Impulse Noise

We propose the audio inpainting framework that recovers portions of audio data distorted due to impairments such as impulsive noise, clipping, and packet loss. In this framework, the distorted data are treated as missing and their location is assumed to be known. The signal is decomposed into overlapping time-domain frames and the restoration problem is then formulated as an inverse problem per audio frame. Sparse representation modeling is employed per frame, and each inverse problem is solved using the Orthogonal Matching Pursuit algorithm together with a discrete cosine or a Gabor dictionary. The Signal-to-Noise Ratio performance of this algorithm is shown to be comparable or better than state-of-the-art methods when blocks of samples of variable durations are missing. We also demonstrate that the size of the block of missing samples, rather than the overall number of missing samples, is a crucial parameter for high quality signal restoration. We further introduce a constrained Matching Pursuit approach for the special case of audio declipping that exploits the sign pattern of clipped audio samples and their maximal absolute value, as well as allowing the user to specify the maximum amplitude of the signal. This approach is shown to outperform state-of-the-art and commercially available methods for audio declipping in terms of Signal-to-Noise Ratio.

Talk: Impulse noise and vibrations in buildings are common from weight drops in gyms. There are a growing emphasis and literature on the matter, often supported by the floor or mat-industry. We will show measurements of impacts from free weights, and Olympic bar with bumper plates and insertion loss for mats and different designs of floating floors. Reference noise levels from weight drops of typical sources are given. Realistic potential energies and impact forces are discussed. The differences between impact sources like bumper plates, grip plates, hand manuals and kettlebells, as well as the physics and nature of some of the typical lifts are discussed, leading to suggestions for possible drop energies and noise levels from weight drops in gyms.

A new framework for removing impulse noise from images is presented in which the nature of the filtering operation is conditioned on a state variable defined as the output of a classifier that operates on the differences between the input pixel and the remaining rank-ordered pixels in a sliding window. As part of this framework, several algorithms are examined, each of which is applicable to fixed and random-valued impulse noise models. First, a simple two-state approach is described in which the algorithm switches between the output of an identity filter and a rank-ordered mean (ROM) filter. The technique achieves an excellent tradeoff between noise suppression and detail preservation with little increase in computational complexity over the simple median filter. For a small additional cost in memory, this simple strategy is easily generalized into a multistate approach using weighted combinations of the identity and ROM filter in which the weighting coefficients can be optimized using image training data. Extensive simulations indicate that these methods perform significantly better in terms of noise suppression and detail preservation than a number of existing nonlinear techniques with as much as 40% impulse noise corruption. Moreover, the method can effectively restore images corrupted with Gaussian noise and mixed Gaussian and impulse noise. Finally, the method is shown to be extremely robust with respect to the training data and the percentage of impulse noise

DMT modulation is an OFDM-based modulation scheme used in ADSL and proposed for various other high-speed broadband access systems. Existing mathematical models for transmission lines make a number of simplistic assumptions about the distribution of noise, in particular, the assumption that impulsive noise originates at either end of the transmission line. It is therefore desirable to improve the accuracy of the transmission-line model to allow better prediction of broadband modem performance, and to further improve the bit-allocation algorithms and equaliser designs used in DMT-based modems. This Thesis presents a new channel model particularly well-suited for simulation of high-speed digital subscriber line systems. The model extends a commonly-used physical channel model by distributing the points of noise ingress along the physical length of the transmission line. Simulation results are presented for a highspeed multicarrier modem operating on channels modelled with both the conv...

We discuss the relations between OFDM and Reed-Solomon codes, pointing out that OFDM can be seen as an analog RS code as soon as a cyclically consecutive range of carriers is not used for transmission, i.e., carries zeros. Thus, these RS codes could be used for correction purposes before a standard channel decoder is invoked. We will con- centrate on

Elimination of combined Gaussian and impulse noises in digital image processing with preservation of image details and suppression of noise are challenging problem. For this purpose, a new filter which is median filters combined with convolutional neural network for Gaussian and salt & pepper noises. The previous methods are application dependents;
some used for impulse noise and other employed only for Gaussian noise. The elimination of Gaussian and impulse noise completed into two steps. First the detection of impulse noise with the rejection of noise by employed of 3 × 3 and 5 × 5 window size median filters. In the second step removal of Gaussian noise performed by residual learning denoising convolutional neural network. It is very favorable and the ability of learning and denoising performance in the field of digital image processing. Denoising convolutional neural network also has active Gaussian noise with an unknown level of noise. Experimental work showed that the proposed method can achieve low loss and root mean square error during training, high peak signal to noise ratio, low mean square error, image quality assessment with good quality and mean absolute error for close prediction between denoised and original color images.

Since last 20 years the Noise Removal techniques have drawn a lot of interest of the researchers, there are types of Noise Removal technique. The process of removing noise from the original image is still a demanding problem for researchers. One of the main tasks of image processing is to distinguish between noise and actual contents so that the un-wanted noise from the image signal can be removed. The distortion of an image by noise is very common that gets introduced during its acquisition processing, compression, transmission, and reproduction. Various terms related to Noise Removal are explored in this paper.

In this paper, new area minimized architecture is proposed for median filters based on modified decomposition algorithm. The modified decomposition replaces the complexity of existing threshold decomposition algorithm such as complex comparators. The proposed algorithm works in two stages, decomposition and recombination. The proposed algorithm removes the need for 0 to 255 threshold gray levels for each input in the given 3×3 window by decomposing each pixel itself using the 255 column counter. Similarly the decomposed pixel is regrouped using 9 column counters. This architecture requires a less number of slices and look up table for its VLSI implementation. The proposed architecture implemented in the XC2S400-6Eft256 FPGA using xilinx compiler version 7.1i. The results prove that the proposed architecture requires less area, optimum speed and equal power than the existing architecture.

This paper describes an impulse noise measurement system for digital subscriber line channels composed by open hardware/software elements developed using the universal software radio peripheral and GNU is not Unix Radio. The proposed system proved to be capable of digitizing real occurrences of impulse noise. The goal is to use such system in large scale measurement campaigns, and then derive stochastic models for behavioral description of impulse noise. These models will provide better methods to reduce impulse noise effects, ever improving the quality of service in digital subscriber line transmissions.

In this paper the spectral features of impulse noise in DSL systems are mapped from the waveform domain onto the symbol domain, considering a QAM receiver. This work can be viewed as part of a full symbol domain model for impulse noise, which should include the amplitude and spectral aspects. Since the available models of impulse noise in DSL are generally waveform-based, this kind of mapping is justified when symbol-based simulations are intended, in order to avoid computational overhead due to demodulating noise samples generated on waveform domain. The analytical development of such mapping is provided as well as some simulation results.

In this paper a novel class of filters designed for the removal of impulsive noise in colour images is presented. The proposed filter family is based on the kernel function which controls the noise suppression properties of the new filtering scheme. The comparison of the new filtering method with the standard techniques used for impulsive noise removal indicates its superior noise removal capabilities and excellent structure preserving properties. The proposed filtering scheme has been successfully applied to the denoising of the cDNA microarray images. Experimental results proved that the new filter is capable of removing efficiently the impulses present in multichannel images, while preserving their textural features.

This paper presents an edge enhancement nucleus and cytoplast contour (EENCC) detector to enable cutting the nucleus and cytoplast from a cervical smear cell image. To clean up noises from an image, this paper proposes a trim-meaning filter that can effectively remove impulse and Gaussian noises but still preserves the sharpness of object boundaries. In addition, a bigroup enhancer is proposed to make a clear-cut separation of the pixels lying in-between two objects. A mean vector difference enhancer is presented to suppress the gradients of noises and also to brighten the gradients of object contours. What is more, a relative-distance-error measure is put forward to evaluate the segmentation error between the extracted and target object contours. The experimental results show that all the aforementioned techniques proposed have performed impressively. Other than for cervical smear images, these proposed techniques can also be utilized in object segmentation of other images.

A new method for removing impulsive noise in color images is presented. The fuzzy metric peer group concept is used to build novel switching vector filters. In the proposed filtering procedure, a set of noise-free pixels of high reliability is determined by applying a highly restrictive condition based on the peer group concept. Afterwards, an iterative detection process is used

Commonly employed reconstruction algorithms in compressed sensing (CS) use the L_2 norm as the metric for the residual error. However, it is well-known that least squares (LS) based estimators are highly sensitive to outliers present in the measurement vector leading to a poor performance when the noise no longer follows the Gaussian assumption but, instead, is better characterized by heavier-than-Gaussian tailed distributions. In this paper, we propose a robust iterative hard Thresholding (IHT) algorithm for reconstructing sparse signals in the presence of impulsive noise. To address this problem, we use a Lorentzian cost function instead of the L_2 cost function employed by the traditional IHT algorithm. We also modify the algorithm to incorporate prior signal information in the recovery process. Specifically, we study the case of CS with partially known support. The proposed algorithm is a fast method with computational load comparable to the LS based IHT, whilst having the advan...

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