted images while preserving the image details is proposed and substantially outperforms all other existing median-based filters.
TL;DR: In this article, a two-stage method for high density noise suppression while preserving the image details is proposed, where the first stage applies an iterative impulse detector, exploiting the image entropy, to identify the corrupted pixels and then employs an adaptive iterative mean filter to restore them.
Abstract: 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.
01 Dec 2013
TL;DR: An improved algorithm fusing the enhanced Lee filter and median filter based on spatial filtering of SAR image speckle has a good performance in preserving edges and details while filtering images.
Abstract: Speckle noise usually occurs in synthetic aperture radar (SAR) images , and SAR data is processed coherently. Speckle filters commonly are adaptive filters using local statistics such as mean and standard deviation, such as the Lee and its enhanced filters and median filter. They adapt the filter coefficients based on data within a fixed moving window, and this brings in contradiction between the quality of speckle noise suppression and the capability of preserving image details. The Lee filter decreases speckle noise well in homogeneous regions, and the enhanced filter performs well both in the homogeneous and heterogeneous areas. But it does not effectively maintain image edges and details, while depressing SAR image noise. The median filter does well in decreasing impulse noise. In this paper, we propose an improved algorithm fusing the enhanced Lee filter and median filter based on spatial filtering of SAR image speckle. The experiment proves it has a good performance in preserving edges and details while filtering images.
TL;DR: The patch-based approach, which requires careful choices for both the distance between patches and for the statistical estimator of the original patch, proves to be particularly powerful, especially for the restoration of textured regions, and compares favorably to recent restoration methods.
Abstract: In this paper, we address the problem of the restoration of images which have been affected by impulse noise or by a mixture of Gaussian and impulse noise. We rely on a patch-based approach, which requires careful choices for both the distance between patches and for the statistical estimator of the original patch. Experiments are run in the case of pure impulse noise and in the case of a mixture. The method proves to be particularly powerful, especially for the restoration of textured regions, and compares favorably to recent restoration methods.
TL;DR: The experimental results show that the proposed algorithm significantly outperforms other state-of-the-art video denoising methods in terms of both objective measure and visual evaluation.
Abstract: In this paper, a new quaternion vector filter for removal of random impulse noise in color video sequences is presented. First, luminance distances and chromaticity differences that are represented in quaternion form are combined together to measure color distances between color pixels. Then, based on this new color distance mechanism, the samples along horizontal, vertical, and diagonal directions in current frame and the samples of adjacent frames on motion trajectory are used to detect whether each pixel is noisy or not. By analyzing the spatiotemporal order-statistic information about these directional samples, the video pixels are classified into noise free and noisy. Finally, 3-D weighted vector median filtering is performed on the pixels that are judged as noisy, and the other pixels remain unchanged. The experimental results show that the proposed algorithm significantly outperforms other state-of-the-art video denoising methods in terms of both objective measure and visual evaluation.
TL;DR: This paper proposes a patch-based model that works extremely well for image deblurring under salt-and-pepper noise and combines the two separate phases to simultaneously detect the random-valued noise positions and to recover the image.
Abstract: In this paper, we study the image recovery problem where the observed image is simultaneously corrupted by blur and impulse noise. Our proposed patch-based model contains three terms: the sparse representation prior, the total variation regularization, and the data-fidelity term. We are interested in the two-phase approach. The first phase is to identify the possible impulse noise positions; the second phase is to recover the image via the patch-based model using noise position information. An alternating minimization method is then applied to solve the model. This approach works extremely well for image deblurring under salt-and-pepper noise. However, as the detection for random-valued noise is usually unreliable, extra work is then needed. Indeed, to get better recovery results for the latter case, we combine the two separate phases to simultaneously detect the random-valued noise positions and to recover the image. The numerical experiments clearly demonstrate the super performance of the proposed methods.
TL;DR: Extensive simulations have been carried out on a set of standard gray scale images and the state of the art median filter variants are compared in terms of the well known image quality assessment metrics namely mean square error, peak signal to noise ratio and multiscale structural similarity index.
Abstract: Impulse noise removal is a mechanism for detection and removal of impulse noise from images. Median filters are preferred for removing impulse noise because of their simplicity and less computational complexity. In this paper, impulse noise removal using the standard median filter and its variants are analyzed. Extensive simulations have been carried out on a set of standard gray scale images and the state of the art median filter variants are compared in terms of the well known image quality assessment metrics namely mean square error, peak signal to noise ratio and multiscale structural similarity index.
Patent•
11 Jun 2013
TL;DR: In this article, the authors evaluate the impact of impulse noise on a communication system and determine how the system should be configured to adapt to impulse noise events, such as length of the event, repetition period, and timing of event.
Abstract: Evaluation of the impact of impulse noise on a communication system can be utilized to determine how the system should be configured to adapt to impulse noise events. Moreover, the system allows for information regarding impulse noise events, such as length of the event, repetition period of the event and timing of the event, to be collected and forwarded to a destination. The adaptation can be performed during one or more of Showtime and initialization, and can be initiated and determined at either one or more of a transmitter and a receiver.
TL;DR: Extensive simulations demonstrate that the decision-based non-local means filter can remove impulse noise from the corrupted images effectively while preserving image details very well at the various noise ratios, which leads to its significantly better image restoration performance than numerous state-of-the-art switching-based filters.