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 provides impulse noise suppression and demapping soft decision method and system in a multi-carrier system, wherein the method comprises the following steps of: carrying out statistics on power of receiving signals by an A / D (Analog to Digital) module by a power statistical module, comparing the power with a set threshold, judging whether impulse noise exists in the received signals or not, and if the impulse noise exists, generating an impulse indication signal and a confidence indication signal by the power statistical module; carrying out impulse noise suppression processing on the received signals which are judged to have the impulse noise by an AGC (Automatic Gain Control) module and carrying out power regulation on the receiving signal according to the impulse indication signal; feeding back the power-regulated receiving signal to the A / D module; setting up a confidence weight number according to the confidence indication signal by a demapping module, and carrying out soft decision processing on received information output from the A / D module according to the confidence weight number. The invention can effectively suppress the impulse noise and provides different confidence indications according to different impulse noise types to ensure the reliability of the demapping soft decision.

The invention discloses a method restraining image impulse noise based on an iteration non-local mean value. The method is mainly used for solving the problems that an existing method can not retain image detail information in the denoising process, and a sharp image can not be restored under high noise density. The method comprises the following realizing steps that (1) for a noise image, a histogram method is used for detecting the noise position; (2) an option switch median filter is used for carrying out pre-filtering on the noise image; (3) the iteration non-local mean value method is used for the image processed in a pre-filtering mode is carried out so that the sharp image with the impulse noise effectively restrained can be obtained. A simulation experiment shows that under the circumstance of the impulse noise of different degrees, the method is better in subjective visual effect and objective evaluation result compared with an existing algorithm, and the method can be used for restraining the high-density impulse noise and restoring the sharp image.

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 system and method provide for an adaptive median filter with edge protection and four line buffers to remove the impulse noise for a Bayer pattern image. The system and method separates the Bayer pattern image data into different channels by color and applies the adaptive median filter to remove the impulse noise on each channel. The adaptive median filter has five threshold values that may be modified to achieve different light condition response based on the detected noise level of the image. Thus, proper threshold values with better brightness response to the input pixels may be applied and impulse noise may be suppressed effectively, while preserving image details.

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 kth sample multiplied by a first constant with a first threshold, comparing energy sum of the kth 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 kth sample multiplied by the first constant is greater than the first threshold, and replacing the kth sample with a first replacement sample to output while the energy sum of the kth 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.

The invention discloses an impulse noise inhibition method based on recursion Gauss maximum likelihood estimation of confidence similarity. The method includes the steps that firstly, assuming that, pixels with the gray value of 0 and 255 are pixels polluted by noise, a mask image is obtained, and noise density is calculated; secondly, the restored value of each pixel is determined in a cyclic mode, if a noise point exists, a weighed estimated value is assigned to a target restored image, or else, a current pixel value is assigned to the target restored image, a current pixel is calculated to be a window weight matrix, the estimated value of the current pixel is calculated through the Gauss maximum likelihood estimation, the gray values of pixels which are not polluted by noise in the image are calculated again in each time of iteration, the peak signal to noise ratio of the gray values of the pixels to the gray values of pixels at corresponding positions in a noise image is calculated, and if the peak signal to noise ratio is not increased any more, iteration is stopped. According to the impulse noise inhibition method, impulse noise is effectively inhibited, meanwhile, local details are stored so that a local structure has the better contrast ratio, and the better image restoring effect is achieved.

The invention discloses a salt and pepper noise filtering method based on a color image, relating to the image processing field. In the method, based on a vector median filter for detecting a gradual switch median, a vector median filtering method is utilized to effectively inhibit the pulse noise, and the advantages of the gradual switch median detection method are combined to effectively eliminate the noise and synchronously avoid blurring of the image details; the gradual way is firstly reflected in that the detecting process of each iteration is performed based on the result of previous detection; with respect to a color image with high contaminating rate, densely distributed noise can be gradually detected one by one through the iteration process and is avoided being remained by regarding the noise as the real information of the image; the iteration process prevents the small noise which are densely distributed originally from gathering to be a large noise area; and the filteringprocess is used for filtering the noise based on the previous detecting results.

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.

The invention provides an adaptive non-integer time delay estimation method for use in a low signal-to-noise ratio impulse noise environment. The method is characterized by comprising the following steps: performing self-covariant and cross-covariant operations on an observation sequence, and performing correlation method time delay estimation on a covariant sequence to obtain an integer bit of atime delay estimation value to serve as the initial iteration value of an LMPFTDE algorithm; then performing iteration under a minimum average p norm criterion by using the covariant sequence as an input signal of the LMPFTDE algorithm to obtain a non-integer time delay estimated value; and using the median of the iteration time delay value of a convergence process as a time delay estimated value.In the method, the observation sequence is subjected to covariant processing to weaken the influence of irrelevant noise, enhance the signal-to-noise ratio, suppress impulsive noise, and retain the correlation information between signals. After the observation sequence is subjected to covariant processing, the signal length doubles, more iterations can be carried out, and more iteration value references can make the time delay estimated value closer to a real value.

The invention provides a noise removing circuit having an improved pulse noise detection accuracy irrespective of the intensity of the electric field by selecting a noise detection signal according to the intensity of the electric field. A noise removing circuit comprising a predicting section for predicting the value of an intermediate frequency signal at a predetermined time on the basis of the intermediate frequency signal previously generated before the predetermined time, a detecting section for comparing the difference in magnitude between the predicted value of the intermediate frequency signal at the predetermined time and the value of the previously generated intermediate frequency signal with a predetermined threshold and thereby outputting a second detection signal representing the occurrence of the pulse noise, and a noise removal control unit for selectively outputting to a noise removing unit either a first detection signal representing the occurrence of pulse noise generated by level-detecting the intermediate frequency signal of the received signal or the second detection signal as a signal for interpolating the period when the pulse noise occurs according to the electric field intensity signal generated from the intermediate frequency signal.

The invention provides an underwater sound JANUS signal identification method and system based on a time-frequency spectrum and transfer learning, and the method comprises the steps: receiving an underwater sound signal transmitted through an underwater sound channel, judging a time period of a leading signal possibly containing the JANUS signal in the underwater sound signal based on the characteristics of the leading signal of the JANUS signal, and carrying out the recognition of the JANUS signal. The signals in the time periods are intercepted; performing fractional low-order Fourier synchronous compression transformation on the intercepted signal, and forming a time-frequency image set by using time-frequency image data obtained by transformation; the method comprises the following steps: constructing a transfer learning network, inputting a universal data set on the Internet into the transfer learning network, pre-training the transfer learning network based on the data set to obtain a training model, and inputting a time-frequency image set into the training model for identification, thereby identifying a JANUS signal. According to the method, the impulse noise can be better suppressed, the impulse noise can be suppressed in an underwater acoustic environment with a low signal-to-noise ratio, the influence caused by a multipath effect is reduced, and the recognition rate is improved.

The present invention discloses a method and device for estimating the multipath time delay of an LFM signal, wherein the estimating method includes: receiving a multipath LFM signal, and performing P-NAT transformation on the multipath LFM signal; ‑NAT-transformed multipath LFM signals are subjected to optimal-order fractional Fourier transform; the peak point in the fractional Fourier transform domain is detected, and the peak position offset and time The relation of delay estimates the multipath time delay of described LFM signal; Estimation method provided by the present invention is by carrying out P-NAT transformation to multipath LFM signal, then carries out the fractional order Fourier transform of optimum order, finally to The multipath time delay of the LFM signal is estimated, and since the P-NAT function can suppress impulse noise, the multipath time delay of the LFM signal can be accurately estimated in an Alpha stable distributed noise environment.

The invention belongs to the technical field of non-stationary signal modulation and analysis, and discloses an LFM signal modulation parameter estimation method under alpha stable distribution noise.The method comprises the following steps of carrying out generalized extension linear chirplet transform on a received LFM signal to obtain a time-frequency analysis image; carrying out Radon transform on the time-frequency analysis image, calculating a maximum value of the time-frequency analysis image, and estimating an angle estimation frequency modulation slope corresponding to the maximum value; and constructing a demodulation reference signal by utilizing the frequency modulation slope, multiplying the demodulation reference signal by an original signal to obtain a demodulation signal,carrying out generalized Fourier transform on the demodulation signal, and estimating an initial frequency by utilizing the position of the maximum value. When a generalized signal-to-noise ratio is larger than 0dB, a normalized mean square error of the frequency modulation slope estimation of the LFM signal is smaller than -33dB; and when the generalized signal-to-noise ratio is larger than -6dB,the normalized mean square error of the initial frequency estimation of the LFM signal is less than or equal to -22.4dB.

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 kth sample multiplied by a first constant with a first threshold, comparing energy sum of the kth 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 kth sample multiplied by the first constant is greater than the first threshold, and replacing the kth sample with a first replacement sample to output while the energy sum of the kth sample and the (k+1)th sample multiplied by the second constant is greater than the second threshold.

The invention provides impulse noise suppression and demapping soft decision method and system in a multi-carrier system, wherein the method comprises the following steps of: carrying out statistics on power of receiving signals by an A / D (Analog to Digital) module by a power statistical module, comparing the power with a set threshold, judging whether impulse noise exists in the received signals or not, and if the impulse noise exists, generating an impulse indication signal and a confidence indication signal by the power statistical module; carrying out impulse noise suppression processing on the received signals which are judged to have the impulse noise by an AGC (Automatic Gain Control) module and carrying out power regulation on the receiving signal according to the impulse indication signal; feeding back the power-regulated receiving signal to the A / D module; setting up a confidence weight number according to the confidence indication signal by a demapping module, and carrying out soft decision processing on received information output from the A / D module according to the confidence weight number. The invention can effectively suppress the impulse noise and provides different confidence indications according to different impulse noise types to ensure the reliability of the demapping soft decision.

The invention discloses a method for super-resolution time delay estimation of a narrowband multipath signal in an impulse noise environment, and belongs to the technical field of signal processing. It is characterized in that the exponential Gaussian kernel function is used to suppress impulse noise to obtain a higher accuracy rate of time delay estimation; the multipath signal model is converted by using Fourier transform and inverse Fourier transform to obtain a false signal suitable for the MUSIC algorithm model to achieve super-resolution time delay estimation. Experiments prove that the present invention can obtain a higher correct rate of time delay estimation under the condition of strong impulsive noise.

The invention discloses a method for suppressing image impulse noise based on an iterative non-local mean value, which is mainly used to solve the problems that the existing method cannot preserve image detail information in the denoising process and cannot restore a clear image under high noise density. The implementation steps are: (1) use the histogram method to detect the noise position on the noise image; (2) pre-filter the noise image by selecting and switching the median filter; (3) use iterative non-local filtering on the pre-filtered image The average method is used to obtain a clear image that effectively suppresses impulse noise. Simulation experiments show that the present invention is superior to existing algorithms in subjective visual effects and objective evaluation results under the condition of different degrees of impulse noise, and can be used to suppress high-density impulse noise and restore clear images.

A liquid crystal display drive circuit that suppresses a pulse noise in a liquid crystal display panel without increasing a mounting area is offered. A common signal COMi is varied in a staircase waveform with an increment of 1 / 3 VLCD in such a way that a high electric potential VLCD->a first intermediate electric potential VLCD1->a second intermediate electric potential VLCD2->a low electric potential VSS, when the common signal COMi varies by the maximum amplitude, in other words, when the common signal COMi makes a transition from the high electric potential VLCD to the low electric potential VSS. The segment signal SEGj is varied similarly in a staircase waveform in such a way that the low electric potential VSS->the second intermediate electric potential VLCD2->the first intermediate electric potential VLCD1->the high electric potential VLCD when the segment signal SEGj makes a transition from the low electric potential VSS to the high electric potential VLCD. A peak value of the pulse noise due to a capacitive coupling can be reduced to 1 / 3 of that in the prior art.

The invention discloses a pulse noise suppression method for sparse iterative covariance estimation in power lines. At the sending end, the discrete time-domain signal with a cyclic prefix is ​​transmitted to the receiving end through a channel; at the receiving end, the received signal is first projected On the empty subcarrier matrix, a mixed signal containing only impulse noise and Gaussian noise is obtained; using sparse iterative covariance estimation, the power of the mixed signal is obtained, and then the power of the impulse noise is obtained; the estimated value of the impulse noise is obtained by using the linear mean square error ; Subtract the estimated value of impulse noise from the received signal to complete the suppression of impulse noise; the advantage is that the sparse iterative covariance estimation adopted has a higher resolution, and can estimate the power of the mixed signal more accurately, so that The estimated impulse noise is more accurate, so that the suppression effect of 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.