Showing papers on "Impulse noise published in 2012"
TL;DR: In this paper, the authors investigate the recovery of signals exhibiting a sparse representation in a general (i.e., possibly redundant or incomplete) dictionary that are corrupted by additive noise admitting sparse representations in another general dictionary.
Abstract: We investigate the recovery of signals exhibiting a sparse representation in a general (i.e., possibly redundant or incomplete) dictionary that are corrupted by additive noise admitting a sparse representation in another general dictionary. This setup covers a wide range of applications, such as image inpainting, super-resolution, signal separation, and recovery of signals that are impaired by, e.g., clipping, impulse noise, or narrowband interference. We present deterministic recovery guarantees based on a novel uncertainty relation for pairs of general dictionaries and we provide corresponding practicable recovery algorithms. The recovery guarantees we find depend on the signal and noise sparsity levels, on the coherence parameters of the involved dictionaries, and on the amount of prior knowledge about the signal and noise support sets.
200 citations
TL;DR: The proposed blind inpainting method is applied to various challenging image restoration tasks, including recovering images that are blurry and damaged by scratches and removing image noise mixed with both Gaussian and random-valued impulse noise.
144 citations
TL;DR: This paper proposes a new detection mechanism for universal noise and a universal noise-filtering framework based on the nonlocal means (NL-means) that produces excellent results and outperforms most existing filters for different noise models.
Abstract: Impulse noise detection is a critical issue when removing impulse noise and impulse/Gaussian mixed noise. In this paper, we propose a new detection mechanism for universal noise and a universal noise-filtering framework based on the nonlocal means (NL-means). The operation is carried out in two stages, i.e., detection followed by filtering. For detection, first, we propose the robust outlyingness ratio (ROR) for measuring how impulselike each pixel is, and then all the pixels are divided into four clusters according to the ROR values. Second, different decision rules are used to detect the impulse noise based on the absolute deviation to the median in each cluster. In order to make the detection results more accurate and more robust, the from-coarse-to-fine strategy and the iterative framework are used. In addition, the detection procedure consists of two stages, i.e., the coarse and fine detection stages. For filtering, the NL-means are extended to the impulse noise by introducing a reference image. Then, a universal denoising framework is proposed by combining the new detection mechanism with the NL-means (ROR-NLM). Finally, extensive simulation results show that the proposed noise detector is superior to most existing detectors, and the ROR-NLM produces excellent results and outperforms most existing filters for different noise models. Unlike most of the other impulse noise filters, the proposed ROR-NLM also achieves high peak signal-to-noise ratio and great image quality by efficiently removing impulse/Gaussian mixed noise.
137 citations
TL;DR: Experimental results show that the proposed approach to efficiently remove background noise by detecting and modifying noisy pixels in an image cannot only efficiently suppress high-density impulse noise, but also can well preserve the detailed information of an image.
110 citations
TL;DR: A novel adaptive detail-preserving filter based on the cloud model (CM) to remove impulse noise is presented and it is shown that, compared with the traditional switching filters, the CM filter makes a great improvement in image denoising.
Abstract: Uncertainties are the major inherent feature of impulse noise. This fact makes image denoising a difficult task. Understanding the uncertainties can improve the performance of image denoising. This paper presents a novel adaptive detail-preserving filter based on the cloud model (CM) to remove impulse noise. It is called the CM filter. First, an uncertainty-based detector identifies the pixels corrupted by impulse noise. Then, a weighted fuzzy mean filter is applied to remove the noise candidates. The experimental results show that, compared with the traditional switching filters, the CM filter makes a great improvement in image denoising. Even at a noise level as high as 95%, the CM filter still can restore the image with good detail preservation.
94 citations
TL;DR: The convergence of a general primal–dual method for nonsmooth convex optimization problems whose structure is typical in the imaging framework, as, for example, in the Total Variation image restoration problems, is established.
Abstract: In this paper we establish the convergence of a general primal–dual method for nonsmooth convex optimization problems whose structure is typical in the imaging framework, as, for example, in the Total Variation image restoration problems. When the steplength parameters are a priori selected sequences, the convergence of the scheme is proved by showing that it can be considered as an e-subgradient method on the primal formulation of the variational problem. Our scheme includes as special case the method recently proposed by Zhu and Chan for Total Variation image restoration from data degraded by Gaussian noise. Furthermore, the convergence hypotheses enable us to apply the same scheme also to other restoration problems, as the denoising and deblurring of images corrupted by Poisson noise, where the data fidelity function is defined as the generalized Kullback–Leibler divergence or the edge preserving removal of impulse noise. The numerical experience shows that the proposed scheme with a suitable choice of the steplength sequences performs well with respect to state-of-the-art methods, especially for Poisson denoising problems, and it exhibits fast initial and asymptotic convergence.
90 citations
TL;DR: An iterative interference cancellation scheme which can effectively reduce the level of ICI caused by the blanking operation at the OFDM receiver is proposed and simulation results show significant performance improvement is achieved.
Abstract: A simple iterative interference cancellation scheme for orthogonal frequency division multiplexing (OFDM) signals with blanking nonlinearity in impulsive noise channels is presented. Blanking nonlinearity has been widely used in practical OFDM systems to suppress impulsive noises at the expense of reducing signal power and generating intercarrier interference (ICI). To improve the performance of blanking nonlinearity element, we propose an iterative interference cancellation scheme which can effectively reduce the level of ICI caused by the blanking operation at the OFDM receiver. With adaptive blanking threshold for each iteration, the proposed iterative receiver design can converge to its best performance with only three iterations. Simulation results show significant performance improvement is achieved by the proposed scheme.
89 citations
TL;DR: FBDA is a fuzzy-based switching median filter in which the filtering is applied only to corrupted pixels in the image while the uncorrupted pixels are left unchanged and produces better results in terms of both quantitative measures such as PSNR, SSIM, IEF and qualitative measuressuch as Image Quality Index (IQI).
Abstract: This paper proposes a new efficient fuzzy-based decision algorithm (FBDA) for the restoration of images that are corrupted with high density of impulse noises. FBDA is a fuzzy-based switching median filter in which the filtering is applied only to corrupted pixels in the image while the uncorrupted pixels are left unchanged. The proposed algorithm computes the difference measure for each pixel based on the central pixel (corrupted pixel) in a selected window and then calculates the membership value for each pixel based on the highest difference. The algorithm then eliminates those pixels from the window with very high and very low membership values, which might represent the impulse noises. Median filter is then applied to the remaining pixels in the window to get the restored value for the current pixel position. The proposed algorithm produces excellent results compared to conventional method such as standard median filter (SMF) as well as some advanced techniques such as adaptive median filters (AMF), efficient decision-based algorithm (EDBA), improved efficient decision-based algorithm (IDBA) and boundary discriminative noise detection (BDND) switching median filter. The efficiency of the proposed algorithm is evaluated using different standard images. From experimental analysis, it has been found that FBDA produces better results in terms of both quantitative measures such as PSNR, SSIM, IEF and qualitative measures such as Image Quality Index (IQI).
74 citations
TL;DR: This work derived the clipping threshold without assuming the a priori knowledge of the probability density function of impulsive noise, which is usually unobtainable in precise measure in most practical scenarios, and may change rapidly over time.
Abstract: The detriment arising from strong and frequently occurring impulses over an Orthogonal Frequency Division Multiplexing system is paramount because signals on sub-carriers appear to be corrupted simultaneously. To overcome this obstacle, clipping has been reported as an effective approach. Unlike previous works, this work derived the clipping threshold without assuming the a priori knowledge of the probability density function (PDF) of impulsive noise, which is usually unobtainable in precise measure in most practical scenarios, and may change rapidly over time. Then, a decoding metric accommodated to the clipping effect was derived to realize coding gain. To attest the proposed scheme, this study conducted computer simulations in compliance with the IEEE 1901 standard. For various impulse noise models under consideration, the proposed scheme was promisingly on par with its conventional counterpart, the clipping threshold of which, however, relies on an assumed PDF.
57 citations
TL;DR: Experimental results show the superiority of the proposed algorithm in measures of PSNR and SSIM, specifically when the image is corrupted with more than 90% impulse noise.
47 citations
TL;DR: An iterative algorithm is proposed for solving the robust CS problem that exploits the power of existing CS solvers and the upper bound on the recovery error in the case of non-Gaussian noise is reduced.
Abstract: Compressed sensing (CS) is a new information sampling theory for acquiring sparse or compressible data with much fewer measurements than those otherwise required by the Nyquist/Shannon counterpart. This is particularly important for some imaging applications such as magnetic resonance imaging or in astronomy. However, in the existing CS formulation, the use of the l2 norm on the residuals is not particularly efficient when the noise is impulsive. This could lead to an increase in the upper bound of the recovery error. To address this problem, we consider a robust formulation for CS to suppress outliers in the residuals. We propose an iterative algorithm for solving the robust CS problem that exploits the power of existing CS solvers. We also show that the upper bound on the recovery error in the case of non-Gaussian noise is reduced and then demonstrate the efficacy of the method through numerical studies.
TL;DR: The results demonstrate that the type-2 fuzzy logic based impulse detector can be used as an efficient tool to effectively improve the performances of impulse noise filters and reduce the impulse noise undesirable distortion effects.
Abstract: In this paper, we present a novel application of type-2 fuzzy logic to the design of an image processing operator called an impulse detector. The type-2 fuzzy logic based impulse detector can be used to guide impulse noise removal filters to significantly improve their filtering performance and enhance their output images. The design of the proposed impulse detector is based on two 3-input 1-output first order Sugeno type interval type-2 fuzzy inference systems. The internal parameters of the type-2 fuzzy membership functions of the systems are determined by training. The performance of the impulse detector is evaluated by using it in combination with four popular impulse noise filters from the literature on four different popular test images under three different noise conditions. The results demonstrate that the type-2 fuzzy logic based impulse detector can be used as an efficient tool to effectively improve the performances of impulse noise filters and reduce the impulse noise undesirable distortion effects.
TL;DR: Second generation ANI camera (ROMANIS) was developed and algorithms based on low-order moments and fractiles are developed and demonstrated and shown that the ambient noise is well modeled by a symmetric α-stable (SαS) distribution.
Abstract: The high frequency ambient noise in warm shallow waters is dominated by snapping shrimp. The loud snapping noises they produce are impulsive and broadband. As the noise propagates through the water, it interacts with the seabed, sea surface, and submerged objects. An array of acoustic pressure sensors can produce images of the submerged objects using this noise as the source of acoustic "illumination." This concept is called ambient noise imaging (ANI) and was demonstrated using ADONIS, an ANI camera developed at the Scripps Institution of Oceanography. To overcome some of the limitations of ADONIS, a second generation ANI camera (ROMANIS) was developed at the National University of Singapore. The acoustic time series recordings made by ROMANIS during field experiments in Singapore show that the ambient noise is well modeled by a symmetric α-stable (SαS) distribution. As high-order moments of SαS distributions generally do not converge, ANI algorithms based on low-order moments and fractiles are developed and demonstrated. By localizing nearby snaps and identifying the echoes from an object, the range to the object can be passively estimated. This technique is also demonstrated using the data collected with ROMANIS.
25 Oct 2012
TL;DR: This hybrid of MC modulation and DCSK is aimed at combining the height data rate, the simplified equalization in multipath propagation, the robustness to impulse noise, and the security of transmission coming from the use of chaotic signals in digital communications.
Abstract: This paper presents a secure Multi-Carrier Differential Chaos Shift Keying (MC-DCSK) system. This hybrid of MC modulation and DCSK is aimed at combining the height data rate, the simplified equalization in multipath propagation, the robustness to impulse noise, and the security of transmission coming from the use of chaotic signals in digital communications. In this paper, we describe the proposed transmitter and receiver, including the chaotic modulation used in a single input, single output system, and then we evaluate the potential benefits of the MC-DCSK. To increase security, a spreading and interleaving in time and frequency are used to break the similarity between the reference and the data samples of the DCSK signal. The performance of the MC-DCSK is evaluated under a fading communication channel, and without equalization on the receiver side. An approach for computing the bit-error-rate (BER) performance is provided, and an analytical BER expression is derived. Simulation results confirm the accuracy of our performance computation approach.
TL;DR: There are many variations of median filter in literature, and this paper will survey these median filtering frameworks, including weighted median filter, iterative median filters, recursive medianfilter, recursive Median filter, directional median Filter, switching median filter and adaptive median filter.
Abstract: —One of the noise types that is normally degrades digital images, including grayscale digital images, is impulse noise. Therefore, researches regarding to impulse noise removal have become one of the active researches in the field of image restoration. Median based filter is normally becoming the choice to deal with this type of noise. However, there are many variations of median filter in literature. In addition to standard median filter, there are weighted median filter, iterative median filter, recursive median filter, directional median filter, switching median filter, and adaptive median filter. Therefore, this paper will survey these median filtering frameworks.
25 Mar 2012
TL;DR: The computational performance of the proposed algorithm greatly exceeds that of the state of the art algorithms within the TV framework, and its reconstruction quality performance is competitive for high noise levels, for both grayscale and vector-valued images.
Abstract: Several Total Variation (TV) regularization methods have recently been proposed to address denoising under mixed Gaussian and impulse noise While achieving high-quality denoising results, these new methods are based on complicated cost functionals that are difficult to optimize, which negatively affects their computational performance In this paper we propose a simple cost functional consisting of a TV regularization term and l 2 and l 1 data fidelity terms, for Gaussian and impulse noise respectively, with local regularization parameters selected by an impulse noise detector The computational performance of the proposed algorithm greatly exceeds that of the state of the art algorithms within the TV framework, and its reconstruction quality performance is competitive for high noise levels, for both grayscale and vector-valued images
TL;DR: An improved decision-based detail-preserving variational method (DPVM) for removal of random-valued impulse noise that outperforms some existing algorithms, both in vision and quantitative measurements.
Abstract: The authors propose an improved decision-based detail-preserving variational method (DPVM) for removal of random-valued impulse noise. In the denoising scheme, adaptive centre weighted median filter (ACWMF) is first ameliorated by employing the variable window technique to improve its detection ability in highly corrupted images. Based on the improved ACWMF, a fast iteration strategy is used to classify the noise candidates and label them with different noise marks. Then, all the noise candidates are restored one-time by weight-adjustable detail-preserving variational method. The weights between the data-fidelity term and the smooth regularisation term of the convex cost-function in DPVM are decided by the noise marks. After minimisation, the restored image is obtained. Extensive simulation results show that the proposed method outperforms some existing algorithms, both in vision and quantitative measurements. Moreover, our method is faster than some decision-based DPVM. Therefore it can be ported into practical application easily.
Patent•
21 Nov 2012TL;DR: In this paper, a new and effective keyboard click noise reduction scheme is presented, which adaptively changing the coefficients of the proposed adaptive filter through minimizing the output energy, the scheme can provide the target signal/voice with nearly zero keyboard click noises.
Abstract: According to various embodiments of the invention, a new and effective keyboard click noise reduction scheme is presented. The keyboard click noise reduction scheme may have various processing units including: Dynamic Signal Modeler, Smart Model Selector, Adaptive Filtering Module, Keyboard/Impulse Noise and Voice Activity Detectors, and a Post-Processing Unit. By adaptively changing the coefficients of the proposed adaptive filter through minimizing the output energy, the scheme can provide the target signal/voice with nearly zero keyboard click noise. The scheme could be used in real-time to minimize keyboard click noise or any kind of unwanted noise, especially noise having transient impulse characteristics.
Posted Content•
TL;DR: Simulation results show that the proposed statistical modelling method, which integrates the impact of network topology presents the PLC channel features as the underlying transmission line theory model.
Abstract: This paper proposes a new channel modelling method for powerline communications networks based on the multipath profile in the time domain. The new channel model is developed to be applied in a range of Powerline Communications (PLC) research topics such as impulse noise modelling, deployment and coverage studies, and communications theory analysis. To develop the methodology, channels are categorised according to their propagation distance and power delay profile. The statistical multipath parameters such as path arrival time, magnitude and interval for each category are analyzed to build the model. Each generated channel based on the proposed statistical model represents a different realisation of a PLC network. Simulation results in similar the time and frequency domains show that the proposed statistical modelling method, which integrates the impact of network topology presents the PLC channel features as the underlying transmission line theory model. Furthermore, two potential application scenarios are described to show the channel model is applicable to capacity analysis and correlated impulse noise modelling for PLC networks.
Journal Article•
TL;DR: In this article, the results of objective tests performed on 13 personal active noise reduction devices (earmuffs, headphones, headsets and insert earphones) divided into four groups based mainly on structure, using an acoustic test fixture (ATF).
TL;DR: An image filtering technique based on fuzzy logic control to remove impulse noise for low as well as highly corrupted images and sensitivity analysis proves experimentally that significant image details have been preserved by the proposed method.
Abstract: In this paper, we propose an image filtering technique based on fuzzy logic control to remove impulse noise for low as well as highly corrupted images. The proposed method is based on noise detection, noise removal and edge preservation modules. The main advantage of the proposed technique over the other filtering techniques is its superior noise removal as well as detail preserving capability. Based on the criteria of peak-signal-to-noise-ratio (PSNR), mean square error (MSE), structural similarity index measure (SSIM) and subjective evaluation measure we have found experimentally that the proposed method provides much better performance than the state-of-the-art filters. To analyze the detail preservation capability of the proposed filter sensitivity analysis is performed by changing the detail preservation module to see its effects on the details (texture and edge information) of resultant image. This sensitivity analysis proves experimentally that significant image details have been preserved by the proposed method.
TL;DR: To eliminate impulse noises from noisy images, a hybrid method based on cellular automata (CA) and fuzzy logic called Fuzzy Cellular Automata (FCA) in two steps is used which keeps the important details of the image effectively.
Abstract: Impulse noise reduction from corrupted images plays an important role in image processing. This problem will also affect on image segmentation, object detection, edge detection, compression, etc. Generally, median filters or nonlinear filters have been used for noise reduction but these methods will destroy the natural texture and important information in the image like the edges. In this paper, to eliminate impulse noises from noisy images, we used a hybrid method based on cellular automata (CA) and fuzzy logic called Fuzzy Cellular Automata (FCA) in two steps. In the first step, based on statistical information, noisy pixels are detected by CA; then using this information, the noisy pixel will change by FCA. Regularly, CA is used for systems with simple components where the behavior of each component will be defined and updated based on its neighbors. The proposed hybrid method is characterized as simple, robust and parallel which keeps the important details of the image effectively. The proposed approach has been performed on well-known gray scale test images and compared with other conventional and famous algorithms, is more effective.
TL;DR: Experimental results on real image for Gaussian noise removal and impulse noise removal consistently demonstrate that the proposed approach can efficiently remove the noise while maintaining high image quality.
TL;DR: Simulation results show that the proposed switching median filter algorithm is better when compared to other existing techniques in terms of visual and quantitative measures such as PSNR, MSE, SSIM using MATLAB.
27 Mar 2012
TL;DR: In this paper, a modified conventional QPSK-OFDM transmission scheme is presented to combat the impairments caused by frequency disturbances in the power line communications (PLC), where real and imaginary parts of QPSk symbols are independently assigned to the selected subcarriers in a group, such that the minimum squared Euclidean distance is maximised.
Abstract: To combat the impairments caused by frequency disturbances in the power line communications (PLC), a modified conventional QPSK-OFDM transmission scheme is presented. The idea of this scheme is to first group the N OFDM subcarriers into groups of μ and then transmit data by selecting a subset of the subcarriers in the group. Real and imaginary parts of QPSK symbols are independently assigned to the selected subcarriers in a group, such that the minimum squared Euclidean distance is maximised. With this kind of symbol assignment to subcarriers our scheme has no net loss in terms of SNR requirements, in AWGN, in comparison to the conventional QPSK-OFDM, even though it has half the data rate of the conventional QPSK-OFDM. We refer to the conventional QPSK-OFDM as Scheme A. Our scheme displays a superior performance over Scheme A and another scheme (Scheme B), in the presence of frequency disturbances and also frequency selective fading noise. We further modify Scheme B and come up with additional two new QPSK-OFDM schemes that have better performance than Scheme B in AWGN and impulse noise. To encode, we apply a (n, k) RS code and a simple permutation code on the conventional QPSK-OFDM scheme, which significantly improves the decoder's performance in the presence of frequency disturbances. A simple narrow band noise model is developed and presented.
TL;DR: Experiments shows that proposed method that consists of noise detection and noise filtering produce better results as compare to existing methods.
TL;DR: The experimental results for the proposed method demonstrate that it is faster and simpler than even median filtering, and it is very efficient for images corrupted with a wide range of impulse noise densities varying from 10% to 90%.
Abstract: This paper proposes a two-stage adaptive method for restoration of images corrupted with impulse noise. In the first stage, the pixels which are most likely contaminated by noise are detected based on their intensity values. In the second stage, an efficient average filtering algorithm is used to remove those noisy pixels from the image. Only pixels which are determined to be noisy in the first stage are processed in the second stage. The remaining pixels of the first stage are not processed further and are just copied to their corresponding locations in the restored image. The experimental results for the proposed method demonstrate that it is faster and simpler than even median filtering, and it is very efficient for images corrupted with a wide range of impulse noise densities varying from 10% to 90%. Because of its simplicity, high speed, and low computational complexity, the proposed method can be used in real-time digital image applications, e.g., in consumer electronic products such as digital televisions and cameras.
TL;DR: According to simulation results, the proposed blind algorithm produces superior performance in multi-path communication channels corrupted with impulsive noise and has relatively less sensitivity to channel eigenvalue ratio.
Abstract: In this paper, a new blind signal processing scheme for equalization in fading and impulsive-noise channel environments is introduced based on probability density function matching method and a set of Dirac-delta functions. Gaussian kernel of the proposed blind algorithm has the effect of cutting out the outliers on the difference between the desired level values and impulse-infected outputs. And also the proposed algorithm has relatively less sensitivity to channel eigenvalue ratio and has reduced computational complexity compared to the recently introduced correntropy algorithm. According to these characteristics, simulation results show that the proposed blind algorithm produces superior performance in multi-path communication channels corrupted with impulsive noise.
TL;DR: Experimental results show that the proposed impulse noise removal scheme is capable of removing impulse noise effectively while preserving the fine image details and has shown effectiveness against high impulse noise density.
Abstract: Generally, the impulse noise filtering schemes use all pixels within a neighborhood and increase the size of neighborhood with the increase in noise density. However, the estimate from all pixels within neighborhood may not be accurate. Moreover, the larger window may remove edges and fine details as well. In contrast, we propose a novel impulse noise removal scheme that emphasizes on few noise-free pixels and small neighborhood. The proposed scheme searches noise-free pixels within a small neighborhood. If at least three pixels are not found, then the noisy pixel is left unchanged in current iteration. This iterative process continues until all noisy pixels are replaced with estimated values. In order to estimate the optimal value of the noisy pixel, genetic programming-based estimator is developed. The estimator (function) is composed of useful pixel information and arithmetic functions. Experimental results show that the proposed scheme is capable of removing impulse noise effectively while preserving the fine image details. Especially, our approach has shown effectiveness against high impulse noise density.