31results about How to "Impulse noise suppression" patented technology

This invention provides an impulsive noise suppression method in orthogonal frequency division multiplexing. The method comprises an equalization and de-mapping step for estimating a preliminary estimation of signal and a total noise estimation by utilizing ideal channel estimation, de-mapping, and pilot insertion technique on received signal; and a SNR comparison step for determining a SNR by dividing said preliminary estimation of signal and said total noise estimation and comparing said SNR with a threshold value.

The invention discloses a historical voice frequency noise detection and elimination method. The existing historical voice frequency noise elimination method has high complexity, depends on the veracity of predicting model coefficients, has a complex algorithm and low efficiency and needs to estimate the statistical property of the noise in advance. The method of the invention comprises: (1) sound modeling: a sound signal can be described with the following method: y(k)=x(k)+j(k)*d(k); (2) short time discrete Fourier transform and spectrogram: an analysis window sliding along time is adopted for windowing and cutting off a non-stable signal so as to decompose the non-stable signal into a series of approximatively stable short signal, and Fourier transform is adopted to analyze the frequency spectrum of each stable short signal; (3) pulse noise detection; (4) pulse noise detection performance analysis; (5) signal repair and reconstruction; and (6) experiment result and analysis. The invention is used for removing the pulse noise in the audio data.

The invention discloses a correlation-based gear signal noise reduction method combining EMD and morphological singular value decomposition. Firstly, the original signal is EMD decomposed to obtain several IMF components, and the noise dominant components in the IMF component are screened according to the statistical characteristics of the white noise according to the autocorrelation function, andthe remaining IMF components are divided into useful components and spurious components according to the mutual relation number method. Then the singular value denoising of the noise dominant component is performed and then reconstructed with the useful component. Finally, the morphological filtering is used to extract the gear defect characteristic signal. The method solves the problem that theloss of useful components of the signal after denoising and the defect characteristics are not easily recognized by the modal aliasing phenomenon in the EMD decomposition, which highlights the defectcharacteristics of the original vibration signal and has a strong self-detail adaptability, while also improve the reliability and accuracy of gear defect information extraction.

The invention relates to the technical field of digital image processing, in particular to a switch weighting vector median filter method utilizing edge detection. The switch weighting vector median filter method improves noise reduction capacity vector median filter, and protects image details. According to the technical scheme, the switch weighting vector median filter method comprises the following steps that the similarity of pixel points in a noise image are compared with that of the corresponding pixel points in a reference image obtained through scalar median filter which is carried out on R, G and B components of the noise image, so that noise monitoring is carried out, vector median filter is carried out on suspected noise points through the weighting method based on edge detection, the noise reduction capacity of vector median filter is improved, and the image tails are protected. The switch weighting vector median filter method is mainly applied to digital image processing.

The invention discloses a filtering method and device based on multichannel noise detection, and the method comprises the steps: obtaining a to-be-detected color image; carrying out the detection of noise points of each color channel of the color image, and obtaining three sub-channel noise identification matrixes; obtaining the noise identification matrix of the color image through the sub-channel noise identification matrixes; carrying out the filtering of pulse noise points in the color image according to the noise identification matrix of the color image, and obtaining a filtered image. According to the invention, the method and device can quickly and effectively inhibit pulse noises, enables the capability of protecting the image edge information and detail features to be more excellent, and improves the image filtering speed and accuracy.

The invention belongs to the technical field of digital image processing, and discloses an impulse noise blurred image nonlinear restoration method and system, and the method specifically comprises the steps: building an impulse noise blurred image degradation mechanism model, and determining an optimization framework; constructing data items to model impulse noise; selecting an image prior item,and constructing a pulse noise blurred image non-blind deconvolution model in combination with the data item; and performing numerical optimization solution on the non-blind deconvolution model by using an iterative reweighted least square algorithm and a conjugate gradient method to obtain a restored image. According to the impulse noise blurred image nonlinear restoration method, properties of impulse noise are analyzed, a nonlinear deconvolution model is designed in a targeted mode, and the nonlinear degenerated impulse noise blurred image restoration problem is solved.

A method for suppressing impulse noises is provided. The method is employed to receive a sample stream x[n], and to detect and suppress the interruption of impulse noise to the samples, wherein the sample stream x[n] includes a plurality of samples and n represents a discrete-time independent variable. The method includes comparing the energy sum of a (k−1)^th sample and a k^th sample multiplied by a first constant with a first threshold, comparing energy sum of the k^th sample and a (k+1)^th sample multiplied by a second constant with a second threshold while the energy sum of the (k−1)^th sample and the k^th sample multiplied by the first constant is greater than the first threshold, and replacing the k^th sample with a first replacement sample to output while the energy sum of the k^th sample and the (k+1)^th sample multiplied by the second constant is greater than the second threshold.

The invention discloses an impulse noise suppression method for sparse iterative covariance estimation in a power line, which comprises the following steps of: at a sending end, transmitting a discrete time domain signal added with a cyclic prefix to a receiving end through a channel; at a receiving end, firstly, projecting a received signal on an empty subcarrier matrix to obtain a mixed signal only containing impulse noise and Gaussian noise; utilizing sparse iterative covariance estimation to obtain the power of the mixed signal so as to obtain the power of the impulse noise; utilizing thelinear mean square error to obtain an estimated value of the impulse noise; subtracting the estimated value of the impulse noise from the received signal to complete suppression of the impulse noise.The method has the advantages that the adopted sparse iterative covariance estimation has higher resolution, and the power of the mixed signal can be estimated more accurately, so that the estimated impulse noise is more accurate, and the suppression effect of the impulse noise is better.

A noise suppression apparatus disclosed is capable of suppressing pulse noise in an input signal even in a situation in which the level of the input signal changes. The pulse noise mixed in the input signal is suppressed, and linear prediction coefficients for the input signal is derived by linear prediction analysis. A prediction residual signal is then calculated from the input signal using the linear prediction coefficient. A threshold value is calculated based on the signal level of the input signal and the signal level of the prediction residual signal is compared with the threshold value. A limit control is performed on the prediction residual signal depending on a result of the comparison, and an output signal is generated based on the prediction residual signal having been subjected to the limit control using the linear prediction coefficient.