Showing papers on "Impulse noise published in 2021"
TL;DR: Wang et al. as discussed by the authors developed a 3D spatial-spectral total variation (SSTV) regularization to globally represent the sparse prior in the gradient domain of hyperspectral images, and divided HSIs into local overlapping 3-D patches, and low-rank tensor recovery (LTR) is locally used to effectively separate the clean HSI patches from complex noise.
Abstract: Hyperspectral images (HSIs) are usually corrupted by various noises, e.g., Gaussian noise, impulse noise, stripes, dead lines, and many others. In this article, motivated by the good performance of the $L_{1-2}$ nonconvex metric in image sparse structure exploitation, we first develop a 3-D $L_{1-2}$ spatial–spectral total variation ( $L_{1-2}$ SSTV) regularization to globally represent the sparse prior in the gradient domain of HSIs. Then, we divide HSIs into local overlapping 3-D patches, and low-rank tensor recovery (LTR) is locally used to effectively separate the low-rank clean HSI patches from complex noise. The patchwise LTR can not only adapt to the local low-rank property of HSIs well but also significantly reduce the information loss caused by the global LTR. Finally, integrating the advantages of both the global $L_{1-2}$ SSTV regularization and local LTR model, we propose a $L_{1-2}$ SSTV regularized local LTR model for hyperspectral restoration. In the framework of the alternating direction method of multipliers, the difference of convex algorithm, the split Bregman iteration method, and tensor singular value decomposition method are adopted to solve the proposed model efficiently. Simulated and real HSI experiments show that the proposed model can reduce the dependence on noise independent and identical distribution hypotheses, and simultaneously remove various types of noise, even structure-related noise.
33 citations
TL;DR: The HSI observation model is extended and a double low-rank (DLR) matrix decomposition method is proposed for HSI denoising and destriping to achieve separation of the noise-free HSI, stripe noise, and other mixed noise.
Abstract: Hyperspectral images (HSIs) have a wealth of applications in many areas, due to their fine spectral discrimination ability. However, in the practical imaging process, HSIs are often degraded by a mixture of various types of noise, for example, Gaussian noise, impulse noise, dead pixels, dead lines, and stripe noise. Low-rank matrix decomposition theory has been widely used in HSI denoising, and has achieved competitive results by modeling the impulse noise, dead pixels, dead lines, and stripe noise as sparse components. However, the existing low-rank-based methods for HSI denoising cannot completely remove stripe noise when the stripe noise is no longer sparse. In this article, we extend the HSI observation model and propose a double low-rank (DLR) matrix decomposition method for HSI denoising and destriping. By simultaneously exploring the low-rank characteristic of the lexicographically ordered noise-free HSI and the low-rank structure of the stripe noise on each band of the HSI, the two low-rank constraints are formulated into one unified framework, to achieve separation of the noise-free HSI, stripe noise, and other mixed noise. The proposed DLR model is then solved by the augmented Lagrange multiplier (ALM) algorithm efficiently. Both simulation and real HSI data experiments were carried out to verify the superiority of the proposed DLR method.
31 citations
TL;DR: Momeny et al. as mentioned in this paper proposed a learning-to-augment approach that generates new noisy variants of the original image data with optimized noise density to improve the robustness and generalization of deep CNNs for COVID-19 detection.
28 citations
TL;DR: The results show that the proposed method outperforms the state-of-the-art techniques in terms of impulse detection and noise removal.
Abstract: The issue of impulse noise detection and reduction is a critical problem for image processing application systems. In order to detect impulse noises in corrupted images, a statistic named local consensus index (LCI) is proposed for quantitatively evaluating how noise free a pixel is, and then an impulse noise detection scheme based on LCI is introduced. First, the similarity between arbitrary two pixels in an image is quantified based on both their geometric distance and intensity difference, and the LCI of arbitrary pixel is calculated by summing all the similarity values of pixels in its neighborhood. As a new statistic, the value of LCI indicates the local consensus of the concerned pixel regarding its neighbors and could also tell whether a pixel is noise free or impulsive. Therefore, LCI can be directly used as an efficient indicator of impulse noise. Furthermore, to improve the performance of impulse noise detection, different strategies are applied to the pixels at flat regions and the ones with complex textures, since distributions of LCI value within those regions are totally different. As for impulse noise filtering, a hybrid graph Laplacian regularization (HGLR) method is introduced to restore the intensities of those pixels degraded by impulse noise. We conduct extensive experiments to verify the effectiveness of our impulsive noise detection and reduction method, and the results show that the proposed method outperforms the state-of-the-art techniques in terms of impulse detection and noise removal.
26 citations
TL;DR: A modified cascaded filter for the restoration of color pictures that are extremely corrupted by salt and pepper noise and random valued impulse noise is projected and it provides superior peak signal-to-noise ratio and image enhancement factor.
Abstract: A modified cascaded filter (MCF) for the restoration of color pictures that are extremely corrupted by salt and pepper noise and random valued impulse noise are projected in this article. MCF algorithm restores the noisy pixel by trimmed median value while other pixel values, 0’s and 255’s are present in the selected window using decision based median filter (DMF) and when the pixel values are 0’s and 255’s then the noise pixel is replaced by mean value of all the elements present in the selected window using unsymmetrical trimmed mean filtering. This modified cascaded filter proves better results than the standard median filter, DMF, and alpha trimmed median filter, UTMF. The MCF is analyzed against various color images and it provides superior peak signal-to-noise ratio and image enhancement factor.
26 citations
TL;DR: The compression of biomedical images is proposed using a deterministic binary compressive sensing matrix and for the recovery of the biomedical image, Orthogonal Matching Pursuit (OMP) is used.
Abstract: Bio medical images are very important in analysing the internal structure of the body in a non-invasive method, to diagnose for any abnormalities and provide proper treatment to the patients. Medical images include X-rays, CT scan and MRI scan etc. It is necessary to compress these images, so that they can be easily stored and communicated from one place to another. The result of compression should be in such a way that, quality of the compressed image should be good and the compression ratio should be high. The biomedical images are mainly prone to impulse noise which may lead to degradation of image quality. So it is important to filter the impulse noise from the biomedical images without affecting the details of the image. In this paper the filtering of impulse noise from the biomedical images is done using fuzzy transform, followed by compressive sensing to compress the bio medical images without losing information content. Compressive sensing uses a sensing matrix to measure random samples from the image signal. This paper proposes the compression of biomedical images using a deterministic binary compressive sensing matrix and for the recovery of the biomedical image, Orthogonal Matching Pursuit (OMP) is used.
25 citations
TL;DR: This work designs and derives the BS proportionate normalized least mean M-estimate (BSPNLMM) algorithm from the perspective of basis pursuit (BP), which well realizes the BSSI in the presence of impulse noise.
Abstract: In practical applications, the impulse responses (IRs) of some network echo paths are block-sparse (BS), while the traditional proportionate and zero attraction algorithms do not consider the prior sparsity of the BS system, so they do not perform well in the block-sparse system identification (BSSI). In addition, most of the current BS filtering algorithms are based on the assumption of Gaussian noise, so the performance will deteriorate seriously in the background of impulse noise. To overcome the shortcoming, we use the mixed l2,1 norm of the filter weight vector to fully tap the sparsity of the BS system, and combine the anti impulse noise characteristic of the M-estimate function to design and derive the BS proportionate normalized least mean M-estimate (BS-PNLMM) algorithm from the perspective of basis pursuit (BP), which well realizes the BSSI in the presence of impulse noise. Then, we analyze the mean performance of the BS-PNLMM algorithm in detail and give the stable step size bound. Finally, the superiority of the proposed BS-PNLMM algorithm is verified by numerical simulations.
18 citations
TL;DR: The experimental analysis shows that, the proposed WCFOA-based Deep CNN obtained better performance using the metrics, like correlation coefficient and Peak signal-to-noise ratio (PSNR) with the values of 1 and 45.2157 using without noise scenario and the correlation coefficients and PSNR of 0.9918 and 47.0627 for Impulse noise.
Abstract: Due to the rapid growth of multimedia in network technology, accessing the digital media becomes very easy. Hence, protecting the intellectual property requires more interest in image watermarking. For this sake, different image watermarking approaches are developed, but it poses robustness and transparency issues. Therefore, an effective image watermarking method named Water Chaotic fruit fly Optimization algorithm-based Deep Convolutional neural network (WCFOA-based Deep CNN) is developed for embedding the secret message to the cover media. The proposed WCFOA is developed by integrating the Water Wave Optimization (WWO) with the Chaotic Fruit Fly Optimization algorithm (CFOA). The inspiration of propagation operator and the refraction operator increases the diversity of population and minimizes the premature convergence. However, the breaking, propagation and the refraction operator of the proposed optimization shows the effectiveness of balance between the exploitation of exploration phase in search space using the fitness measure. Accordingly, the embedding process is achieved using the wavelet transform with the selected optimal region using the evaluated fitness value. Several images of brain tumors from BRATS dataset, with tumors having different contrast and form, are used to assess the proposed method. The experimental analysis shows that, the proposed WCFOA-based Deep CNN obtained better performance using the metrics, like correlation coefficient and Peak signal-to-noise ratio (PSNR) with the values of 1 and 45.2157 using without noise scenario and the correlation coefficient and PSNR of 0.9918 and 45.0627 for Impulse noise. By considering the salt and pepper noise, the correlation coefficient and PSNR is 0.9918 and 47.001 and in the Gaussian noise scenario the values of correlation coefficient and PSNR is 0.990 and 46.985, respectively.
16 citations
TL;DR: A variational model that uses the non-convex lp-norm, 0 < p < 1 for both the data fidelity and a second-order total variation regularization term combined with an overlapping group sparse regularizer to robustly eliminate impulse noise is proposed.
Abstract: A typical approach to eliminate impulse noise is to use the l1-norm for both the data fidelity term and the regularization terms. However, the l1-norm tends to over penalize signal entries which is one of its underpinnings. Hence, we propose a variational model that uses the non-convex lp-norm, 0 < p < 1 for both the data fidelity and a second-order total variation regularization term combined with an overlapping group sparse regularizer. Specifically, to robustly eliminate impulse noise, the proposed method uses a non-convex data fidelity term. The hybrid combination of a second-order non-convex total variation and an overlapping group sparse regularization term is used to eliminate the remaining staircase artifacts while maintaining a sharp restored image. A mathematical formulation is derived and to implement it, the iterative re-weighted l1 (IRL1) based alternating direction method of multipliers (ADMM) is used to solve the constraints and the subproblems. Experimental results for image denoising and deblurring on several widely used standard images demonstrate that the proposed method performed better when compared to the l1-norm total variation (TV), total generalized variation (TGV) model, and the non-convex lp-norm TV-based data fidelity model in terms of peak signal-to-noise ratio (PSNR) and structure similarity index measure (SSIM).
15 citations
TL;DR: In this paper, the authors estimate clean HSIs from observations corrupted by mixed noise by exploiting two main characteristics of hyperspectral data, namely low-rankness in the spectral domain and high correlation in the spatial domain.
Abstract: The ever-increasing spectral resolution of hyperspectral images (HSIs) is often obtained at the cost of a decrease in the signal-to-noise ratio (SNR) of the measurements. The decreased SNR reduces the reliability of measured features or information extracted from HSIs, thus calling for effective denoising techniques. This work aims to estimate clean HSIs from observations corrupted by mixed noise (containing Gaussian noise, impulse noise, and dead-lines/stripes) by exploiting two main characteristics of hyperspectral data, namely low-rankness in the spectral domain and high correlation in the spatial domain. We take advantage of the spectral low-rankness of HSIs by representing spectral vectors in an orthogonal subspace, which is learned from observed images by a new method. Subspace representation coefficients of HSIs are learned by solving an optimization problem plugged with an image prior extracted from a neural denoising network. The proposed method is evaluated on simulated and real HSIs. An exhaustive array of experiments and comparisons with state-of-the-art denoisers were carried out.
13 citations
TL;DR: A robust impulse denoising method based on noise accumulation and harmonic analysis techniques (NAHAT filter) that can remove impulse noise very effectively even under high-density impulse noise levels and preserve image structures such as the edges and textures well.
TL;DR: Wang et al. as mentioned in this paper proposed to denoise the HSI by using stripe and spectral low-rank matrix recovery and combine it with the global spatial-spectral TV regularization (SSLR-SSTV).
Abstract: Hyperspectral image (HSI) is easily corrupted by different kinds of noise, such as stripes, dead pixels, impulse noise, Gaussian noise, etc. Due to less consideration of the structural specificity of stripes, many existing HSI denoising methods cannot effectively remove the heavy stripes in mixed noise. In this paper, we classify the noise on HSI into three types: sparse noise, stripe noise, and Gaussian noise. The clean image and different types of noise are treated as independent components. In this way, the image denoising task can be naturally regarded as an image decomposition problem. Thanks to the structural characteristic of stripes and the low-rank property of HSI, we propose to destripe and denoise the HSI by using stripe and spectral low-rank matrix recovery and combine it with the global spatial-spectral TV regularization (SSLR-SSTV). By considering different properties of different HSI ingredients, the proposed method separates the original image from the noise components perfectly. Both simulation and real image denoising experiments demonstrate that the proposed method can achieve a satisfactory denoising result compared with the state-of-the-art methods. Especially, it outperforms the other methods in the task of stripe noise removal visually and quantitatively.
TL;DR: In this article, an adaptive median based non-local low rank approximation (AMNLRA) approach is proposed for image restoration problems where the image is corrupted by a mixture of additive white Gaussian noise (AWGN) and impulse noise (IN).
Abstract: In this paper, we present an innovative mechanism for image restoration problems in which the image is corrupted by a mixture of additive white Gaussian noise (AWGN) and impulse noise (IN). Mixed noise removal is much more challenging problem in contrast to the problems where either only one type of noise model (either Gaussian or impulse) is involved. Several well-known and efficient algorithms exist to effectively remove either Gaussian noise or Impulse noise, independently. However, in practice, noise may occur as a mixture of such noise models. Thus, the existing techniques devised to handle individual types of noise may not perform well. Moreover, the complexity of the problem hinges on the fact that the removal of either type of noise from the given image affects the noise statistics in the residual image. Therefore, a rigorous mechanism is required which not only infers altered noise statistics but also removes the residual noise in an effective manner. In this regard, an innovative approach is introduced to restore the underlying image in three key steps. Firstly, the intensity values, affected by impulsive noise, are identified by analyzing noise statistics with the help of adaptive median filtering. The identified intensity values are then aggregated by exploiting nonlocal data redundancy prior. Thus the first step enables the remaining noise to follow the zero mean Gaussian distribution in the median filtered image. Secondly, we estimate Gaussian noise in the resulting image, which acts as a key parameter in the subsequent singular value thresholding process for rank minimization. Finally, a reduced rank optimization applied to the pre-processed image obtained in the first step. The experimental results indicate that the proposed AMNLRA (Adaptive Median based Non-local Low Rank Approximation) approach can effectively tackle mixed noise complexity as compared to numerous state of the art algorithms.
TL;DR: This work designs a novel filter through the hybridization of the fuzzy filter and the Non-Local Means (NLM) filter that removes the impulse noise in the images in two stages: noise identification and denoising.
TL;DR: The results of extensive experiments confirm that the RLENR method quantitatively and visually outperforms state-of-the-art face SR methods.
TL;DR: Numerical experiments show that the proposed non-convex constrained PDE produces betterDenoising results compared to the state-of-the-art denoising models.
Abstract: In this paper, we are interested in the mathematical and simulation study of a new non-convex constrained PDE to remove the mixture of Gaussian–impulse noise densities. The model incorporates a non-convex data-fidelity term with a fractional constrained PDE. In addition, we adopt a non-smooth primal-dual algorithm to resolve the obtained proximal linearized minimization problem. The non-convex fidelity term is used to handle the high-frequency of the impulse noise component, while the fractional operator enables the efficient denoising of smooth areas, avoiding also the staircasing effect that appears on the relevant variational denoising models. Moreover, the proposed primal-dual algorithm helps in preserving fine structures and texture with good convergence rate. Numerical experiments, including ultrasound images, show that the proposed non-convex constrained PDE produces better denoising results compared to the state-of-the-art denoising models.
TL;DR: The experimental results show that the split Bregman iterative method is superior to other methods in terms of effectiveness impulse noise (salt and pepper noise) for medical images (CT or MRI).
Abstract: With the rapid development of computer science and technology in modern society, image science is widely used in various fields, especially in medicine field. Image processing plays an important role in medical images. Medical images are often corrupted by noise due to various sources of interference and other phenomena during their acquisition and transmission that affects the measurement processes in imaging reduced image detail due to the introduction of noise. Keeping useful diagnostic information to suppress noise is a challenging task. Salt and pepper noise as a kind of ordinary noise is one of the impulse noises. In this work, we will use a logarithmic image prior constraint the objective function for the removal of the impulse noise. Also, we used the split Bregman iterative method to solve the objective function. Theoretically, under reasonable assumptions, we give partial convergence analysis of the algorithm. Computationally, we use the split Bregman iterative method under the guarantee of convergence analysis and the weight of SVD decomposition; a complex problem is transformed into several simple subproblems to solving, wherein u-subproblem can be solved by fast Fourier transform; h, d-subproblems can be solved use shrinkage operator, respectively. In the experimental aspects, we have done a lot of experiments and compared with other state-of-the-art methods. The experimental results show that the method is superior to other methods in terms of effectiveness impulse noise (salt and pepper noise) for medical images (CT or MRI).
TL;DR: In this article, a novel low-order covariance-based exponential kernel function was proposed for direction-finding using a monostatic MIMO radar in the presence of impulsive noise.
TL;DR: In this article, a two-step fuzzy filter is proposed to remove impulse noise from a color image sequence in RGB color space, where the primary filter recognizes the pixels corrupted by impulse noise and then rectifies them.
Abstract: This paper presents a two-step fuzzy filter to remove impulse noise from a color image sequence in RGB color space. The primary filter recognizes the pixels corrupted by impulse noise. It also computes the extent of noise and afterward rectifies them. The output of the first filter acts as an input to the secondary filter and further refines the outcome to give the final output. Excellent alignment is seen between noise removal and structure conservation of an image due to the classifying nature of the algorithm. To minimize blurring, noisy pixels are exclusively rectified and noise-free pixels remain intact. The proposed filter is a 3-D Spatio-temporal filter that considers spatial, temporal as well as color information. A pixel of one color component is compared to its neighboring pixels within the same frame and with the corresponding pixels in neighboring frames. It is likewise compared with the pixels of the other two color components. Peak Signal to Noise Ratio (PSNR) and structural similarity index (SSIM), Miss Detection (MD), and False Alarm (FA) rates are utilized as a performance metric. The experimental result of several color image sequences demonstrates the efficacy of the proposed fuzzy filter both qualitatively and quantitatively.
TL;DR: A hybrid optimization objective function is designed based on the two independence criteria to achieve more effective and robust BSS and the proposed method is called BSS based on QSMA (QSMA-BSS), which has a wider applications range, more stable performance, and higher precision.
Abstract: In order to resolve engineering problems that the performance of the traditional blind source separation (BSS) methods deteriorates or even becomes invalid when the unknown source signals are interfered by impulse noise with a low signal-to-noise ratio (SNR), a more effective and robust BSS method is proposed. Based on dual-parameter variable tailing (DPVT) transformation function, moving average filtering (MAF), and median filtering (MF), a filtering system that can achieve noise suppression in an impulse noise environment is proposed, noted as MAF-DPVT-MF. A hybrid optimization objective function is designed based on the two independence criteria to achieve more effective and robust BSS. Meanwhile, combining quantum computation theory with slime mould algorithm (SMA), quantum slime mould algorithm (QSMA) is proposed and QSMA is used to solve the hybrid optimization objective function. The proposed method is called BSS based on QSMA (QSMA-BSS). The simulation results show that QSMA-BSS is superior to the traditional methods. Compared with previous BSS methods, QSMA-BSS has a wider applications range, more stable performance, and higher precision.
01 Jan 2021
TL;DR: In this article, the performance of power rotational interleaver has been analyzed in terms of bit error rate by performing simulations in MATLAB and also present a comparative study with the other interleavers.
Abstract: Power line communication (PLC) is a technology of transferring data over power lines. As the power lines were not designed to carry communication signals, noise in the form of impulses and fading corrupt the signals. Researchers have proposed various techniques to make communication over power lines feasible such as interleave division multiple access (IDMA), code division multiple access (CDMA), and orthogonal frequency division multiplexing (OFDM). The most promising results have been obtained by employing a combination OFDM-IDMA scheme which tends to resolve both the issues caused due to impulse noise and fading. But the key factor in the performance is based on the type of interleaver used in the system. In literature, many interleavers have been proposed such as random, prime, power, tree-based, and power rotational out of which the performance of power rotational interleaver has not been implemented in OFDM-IDMA over PLC. The paper aims to bring out the key aspects of power rotational interleaver and analyze the performance in terms of bit error rate by performing simulations in MATLAB and also present a comparative study with the other interleavers.
TL;DR: Zhang et al. as discussed by the authors adopt superpixel segmentation to group the pixels of HSI with adjacent position, similar color, texture and luminance into a homogeneous region, whose shape is adaptive.
TL;DR: In this article, the edge-preserving median filter (EP median filter) and weighted encoding with sparse nonlocal regularization (WESNR) were used to remove the impulse noise and Gaussian noise in the mixed noise.
Abstract: The impulse noise in CT image was removed based on edge-preserving median filter algorithm. The sparse nonlocal regularization algorithm weighted coding was used to remove the impulse noise and Gaussian noise in the mixed noise, and the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) were calculated to evaluate the quality of the denoised CT image. It was found that in nine different proportions of Gaussian noise and salt-and-pepper noise in Shepp-Logan image and CT image processing, the PSNR and SSIM values of the proposed denoising algorithm based on edge-preserving median filter (EP median filter) and weighted encoding with sparse nonlocal regularization (WESNR) were significantly higher than those of using EP median filter and WESNR alone. It was shown that the weighted coding algorithm based on edge-preserving median filtering and sparse nonlocal regularization had potential application value in low-dose CT image denoising.
TL;DR: This paper presents a novel significance driven inverse distance weighted (SDIDW) filter for the impulsive noise removal in the X-ray images using minimum number of nearest noise-free pixels to achieve good estimation while exhibiting low computational complexity.
Abstract: This paper presents a novel significance driven inverse distance weighted (SDIDW) filter for the impulsive noise removal in the X-ray images. The proposed SDIDW filter restores the noisy pixel using minimum number of nearest noise-free pixels to achieve good estimation while exhibiting low computational complexity. In the proposed filter, higher priority (weight) is given to nearest pixels compared to distant pixels and only sufficient nearest noise free pixels are determined to estimate the value of noisy pixel. A high level analysis of the computation complexity at varying noise density is done which shows that proposed SDIDW filter provides significant reduction in computation complexity over the adaptive median filters. Finally, the performance of the proposed filter is evaluated and compared over the state-of-the-art impulse noise removal techniques for varying noise density (wide range 10–90% and very high noise density range 91–99%). The experimental results on medical images demonstrate significant improvement in filtered images quality by the proposed filter over the state-of-the-art filters at each sample of noise density with small computational complexity.
TL;DR: The experimental results reveal that the proposed deep-learning FCNN mean filter can remove impulse noise effectively in corrupted images with different noise densities.
Abstract: Improving the quality of a noisy image is important for image applications. Many novel schemes pay great efforts in the removal of impulse noise. Most of them restore noisy pixels only by using the neighboring noise-free pixels, but the relationship between a noisy image and its noise-free one, which denotes the clean image not corrupted by noise, is ignored. So the reconstruction quality cannot be further improved. In this study, we employ a deep-learning fully connected neural network (FCNN) to select top N candidates of neighboring un-corrupted pixels for the restoration of a center noisy pixel in an analysis window. Hence, the mean value of the gray levels of these top N pixels is computed and employed to replace the noisy pixel, yielding the noisy pixel being restored. The experimental results reveal that the proposed deep-learning FCNN mean filter can remove impulse noise effectively in corrupted images with different noise densities.
01 Jan 2021
TL;DR: In this article, a new noise density range sensitive algorithm for the restoration of images that are corrupted by impulse noise is proposed, which replaces the noisy pixel by mean, median or pre-processed values based on noise density of the image.
Abstract: A new noise density range sensitive algorithm for the restoration of images that are corrupted by impulse noise is proposed. The proposed algorithm replaces the noisy pixel by mean, median or pre-processed values based on noise density of the image. The proposed filter uses a unique approach for recovering images corrupted with very high noise densities (over 85%). It also provides significantly better image quality for different noise densities (10–90%). Simulation results show that the proposed filter outperforms in comparison with the other nonlinear filters. At very high noise densities, the proposed filter provides better visual representation with 6.5% average improvement in peak signal-to-noise ratio value when compared to state-of-the-art filters.
TL;DR: A novel simulation approach of the Middleton Class-A model for impulsive noise generation considering symbol-wise transmission in a real Power Line Communication environment is proposed and validated and allows us to compare binary FSK and OFDM under the same theoretical noise conditions.
Abstract: We propose a novel simulation approach of the Middleton Class-A model for impulsive noise generation considering symbol-wise transmission in a real Power Line Communication environment. Then, we validate the proposed approach for both single carrier binary FSK based—and multi-carrier OFDM-based communication systems. Established analytical formulations and obtained simulation results for the impulsive noise variance distribution and the communication system error probability show that in a binary FSK system, the second order of the noise variance per symbol, as well as the error floor, are closely related to the impulsive index A. The error floor decreases when A becomes large, and its asymptotic value is equal to the Bit Error Rate (BER) for an additive white Gaussian noise channel with noise variance equal to the average variance of the impulse noise.
However, in an OFDM-based system, the noise variance per symbol at the output of the demodulator, as well as the error floor in the BER curves, are fixed and independent of the impulsive index A. The proposed analysis allows us to compare binary FSK and OFDM under the same theoretical noise conditions.
TL;DR: Wang et al. as discussed by the authors modeled the denoising problem for impulse noise by Weighted Schatten p-norm minimization (WSNM) with robust principal component analysis (RPCA) and incorporated anisotropic Total Variation (TV) regularization to preserve edge information which was important for clinic detection and diagnosis.