Impulse Noise

Images are vulnerable to anomalies called as distortion or corruption during the transmission of signals. When the impulse noise encounters the image quality gets disturbed, to reconstruct the pixel information with reduced complexity, we hypothesize an efficacious VLSI architecture to drastically reduce impulse noise in pictures leveraging edge preserving filter. The method is compared with two mask sizes in terms of performance evaluation. To take advantage of detailed identification including certain PSNR generalizations and picture flexibility, the proposed framework will prove to be more effective than the conventional poor complexity paradigms. Throughout the low noise concentrations, the edge conserving median filter spotlights stellar performance and indeed in high noise concentrations through high window sizes. These effects are demonstrated through the simulation results. The comparisons are framed between area and power consumptions of 3*3 and 5*5 window sizes concluding which window size gives better performance in terms of those constraints.

is planned at February/March 2007 in Wisła. The conference is the forum for all environmental vibroacoustics fields. Particularly it concern to traffic noise, industry noise, vibroacoustics of machines, room acoustics, noise protection and similar problems. During the School the theoretical works, experimental, measuring, technical, applied and normative ones are presented. The School lectures, and other conference materials, will be published in the "Materials of the XXXV Winter School on Vibroacoustical Hazards Suppressions" (in Polish) edited by Dr. Mieczysław Roczniak (chairman of the conference). This publication is intend to participants of the School and for many libraries in Poland. Other information about XXXV WS on VHS you can find at address .

Reduction of mixed noise is an ill posed problem for the occurrence of contrasting distributions of noise in the image. The mixed noise that is usually encountered is the simultaneous presence of additive white Gaussian noise (AWGN) and impulse noise (IN). A standard approach to denoise an image with such corruption is to apply a rank order filter (ROF) followed by an efficient linear filter to remove the residual noise. However, ROF cannot completely remove the heavy tail of the noise distribution originating from the IN and thus the denoising performance can be suboptimal. In this paper, we present a variational step to remove the heavy tail of the noise distribution. Through experiments, it is shown that this approach can significantly improve the denoising performance of mixed AWGN-IN using well-established methods.

In this paper a novel adaptive filtering scheme for impulsive noise removal in color images is presented. The noise detection algorithm is based on the concept of aggregated distances assigned to the pixels belonging to the filtering window. The value of the difference between the accumulated distance assigned to the central sample and to the pixel with the lowest rank, serves as an indicator of the presence of impulses injected into the image by the noise process. The output of the proposed filter is a weighted mean of the central pixel of the filtering window and the vector median of its samples. The obtained results show that the proposed filter outperforms existing impulse noise removal techniques for low noise contamination and can be used in various applications in which the detail preserving reduction of impulses play an important role.

During acquisition and transmission, noise can be introduced into images. The main problem of image processing is to effectively remove noise from an image, but keep its features intact. The impulse noise is removed by using Gaussian filter. This filtering process assumes a Gaussian spatial profile in the neighborhood of noisy pixel. Image registration can be defined as the process of transformation of various image data sets into one co-ordinate system. The need of image registration is its ability to compare or integrate the data obtained from the different measurements. For any image registration, filtering is necessary to remove the noise. Image registration is classified into image segmentation and Scale invariant feature transformation. The last process is the template matching. This method provides best filtering at higher noise density. It also provides maximum cross correlation coefficient for registered images. Key points: Gaussian noise, K-means clustering algorithm, Scal...

Images transmitted via ATM networks suffer from quality degradation due to buffer overflow or cell header errors which cause ATM cells to be lost. This paper presents a new approach to conceal the errors in the received images by the application of novel error recovery techniques to the decomposed DCT-coefficient subimages of the corrupted image. These techniques were developed to recover images corrupted by impulsive noise. Since decomposing the corrupted image into the DCT-coefficient subimages generates low resolution images corrupted by impulsive noise, all the techniques used t o recover images corrupted by impulsive noise can be used to recover the subimages and hence the corrupted image. In this paper, we study the performance of different iterative and non-linear techniques to recover the corrupted subimages. The quality of the recovered image using these techniques is better than the quality obtained by many classical error concealment techniques.

Images transmitted via ATM networks suffer from quality degradation due to buffer overflow or cell header errors which cause ATM cells to be lost. This paper presents a new approach to conceal the errors in the received images by the application of novel error recovery techniques to the decomposed DCT-coefficient subimages of the corrupted image. These techniques were developed to recover images corrupted by impulsive noise. Since decomposing the corrupted image into the DCT-coefficient subimages generates low resolution images corrupted by impulsive noise, all the techniques used to recover images corrupted by impulsive noise can be used to recover the subimages and hence the corrupted image. We study the performance of new techniques to recover the corrupted subimages. The quality of the recovered image using these techniques is better than the quality obtained by many classical error concealment techniques

A simple method of improving orthogonal frequency division multiplexing (OFDM) receiver performance in an impulsive noise environment is to precede a conventional OFDM demodulator with blanking nonlinearity. This method is widely used in practice since it is efficient and very simple to implement. However, performance analysis of this scheme has not yet appeared. In this paper, we study performance of the OFDM receiver with blanking nonlinearity in the presence of impulsive noise. Closed form analytical expressions for the signal-to-noise ratio (SNR) at the output of blanking nonlinearity and the optimal blanking threshold that maximizes SNR are derived. Simulation results are provided that show good agreement with theory if the number of OFDM subcarriers is sufficiently large.

Orthogonal frequency division multiplexing (OFDM) is a technique used for terrestrial digital video broadcasting (DVB-T) and many other modern applications. The longer OFDM symbol duration provides an advantage in a presence of weak impulsive noise, because impulsive noise energy is spread among simultaneously transmitted OFDM sub-carriers. However, it has been recently recognized that this advantage turns into a disadvantage if the impulsive noise energy exceeds certain threshold. In this paper the algorithm for impulsive noise suppression in OFDM receivers is proposed and investigated. Whereas traditional methods for impulsive noise suppression are implemented in a time domain before OFDM demodulation, proposed algorithm compensates impulsive noise in a frequency domain after OFDM demodulation and channel equalization. The method is applied to DVB-T and its performance is studied by means of simulation 1 .

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This paper considers the class of non-Gaussian noise modeled as a combination of a white Gaussian process and a filtered Poisson process of which the envelope amplitude obeys the Rayleigh distribution. The error probability for M-QAM systems is shown to be strongly dependent not only on the signal to noise ratio SNR, but also on the average number of impulses in the detection interval γ (hereafter referred to as impulse rate), and the impulse noise power to Gaussian noise power ratio β. Numerical results show that the worst case symbol error probability varies linearly with γ when the noise is purely impulsive, and almost linearly with β when the noise is non-Gaussian over a large range of values of β. In the latter case, this linear variation is limited by the two extremities, the left is characterized by the impulse noise and the right by the Gaussian noise.

The application of ICT tools and techniques has become necessary in various fields in Business, Technology, Engineering and science as it results in accuracy, flexibility, maintainability and timely operation which are vital for growth and accountability all over the world. The paper seeks to present a computer solution to the problem of VAT collection and recording system in Gombe State Integrated Office. A computer program was designed and coded using Visual Basic programming language that utilizes Access Database Structure. The program was capable of automatic recording and generating of receipts to payers. The payment details are automatically transmitted on-line/real-time to the head-office. The results from the program usage shows significant time was saved during processing and documentation details. Also, it completely eliminated fraudulent practices caused by falsification of receipts, and non/partial remittances. The system made it possible for increased revenue from VAT. ...

An optimal algorithm is described for solving the deconvolution problem of the form ku := ∫ t 0 k(t − s)u(s)ds = f(t) given the noisy data fδ, ||f − fδ|| ≤ δ. The idea of the method consists of the representation k = A(I+S), where S is a compact operator, I + S is injective, I is the identity operator, A is not boundedly invertible, and an optimal regularizer is constructed for A. The optimal regularizer is constructed using the results of the paper MR 40#5130.

Strong impulse noise is widely known to adversely affect conventional receivers designed only to consider background noise. Although sophisticated receivers offer substantial performance improvements, fully exploiting the impulse statistics, which are generally not time invariant and are difficult to model accurately, can be unrealistic. Alternatively, without making assumptions regarding the underlying impulse channel model, the authors' previously developed robust decoding schemes can achieve performance equivalent to that of their optimal counterparts in impulse noise channels. In this letter, a robust turbo decoding metric is proposed to address the inherent memory in an impulse channel: a 2-D trellis is used to adapt for channel state transitions when statistics on the memory impulse channel model are lacking. The simulation results verified the robustness of the proposed decoder in harsh environments.

Communication systems are susceptible to impulse noise, particularly when the impulse statistics are not timeinvariant and are difficult to accurately model. To address the challenge of impulse noise, a robust and efficient decoding scheme was devised for single carrier convolutionally coded transmissions over memory impulse noise channels. By accommodating channel states, but without relying on statistical knowledge of impulses, the Viterbi algorithm (VA) based on an expanded set of trellis states, was employed to perform maximum likelihood decoding. A detailed analysis of complexity was offered; the analytical results reinforced the efficiency of the proposed scheme compared with the traditional VA. The simulation results indicated that the proposed decoding scheme is compellingly robust: the bit error probability performance level attained using the proposed decoder is remarkably close to that of an optimal decoder, which uses impulse statistics; furthermore, the proposed decoder was superior to the alpha-penalty function decoder, which neglects the channel memory property and experiences an error floor, in fairly general circumstances.

A new and efficient algorithm for high-density salt and pepper noise removal in images and videos is proposed. In the transmission of images over channels, images are corrupted by salt and pepper noise, due to faulty communications. Salt and Pepper noise is also referred to as Impulse noise. The objective of filtering is to remove the impulses so that the noise free image is fully recovered with minimum signal distortion. Noise removal can be achieved, by using a number of existing linear filtering techniques. We will deal with the images corrupted by salt-and-pepper noise in which the noisy pixels can take only the maximum or minimum values (i.e. 0 or 255 for 8-bit grayscale images).

In children with normal cochlear acuity, middle ear fluid often abolishes otoacoustic emissions (OAEs), and negative middle ear pressure (NMEP) reduces them. No convincing evidence of beneficial pressure compensation on distortion product OAE (DPOAE) has yet been presented. Two studies aimed to document effects of NMEP on transient OAE (TEOAE) and DPOAE. In Study 1, TEOAE and DPOAE pass/fail responses were analyzed before and after pressure compensation in 50 consecutive qualifying referrals having NMEP from À100 to À299 daPa. Study 2 concentrated on DPOAE, recording both amplitude (distortion product amplitude) and signal-to-noise ratio (SNR) before and after pressure compensation. Of the 20 participants, 5 had both ears qualifying. An effect of compensation on meeting a pass criterion was present in TEOAE for both left and right ear data in Study 1 but not demonstrable in DPOAE. In Study 2, the distortion product amplitude compensation effect was marginal overall, and depended on recording frequency band. SNR values improved moderately after pressure compensation in the two (overlapping) sets of single-ear data. In the five cases with both ears qualifying, a stronger compensation effect size, over 3 dB, was seen. The absolute dependence of SNR on frequency was also strongly replicated, but in no analysis, the frequency  compensation interaction was significant. Independent of particular frequency range, the data support a limited SNR improvement in 2 to 3 dB for compensation in DPOAE, with slightly larger effects in ears giving SNRs between 0 dB and þ6 dB, where pass/fail cutoffs would generally be located.

In this paper a survey of power line communications has been presented. As power line distribution networks are available in almost each building that is mainly used for the supply of electrical power, it will be very interesting if such media could be used for the transmission of data. The demand of low cost telecommunication, broadband and access to internet services is increasing day by day and applied a driven force leading to further research in the field of power line communications. A number of organizations are working on power line communications and more advanced schemes are being proposed so that a new dimension could be added to the potential application of established wire line infrastructure.

This paper examines advanced modulation and coding schemes that are critical for future-ready wireless access technologies. It focuses on techniques such as higher-order QAM, LDPC, and polar codes that enhance data rates, spectral efficiency, and robustness against noise. By addressing challenges in reliability, latency, and bandwidth utilization, these innovations support the performance demands of 5G, 6G, and beyond, enabling ultra-reliable, high-capacity wireless communication for next-generation networks and applications.

A universal binary neuron (UBN) operates with the complex-valued weights and the complex-valued activation function, which is the function of the argument of the weighted sum. This makes possible the implementation of the nonlinearly separable (non-threshold) Boolean functions on the single neuron. Hence the functionality of the UBN is incompatibly higher than the functionality of the traditional perceptron, because this neuron can implement the non-threshold Boolean functions. The UBN is closely connected with the discrete-valued multi-valued neuron (MVN). This is also a neuron with the complex-valued weights and the complex-valued activation function, which is the function of the argument of the weighted sum. A close relation of the MVN and UBN and of the multiple-valued threshold functions and P-realizable Boolean functions is considered in this paper. A modified learning algorithm for the UBN is presented. It is shown that such classical nonlinearly separable problems as the XOR and Parity n can be easily solved using a single UBN, without any network.

Impulsive noise filtering is an important problem of image processing. The problem of noise elimination is closely connected with the problem of maximal preservation of image edges. The requirement of maximal preservation of edges is especially important for images corrupted by impulsive noise with a low corruption rate. To avoid smoothing of the image during filtering, all noisy pixels must be detected. Then only these detected pixels must be corrected. We present in this paper two solutions to the edge preservation problem. The first one is an impulse detector. This detector is based on a comparison of signal samples within a narrow rank window. It is quite efficient for precise detection of impulses in images corrupted by impulsive noise with a low corruption rate. The second solution is based on threshold Boolean filtering, when the binary slices of an image, obtained by the threshold decomposition, are processed by original Boolean functions.

This paper presented a study of three algorithms, the<br> equalization algorithm to equalize the transmission channel with ZF<br> and MMSE criteria, application of channel Bran A, and adaptive<br> filtering algorithms LMS and RLS to estimate the parameters of the<br> equalizer filter, i.e. move to the channel estimation and therefore<br> reflect the temporal variations of the channel, and reduce the error in<br> the transmitted signal. So far the performance of the algorithm<br> equalizer with ZF and MMSE criteria both in the case without noise,<br> a comparison of performance of the LMS and RLS algorithm.

By normalizing the values of its pixels with respect to the length of the gray scale used, a monochromatic image is interpreted as a fuzzy relation R. We find the GEFS (resp. SEFS) of R, that is the greatest (resp. smallest) eigen fuzzy set with respect to the max-min (resp. min-max) operator. The reconstruction of R is achieved via a genetic algorithm whose initial population of chromosomes is formed by random gray images. The fitness function is based on the GEFS and SEFS of R and of each i mage-chromosome: t he r econstructed i mage i s t he image-chromosome with the highest fitness v alue. I n o ur tests w e h ave u sed 3 50 i mages o f s izes 2 56 × 256 ex tracted f rom t he S IDBA i mage d ataset consisting of 10000 images (www.cs.cmu.edu/~cil/ vision.html).

A new fuzzy image filter controlled by interval-valued fuzzy sets (IVFS) is proposed for removing noise from images. The proposed approach is based on IVFS entropy application. IVFS makes it possible to take into account the total uncertainty inherent to image processing, and particularly noise removal is considered. Interval-valued fuzzy sets entropy is used as a tool to perform histogram analysis in order to find all major homogeneous regions at the first stage. Then, an efficient peak-finding algorithm is employed to identify the most significant peaks of the histogram (1) and a noise filtering process (2) that estimates the original value of each noisy pixel (utilizing the global information from (1) and the local information of the image pixels) is proposed. Experimental results have demonstrated that the proposed filter can outperform some well-known classical and fuzzy filters in preserving image details while suppressing impulse noise and reducing Gaussian noise. The main advantage of the proposed technique is to restrict the number of thresholds or parameters which have to be tuned.

No-reference image quality assessment needs no prior knowledge of reference image. A new fuzzy image quality measure (built from interval type-2 fuzzy sets (IFS2)) is compared with experimental psycho-visual data. The proposed measure is based on IFS2 entropy applied on synthetic images. A recently performed psycho-visual experiment provides psycho-visual scores on some synthetic images, and comprehensive testing demonstrates the good consistency between these scores and the quality measures we obtain. The proposed measure has been too compared with full-reference quality measures (or faithfullness measures) like SSIM and gives satisfactory performance.

Digital signal processing techniques have gained steadily in importance over the past
few years in many areas of science and engineering and have transformed the character of
instrumentation used in laboratory and plant. This is particularly marked in acoustics, which has
both benefited from the developments in signal processing and provided significant stimulus for
these developments. As a result acoustical techniques are now used in a very wide range of
applications and acoustics is one area in which digital signal processing is exploited to its limits.
For example, the development of fast algorithms for computing Fourier transforms and the
associated developments in hardware have led to remarkable advances in the use of spectral
analysis as a means of investigating the nature and characteristics of acoustic sources. Speech
research has benefited considerably in this respect, and, in a rather more technological
application, spectral analysis of machinery noise provides information about changes in machine
condition which may indicate imminent failure. More recently the observation that human and
animal muscles emit low intensity noise suggests that spectral analysis of this noise may yield
information about muscle structure and performance.

A new image denoising algorithm is proposed to restore digital images corrupted by impulse noise. It is based on two dimensional cellular automata (CA) with the help of fuzzy logic theory. The algorithm describes a local fuzzy transition rule which gives a membership value to the corrupted pixel neighborhood and assigns next state value as a central pixel value. The proposed method removes the noise effectively even at noise level as high as 90%. Extensive simulations show that the proposed algorithm provides better performance than many of the existing filters in terms of noise suppression and detail preservation. Also, qualitative and quantitative measures of the image produce better results on different images compared with the other algorithms.

This article rcports on the performance of audio, video, multimedia, and othcr high-datarate in-home networking applications. Thc article starts by describing the pioblems encountered in power linc communication channcls in terms of frequency response and noise characteristics, and explains how in-home power line LANs can he cxtended to the neighborhood transformer. OFDM physical layers providing speeds of 45 Mbis and 200 Mbis as well as QoS and security arc introduccd. Finally, thc results of large tests involving several thousands of n o d e s a r e described.

Los métodos de medida de la exposición a altos niveles sonoros están orientados fundamentalmente al ámbito laboral, sin embargo la escucha de música en reproductores personales es una fuente de exposición cuyo análisis necesitaría de otros instrumentos. Presentamos el diseño de un sistema capaz de analizar y evaluar esta exposición usando varios métodos: los auriculares personales del reproductor sobre un HATS diseñado al efecto o bien unos auriculares calibrados y dotados de micrófonos para medir los niveles sonoros, más un sistema de análisis de las señales. El resultado es un sistema que en base a la información facilitada por el individuo a prueba y el análisis normativo de la señal es capaz de estimar sus pérdidas auditivas. Se aporta un nuevo instrumento de medida útil en todos los ámbitos, desde el caso de los reproductores personales a cualquier otra situación sin la presencia de la persona afectada.

In the field of image processing, removing impulse noise has been regarded as one of the most important tasks, primarily because of the noise pattern it presents. Existing filters used the effect of only those non-noisy pixels which were present inside the specified windows ignoring the effect of the non-noisy pixels present in the surrounding windows. So, the least distant non-noisy pixels in the present window as well as in the surrounding windows may have an influence on the present window's noisy pixels. Hence, considering the above factors, in this paper, a two-step technique named KMDCIFF (K-medoid clustering identified fuzzy filter) is proposed for removing impulse noise from digital images. In the proposed KMDCIFF algorithm, the first step is noise detection using K-medoid clustering, followed by a fuzzy logic-acquainted noise reduction strategy that utilizes the least distant local and non-local non-noisy pixels for removal operation. The detection process involves the application of K-medoid clustering on all 5×5 windows produced by centering each pixel of the considered image. In order to remove noise, a 7×7 window is constructed with each detected noisy pixel in the center. Analyzing the impact of the least distant local and nonlocal pixel on each noisy pixel, the same is replaced by an estimated pixel's intensity value obtained from the most influential non-noisy pixels. KMDCIFF is evaluated using wellknown metrics for diverse types of images. At a high noise density of 80%, KMDCIFF exhibited significant peak-signal-to-noise-ratios (PSNRs) of 26.97 dB and 29.67 dB and structural similarity indexes (SSIMs) of 0.8045 and 0.9288 on random and fixed valued impulse noise impacted Lena image, respectively. Comparing the results of the contemporary study to those of previous studies of a similar kind in this sector, the results are unswervingly astounding.

Removal of random valued noisy pixel is extremely challenging when the noise density is above 50%. The existing filters are generally not capable of eliminating such noise when density is above 70%. In this paper a region wise density based detection algorithm for random valued impulse noise has been proposed. On the basis of the intensity values, the pixels of a particular window are sorted and then stored into four regions. The higher density based region is considered for stepwise detection of noisy pixels. As a result of this detection scheme a maximum of 75% of noisy pixels can be detected. For this purpose this paper proposes a unique noise removal algorithm. It was experimentally proved that the proposed algorithm not only performs exceptionally when it comes to visual qualitative judgment of standard images but also this filter combination outsmarts the existing algorithm in terms of MSE, PSNR and SSIM comparison even up to 70% noise density level.

Channel coding for an impulse noise environment modeled as a Cauchy-Gaussian mixture is addressed. Code design based on the pairwise probability of error indicates the suitability of Hamming distance as a primary determinant of performance. Simulation results of code performance for the maximum likelihood (ML) and several suboptimal metrics are presented.

The paper presents a comparative study of performance of 2D vector median filters which calculate in various color coordinates the distances between the vectors corresponding to pixels of color images. The paper deals with the problem of the best choice of the color space for vector median calculations. The problem is considered for the task of restoration of color images corrupted with impulse noise. The comparison has been made experimentally, using 4 test images and 3 types of impulse noise. The results have been evaluated by consideration of quadratic errors in the Munsell space as well as by comparing their subjective quality.

Digital images are often affected by impulse noise during image acquisition and/or transmission over communication channel. A Neural Based Post Processing Technique for Image Quality Enhancement (NBPPTIQE) for enhancing digital images corrupted by impulse noise is proposed in this paper. The proposed filter is an intelligent filter obtained by aptly combining a Nonlinear Filter (NF), Modified Canny Edge Detector (MCED) and a Feed forward Adaptive Neural (FAN) Network. The internal parameters of the Feed Forward Neural Network are adaptively optimized by training of well known images. The most distinctive feature of the proposed filter offers good line, edge, and fine detail preservation performance and also effectively removes impulse noise from the image. Extensive simulation results show that the proposed Post Processing Technique can be used for efficient enhancement of digital images corrupted by impulse noise without distorting useful information in the image. The performance of proposed filter is compared with median based filter and Neural Filter and shown to be more effective in terms of eliminating impulse noise and preserving edges and fine details of digital images.

Asymmetric Trimmed Median Filter for Image denoising is proposed in this paper. This technique can be used for restoring the images extremely corrupted with random valued impulse noise. This paper introduces an impulse detection technique and decision based median filter for restoring the corrupted images. The detection technique is used for discriminating between corrupted and uncorrupted image pixels. The corrupted pixels are restored using Asymmetric trimmed median filter. The performance of the proposed restoring scheme is evaluated with random valued impulse noise for different test images. Simulation results show that this method is significantly better than a number of existing techniques in terms of image restoration and noise detection.

A neural filtering technique is proposed in this paper for restoring the images extremely corrupted with random valued impulse noise. The proposed intelligent filter is carried out in two stages. In first stage the corrupted image is filtered by applying an asymmetric trimmed median filter. An asymmetric trimmed median filtered output image is suitably combined with a feed forward neural network in the second stage. The internal parameters of the feed forward neural network are adaptively optimized by tr aining of three well known images. This is quite effective in eliminating random valued impulse noise. Simulation results show that the proposed filter is superior in terms of eliminating impulse noise as well as preserving edges and fine details of digital ima ges and results are compared with other existing nonlinear filters.

One of the main concerns of evolvable and adaptive systems is the need of a training mechanism, which is normally done by using a training reference and a test input. The fitness function to be optimized during the evolution (training) phase is obtained by comparing the output of the candidate systems against the reference. The adaptivity that this type of systems may provide by re-evolving during operation is especially important for applications with runtime variable conditions. However, fully automated self-adaptivity poses additional problems. For instance, in some cases, it is not possible to have such reference, because the changes in the environment conditions are unknown, so it becomes difficult to autonomously identify which problem requires to be solved, and hence, what conditions should be representative for an adequate re-evolution. In this paper, a solution to solve this dependency is presented and analyzed. The system consists of an image filter application mapped on an evolvable hardware platform, able to evolve using two consecutive frames from a camera as both test and reference images. The system is entirely mapped in an FPGA, and native dynamic and partial reconfiguration is used for evolution. It is also shown that using such images, both of them being noisy, as input and reference images in the evolution phase of the system is equivalent or even better than evolving the filter with offline images. The combination of both techniques results in the completely autonomous, noise type/level agnostic filtering system without reference image requirement described along the paper.

Partial Discharge (PD) is the most important source of insulation degradation in power transformers. In order to prevent catastrophic failures in transformers, PDs need to be located as soon as possible so that maintenance measures can be taken in time. Due to the structural complexity of windings, locating the PD source inside a transformer winding is not a simple task. In this paper, the efficacy of the proposed Frequency Response Assurance Criterion (FRAC) correlation technique for finding the location of a PD source in a transformer winding is evaluated and compared with two well-known correlation techniques in this regard, that are Time domain correlation and KullbackLeibler divergence. The responses of the winding to PD pulses, generated by Heidler function, of known pulse duration applied in parallel along the whole sections of the winding are considered as the reference data. In addition, the captured responses of the winding generated by injecting the PD pulses of arbitrary...

In this paper a novel method for effectively denoising the extremely corrupted image by fixed value impulse noise using robust estimation based filter is proposed. The proposed algorithm classifies the pixels of localized window in to "corrupted" or "uncorrupted" and removes only corrupted pixels by robust estimation or by mean of the processed neighboring pixels. It is shown that the proposed filter effectively removes the impulse noise while preserving the good image quality. The visual and quantitative results prove that the performance of proposed filter in the preservation of edges and details is better even at noise level as high as 95%.

Future NASA exploration missions will increasingly require sampling, in-situ analysis and possibly the return of material to Earth for further tests. One of the challenges to addressing this need is the ability to drill using for low axial loading while operating from light weight platforms (e.g., lander, rover, etc.) as well as operate at planets with low gravity. For this purpose, the authors developed the Ultrasonic/Sonic Driller/Corer (USDC) jointly with Cybersonics Inc. Studies of the operation of the USDC at high power have shown there is a critical need to self-tune to maintain the operation of the piezoelectric actuator at resonance. Performing such tuning is encountered with difficulties and to address them an extremum-seeking control algorithm is being investigated. This algorithm is designed to tune the driving frequency of a time-varying resonating actuator subjected to both random and high-power impulsive noise disturbances. Using this algorithm the performance of the actuator is monitored on a time-scale that is compatible with its slowly time-varying physical characteristics. The algorithm includes a parameter estimator, which estimates the coefficients of a function that characterizes the quality factor of the USDC. Since the parameter estimator converges sufficiently faster than the time-varying drift of the USDC's physical parameters, the proposed extremum-seeking estimation and control algorithm is potentially applicable for use as a closed-loop health monitoring system. Specifically, this system may be programmed to automatically adjust the duty-cycle of the sinusoidal driver signal to guarantee that the quality factor of the USDC does not fall below a user-defined set-point. Such fault-tolerant functionality is especially important in automated drilling applications where it is essential not to inadvertently drive the piezoelectric ceramic crystals of the USDC beyond their capacities. The details of the algorithm and experimental results will be described and discussed in this paper.

This paper presents a new algorithm for the denoising of images corrupted with random-valued impulse noise (RVIN). It employs a switching approach that identifies the noisy pixels in the first stage and then estimates their intensity values to restore them. Local statistics of the textons in distinct orientations of the sliding window are exploited to identify the corrupted pixels in an iterative manner; using an adaptive threshold range. Textons are formed by using an isometric grid of minimum local distance that preserves the texture and edge pixels of an image, effectively. At the noise filtering stage, fuzzy rules are used to obtain the noise-free pixels from the proposed tridirectional pixels to estimate the intensity values of identified corrupted pixels. The performance of the proposed denoising algorithm is evaluated on a variety of standard gray-scale images under various intensities of RVIN by comparing it with state-of-the-art denoising methods. The proposed denoising algorithm also has robust denoising and restoration power on biomedical images such as, MRI, X-Ray and CT-Scan. The extensive simulation results based on both quantitative measures and visual representations depict the superior performance of the proposed denoising algorithm for various noise intensities.

Impulsive Noise is the sudden burst noise of short duration. Mostly it causes by electronic devices and electrosurgical noise in biomedical signals at the time of acquisition. In this work, Electrocardiograph (ECG) signal is considered and tried to remove impulsive noise from it. Impulsive noise in ECG signal is random type of noise. The objective of this work is to remove the noise using different adaptive algorithms and comparison is made among those algorithms. Initially the impulsive noise in sinusoidal signal is synthesized and tested for different algorithms like LMS, NLMS, RLS and SSRLS. Further those algorithms are modified in a new way to weight variation. The proposed novel approach is applied in the corrupted ECG signal to remove the noise. The effectiveness of the proposed approach is verified for ECG signal with impulsive noise as compared to the traditional approaches as well as previously proposed approaches. Also the performance of our approach is validated by SNR co...

Elimination of noise is a necessary and challengeable operation in image processing. Before performing any operation, images have to be first restored. Images are corrupted by noise during image acquirement and transmission. Noise and blurring effects always corrupts any recorded image. To reduce the impulse noise level in digital images a novel algorithm FSM(Fuzzy Switching Median) filter is proposed in this paper .In this algorithm first phase is used to detect the impulse noise using clustered based approach, which works as impulse noise detector. And the second phase which is also called as filtering phase replaces the detected noise pixel, which also includes fuzzy reasoning to deal with uncertainties present in local information FSM, is capable of filtering all types of impulse noise.