77 results about "Wiener filter" patented technology

The invention relates to a cloud edge cooperative computing framework and processing method for multi-modal data stream fusion processing, and the method comprises the following steps: S1, carrying out the noise suppression based on an original data stream collected by an edge computing node through employing an improved Wiener filtering algorithm, achieving the signal denoising through the adaptive threshold wavelet transformation, and obtaining a cloud edge data stream; and a timestamp alignment technology is utilized to solve the problem of time delay difference of multi-modal data, and a space-time alignment purified data stream is generated. Through combination of the improved Wiener filtering algorithm and the adaptive threshold wavelet transform, the noise suppression efficiency of the original data stream is significantly improved, the timestamp alignment technology effectively solves the time delay difference of the multi-modal data, the generation of the space-time alignment purified data stream ensures that the subsequent processing has a unified time sequence benchmark, and the efficiency of noise suppression of the original data stream is improved. The space-time attention fusion network adopts a collaborative architecture effect of a bidirectional gating circulation unit and a lightweight 3D convolutional network.

The invention discloses a target identification method for multi-sensor data fusion, particularly relates to the technical field of data fusion, and comprises a dynamic weight fusion module, a double-branch neural network and an abnormal sensing compensation system. Data are synchronously collected through a visible light camera, a thermal infrared imager and a millimeter wave radar, and a standardized feature map is generated through sensor specificity preprocessing; the dynamic weight fusion module generates an adaptive weight matrix based on the real-time confidence score and the environmental parameters, emphasizes infrared data when illumination suddenly changes, and improves intelligent distribution of radar weights in a rain and fog environment; the combined feature map after weight fusion is input into a double-branch neural network, a channel self-calibration branch suppresses interference noise, and a target category and coordinates are output after residual connection optimization; when the recognition confidence is insufficient, the abnormal compensation system starts visible light Wiener filtering restoration, generative adversarial network infrared compensation and radar multi-frame accumulation algorithms, and the system reliability is ensured when a single sensor fails.

The invention particularly relates to a voice noise reduction method suitable for different noise environments, and relates to the technical field of voice signal processing. Analyzing a noise environment and characteristics; adaptive selection of a noise reduction algorithm; adaptive noise reduction processing is executed; and noise reduction effect evaluation and iterative optimization are carried out. According to the invention, refined noise classification is matched with the algorithm, so that the limitation of a traditional single algorithm in a complex environment is solved; the steady-state noise is dynamically counteracted by adopting an adaptive filter, the frequency domain gain of the unsteady-state noise is adjusted in real time through Wiener filtering, and the pulse noise is subjected to dynamic threshold processing by combining median filtering and wavelet transform; especially, the design of cooperation factors in wavelet transform can accurately distinguish signal details and noise: intensively suppress pulse noise, loosely retain details such as voices and consonants, and realize dynamic balance of noise reduction intensity and signal distortion.

The invention relates to the field of voice signal processing and voice recognition, and discloses a voice recognition enhancement method and system based on harmonic model fundamental frequency optimization and RNN noise suppression, and the method comprises the steps: collecting a voice signal in a noise environment in real time, carrying out the framing and windowing of the voice signal, and generating multi-dimensional time-frequency data; estimating the fundamental frequency of each frame of voice through a cepstrum analysis method, and screening effective fundamental frequency frames according to a confidence coefficient threshold to form a fundamental frequency characteristic matrix; dynamically adjusting the noise power spectrum of the Wiener filter under the drive of the fundamental frequency characteristic matrix, and carrying out dislocation fusion on the filtering output and the original spectrum to form an enhanced spectrum first draft; inputting the enhanced spectrum first draft into a recurrent neural network in a framing manner, predicting the gain of each frequency band, calculating an inhibition factor, and generating a multi-frame continuous enhanced spectrum sequence; multiple frames of continuous enhanced spectrum sequences are synthesized into voice signals through inverse short-time Fourier transform, an end-to-end enhanced recognition process is formed, and the method has the advantage of improving accuracy.

The present application relates to the technical field of image processing, in particular to an insulator image enhancement method based on infrared image denoising of a UAV. The original infrared image collected by the UAV is preprocessed, and the preprocessed image is decomposed in multiple scales by wavelet transform to obtain high-frequency detail coefficients and low-frequency approximation coefficients; the high-frequency detail coefficients are processed by adaptive thresholding, while the low-frequency approximation coefficients are subjected to Wiener filtering, and after wavelet reconstruction and guided filtering, a denoised image is obtained; based on the denoised image, an edge detection operator is used for preliminary edge extraction, edge information is obtained by combining non-maximum suppression and double thresholding, and the edge gap is connected by using morphological edge reconstruction technology; through adaptive contrast enhancement based on local temperature distribution and image fusion processing, the final image with enhanced features is generated. The present application can realize efficient denoising and accurate feature enhancement of the insulator infrared image collected by the UAV.

The application discloses a TDI-CCD image deartifact method and device, equipment, medium and product, and relates to the technical field of image processing. The method comprises the following steps: constructing a deep deblurring network; the deep deblurring network comprises a Wiener filter module and a generative adversarial network, and the Wiener filter module is arranged at the input end of a generator of the generative adversarial network; the Wiener filter module is used for filtering a frequency domain image; the deep deblurring network is trained, and the trained deep deblurring network is used as a deartifact model; during the training of the deep deblurring network, a point spread function and a regularization parameter of the Wiener filter module are used as learnable network parameters; and an image with row smear obtained from a TDI-CCD camera is processed by using the deartifact model to obtain a deartifact image. The application can effectively remove image artifacts and improve the definition of a TDI-CCD image.

The invention relates to a power transmission network security management system, and the system comprises a final-stage conversion mechanism which is used for successively executing Wiener filtering processing and median filtering processing on a secondary conversion picture; and the model application device is used for intelligently identifying whether the field spacing distance between the grounding piece and the operation electrician is smaller than or equal to the safe spacing distance or not by adopting an AI identification model. The power transmission network security management system is intelligent in design and simple to operate and control. The image content capturing action can be executed on the replacement scene for replacing the strain insulator string so as to obtain and output the corresponding replacement scene picture, and the AI identification model is adopted to intelligently identify whether the field spacing distance between the grounding piece and the operation electrician is smaller than or equal to the safe spacing distance based on various visual information in the replacement scene picture; therefore, when a strain insulator string is replaced in a power transmission network, a reliable and safe distance between an operation electrician and a grounding body is maintained.

The invention provides an image processing method based on the combination of Wiener filtering and a Richardson dewson algorithm. The image processing method comprises the following steps: acquiring a to-be-processed blurred image through an image acquisition module; fourier transform is carried out on the to-be-processed blurred images through the blurring kernel estimation module, the spectral characteristics of the to-be-processed blurred images are analyzed, and the to-be-processed blurred images are classified into first-class images or second-class images according to the spectral characteristics; pre-processing the first type of images or the second type of images through a Wiener filtering algorithm and a Richardson-Mexi algorithm to obtain pre-processed images; an edge gradient threshold of a preprocessed image is calculated through a Sobel operator, a fusion weight is given to an intermediate image generated in the process of preprocessing the blurred image to be processed according to the gradient threshold, a final fusion image is output through fusion, and the blurred image to be processed is obtained through blurring type adaptive judgment, staged algorithm adaptation and regional weight fusion. The method achieves the efficient processing of mixed blurring and multi-type noise, and gives consideration to the deblurring effect, noise suppression and edge detail reservation.

The present application discloses a method and an electronic device for reducing echo residue. The method for reducing echo residue can be applied to the electronic device, and comprises: performing echo cancellation on a voice input signal according to an echo reference signal to obtain an echo cancellation signal; converting the echo reference signal into a reference spectrum signal of each frame by fast Fourier transform; converting the echo cancellation signal into a voice spectrum signal of each frame by fast Fourier transform; obtaining an a priori signal-to-noise ratio of a current frame by using the reference spectrum signal of the current frame and the voice spectrum signal of the current frame according to an additive noise principle; filtering the voice spectrum signal of the current frame by a Wiener filter coefficient of the current frame determined by the a priori signal-to-noise ratio of the current frame to obtain a target spectrum signal of each frame; and converting the target spectrum signal of each frame by inverse fast Fourier transform to obtain a target voice signal. Therefore, the method for reducing echo residue can accurately filter out residual echo.

The application relates to a cloud-edge collaborative computing framework and processing method for multi-modal data stream fusion processing, which comprises the following steps: S1: based on the original data stream collected by the edge computing node, an improved Wiener filtering algorithm is used for noise suppression, signal denoising is realized through adaptive threshold wavelet transform, and a time stamp alignment technology is used to solve the time delay difference problem of multi-modal data, and a space-time aligned and purified data stream is generated. The application has the effects that through the combination of the improved Wiener filtering algorithm and the adaptive threshold wavelet transform, the noise suppression efficiency of the original data stream is significantly improved, the time stamp alignment technology effectively solves the time delay difference of multi-modal data, the generation of the space-time aligned and purified data stream ensures that the subsequent processing has a unified time sequence reference, and the space-time attention fusion network adopts the collaborative architecture of a bidirectional gate recurrent unit and a lightweight 3D convolution network.

The invention provides a forest fire early recognition method, medium and system based on multi-modal monitoring data, and belongs to the technical field of forest fire early recognition. Establishing a macroscopic abnormal feature recognition system, calculating a reference threshold value, generating an abnormal feature distribution matrix through spatial clustering analysis, and performing microscopic feature deep mining on an abnormal region boundary and an intensity gradient change region to establish a candidate feature set; a multi-scale feature fusion model is adopted to realize deep fusion of different modal and scale features through a hierarchical convolution structure and a multi-scale pyramid attention mechanism, and the fused features are input into a fire behavior identification and discrimination model to carry out risk grade evaluation and establish a response priority order. The technical problem that the identification precision of multi-mode forest fire monitoring data is insufficient under the condition of weak signals is solved.

The application discloses a kind of deep learning end-to-end lens design methods based on fuzzy kernel correction wiener filtering, in training stage, the nominal fuzzy kernel of each patch area of clear image can be generated by differentiable ray tracing, add tolerance to generate tolerance fuzzy kernel and blur clear image;The simulation fuzzy image and nominal fuzzy kernel are input into correction network to obtain correction fuzzy kernel, wiener filtering is carried out based on the kernel to simulation fuzzy image, and the restoration image is obtained by filtering result input image restoration network, parameter updating is carried out by loss function, gradient back propagation, and iteration is carried out until it meets the exit requirement;In experimental stage, according to the training parameter, manufacture lens and shoot real fuzzy image, use the nominal fuzzy kernel and network parameter after training to complete similar restoration steps with training stage, and generate final restoration image.The application does not need additional calibration and fine adjustment, can effectively resist the influence of various tolerances, along with lens design completion, can significantly reduce lens production cycle and cost.

The application discloses a low-complexity cascaded generalized sidelobe canceling beam forming method and device, and through constructing a beam former of a cascaded generalized sidelobe canceling structure, a high-dimension generalized sidelobe canceler is decomposed into two low-dimension generalized sidelobe canceling units in front and back cascades, so that the operation complexity can be remarkably reduced and the robustness is improved; through using a front and back level feedback iteration processing mode based on frames, the real-time performance of the algorithm is improved, and the application value is higher. The application further utilizes the idea of multi-channel Wiener filtering to construct an interference noise canceling module, directly updates the filtering coefficient between frames, does not need to additionally perform noise estimation, and has simple realization and high robustness, and can balance the interference noise suppression and target speech distortion. Through a multi-branch fusion and interleaving strategy, the application can improve the degree of freedom of beam weight coefficient searching, is more favorable to the algorithm to obtain an optimal solution, and improves the performance of target enhancement and interference noise suppression.

The invention relates to the technical field of intelligent household appliances, in particular to a dishwasher cleanliness detection method adopting T-Faster R-CNN, comprising the following steps: constructing a tableware picture library; de-noising the images of the tableware picture library by using a nonlinear bilateral Wiener filtering method; dividing the denoised tableware picture library into a tableware training set and a tableware testing set; marking the images in the tableware training set; constructing a T-Faster R-CNN (Convolutional Neural Network) model, and performing feature extraction by adopting a T-SR network; the tableware training set is sent to a T-Faster R-CNN model for training, and a trained T-Faster R-CNN model is obtained; a G-Focal cleaning loss function is calculated, and a final T-Faster R-CNN (Convolutional Neural Network) model is obtained; and testing the final T-Faster R-CNN by using the tableware test set, and outputting a detection result to obtain the cleanliness of tableware cleaned by the dish washing machine. By fully utilizing the feature representation capability of the neural network, the dirt in the dish-washing machine can be accurately detected, the dependence on human intervention is reduced, and the robustness and adaptability of the algorithm are improved.

The invention provides a night lighting scheduling method for a power plant. According to the method, an existing security camera is combined with an improved YOLOv4-tiny algorithm to realize rapid and accurate detection of personnel, and lighting scheduling of a specific area is triggered. An infrared induction device array with a Fresnel lens is deployed in key areas such as a power distribution cabinet, signals are processed through wavelet transformation, and after cross validation is conducted on the signals and camera detection results, a full-power brightening mode is started through ZigBee transmission signals. A fuzzy Petri network control model is introduced, and an optimal illumination strategy is generated according to the position, the track and the illumination intensity of the personnel. An improved MFCC algorithm is combined with an HMM to recognize a voice instruction, when a person enters a forbidden zone, warning voice is played, the definition is guaranteed through multi-channel Wiener filtering, meanwhile, a differential privacy protection LBP algorithm is used for analyzing facial expressions, and a second-level alarm is triggered when the facial expressions are abnormal. And a self-adaptive model based on deep reinforcement learning is constructed, scheduling is optimized through a PPO algorithm and an epsilon-greedy exploration mechanism, and power consumption minimization is realized.

The application discloses a method for improving voice conference call quality, relates to the technical field of computer voice processing, and comprises the following steps: using an Escape-TDNN model, adopting diversity samples and double noise reduction technology; using a multi-index fusion algorithm, providing personalized audio services, using adaptive beam forming technology and Wiener filtering algorithm, adopting a human voice separation model, combining with a voiceprint recognition result for post-processing optimization, using natural language processing technology, combining with migration learning technology, using voice synthesis and post-processing technology, fusing microphone array space information and video visual features, combining with environmental prior information, introducing an attention mechanism, and supporting online update of the human voice separation model. The application separates background sound by using an algorithm, isolates noise, makes voice clear, introduces text-to-speech, copes with situations where voice cannot be emitted or a microphone fails, accurately identifies and separates human voice by means of voiceprint technology, improves recognition and purity, and enhances audio effect.

This invention discloses a method and system for restoring defocused images based on estimating the point spread function from a spot image. The method includes: 1) reading the original spot image and the defocused blurred image; 2) cropping the area where the spot is located; 3) projecting the cropped spot image into grayscale along the vertical axis to obtain the original line spread function curve; 4) performing Gaussian fitting on the original line spread function curve to obtain a smooth line spread function curve; and then calculating the standard deviation of the Gaussian function; 5) calculating the blur coefficient of the Gaussian defocus model using the standard deviation; 6) substituting the blur coefficient into the Gaussian defocus model to obtain the point spread function; 7) performing Wiener filtering on the defocused blurred image in the frequency domain to obtain the initial restored image in the frequency domain; 8) performing a second Wiener filtering on the initial restored image to obtain the final restored image in the frequency domain; and 9) performing an inverse Fourier transform to obtain the restored image. This invention effectively reduces the blurriness of defocused images and solves the problem of computational complexity.

This invention discloses a modulation domain speech enhancement method based on group sparse representation, comprising the following stages: a training phase: calculating sub-band spectra of a mixed training signal, speech signal, and noise signal at different modulation frequencies; obtaining signals of different clustered frames of the sub-band spectra through frame clustering analysis; training a joint sub-dictionary using signals from different clustered frames of the sub-band spectra, and then concatenating the sub-dictionaries into a mixed, speech, and noise structured dictionary. A testing phase: calculating the sub-band spectrum of the mixed test signal, preserving its acoustic and modulation phase; calculating the projection coefficients of the mixed test signal's sub-band spectrum onto the mixed structured dictionary through group sparse coding; multiplying the structured dictionary and projection coefficients to recover the speech and noise modulation amplitude spectra, and combining this with a noisy modulation phase inverse transform to obtain the acoustic amplitude spectrum; calculating the weights of the two estimated sets based on the Gini coefficient, and optimizing the estimation using a Wiener filter. This invention utilizes the structured characteristics of modulation domain features as prior information, improving the performance of single-channel speech enhancement.

This invention discloses a method for detecting and suppressing azimuth blur in spaceborne SAR scene matching curve imaging, comprising: 1. Modeling the azimuth blur, providing analytical expressions for the azimuth blur echo, range migration correction, and azimuth phase compensation terms; 2. Refocusing the nth-order azimuth blur based on the range migration correction and azimuth phase compensation terms; 3. Calculating the entropy difference between the SAR image and the nth-order blurred refocused image in blocks, and using the CFAR algorithm to detect blurred regions; 4. Filtering using a Wiener filter to generate a low-blur, low-resolution image; and combining the azimuth blur region detection results, replacing the detected sub-blocks to obtain a low-blur, high-resolution image. This invention can solve the azimuth blur problem in spaceborne SAR scene matching curve imaging mode, effectively improving image quality while preserving the resolution of the original image.

The invention discloses a voiceprint recognition-based hydropower station leakage and leakage recognition method and system. The method comprises the following steps: acquiring a voiceprint signal on a to-be-monitored pipeline; segmenting the voiceprint signal, generating a plurality of audio clips, and forming a data set; wiener filtering processing is carried out on the voiceprint signal; the filtered voiceprint signals are preprocessed; fbank features are extracted from the pre-processed voiceprint signals; performing sequence modeling on the Fbank features by using an ECAPA-TDNN model, and enhancing the feature expression ability through an SE-Res2Block module; replacing an Euclidean distance in the comparison loss with a cosine distance, introducing A-Softmax loss to optimize an intra-class distance, and constructing a voiceprint recognition model; and training the constructed voiceprint recognition model by using the divided data set to obtain a trained voiceprint recognition model for voiceprint recognition. The method has the advantages of accurate prediction and the like.

The invention discloses a children voiceprint self-adaptive recognition method and system in a complex multi-source voice environment. The method comprises the following steps: acquiring an original voice signal, and identifying a voiced segment and a multi-peak energy structure to judge sound source overlapping. And when overlapping exists, analyzing the frame frequency spectrum, and using the child voice feature mask mark to suppress the non-target frequency component to identify the child sound source. And carrying out weighted suppression on the frequency spectrum, and eliminating noise by adopting self-adaptive Wiener filtering to reconstruct the child voice. Multi-dimensional voiceprint features are extracted for normalization processing, and a high-dimensional voiceprint feature vector is constructed; matching judgment is carried out by calculating the Euclidean distance between the vector and the reference vector in combination with a judgment threshold value, the voiceprint of the child is determined, and the judgment threshold value is dynamically adjusted through historical matching data to adapt to sound changes. The reference high-dimensional voiceprint feature vector is a voiceprint template which is obtained by gradually updating a screened sample with confidence meeting the requirement in combination with a moving average method. By implementing the method provided by the invention, the recognition precision and robustness are improved.

According to the PDC signal denoising method based on iteration Wiener filtering of the maximum correlation entropy, on the basis of a traditional Wiener filtering structure, the maximum correlation entropy criterion is introduced to serve as an optimization target, the statistical correlation between filter output and expected signals is enhanced, and the method has the good inhibition capacity for non-Gaussian and non-linear impact noise. According to the method, adaptive convergence can be realized under the condition that an accurate noise priori model is not needed, matrix inversion is avoided, the calculation amount is low, and the method has good engineering realizability and expansibility. The PDC signal preprocessing method is suitable for a PDC signal preprocessing task in the insulation state detection process of the hydro-generator, can serve as a preposed step for expanding Debye equivalent circuit model construction and polarization / depolarization current dynamic decoupling, provides a high-quality data basis for achieving accurate evaluation of major insulation degradation of the large hydro-generator, and has remarkable practical application value.

The invention relates to noise reduction using machine learning. A method of noise reduction includes controlling a Wiener filter using a neural network. The gain estimated by the neural network is combined with the gain generated by the Wiener filter. In this manner, the noise reduction system provides an improved result compared to the use of only neural networks.

The application discloses a photoacoustic signal enhancement method and device based on adaptive multi-band spectral subtraction and Wiener filtering, and belongs to the technical field of speech signal enhancement. The application divides a signal into sub-bands according to Mel scale, determines a subtraction factor and a lower limit factor by a monotone decreasing function linkage according to real-time signal-to-noise ratio of each sub-band, and makes the two factors negatively correlated with the signal-to-noise ratio, so that the denoising strength and the spectral bottom filling depth are automatically adapted; the weighted moving average of the power spectrum of adjacent sub-bands is carried out to smooth isolated spectral peaks and spectral valleys left by spectral subtraction; a residual amplitude limiting is introduced before Wiener filtering, the maximum noise residual threshold is determined based on adjacent frame statistics and limiting, and Wiener filtering is carried out by taking the data after limiting as clean signal estimation; the noise spectrum is recursively updated by a forgetting factor which is dynamically adjusted according to the signal-to-noise ratio, and is only executed in the speech inactive section. The application has an output signal-to-noise ratio improvement of more than 50% when the input is 0dB, can stably enhance without damage under high signal-to-noise ratio, and is suitable for photoacoustic speech acquisition and enhancement scenes.

The invention belongs to the technical field of image processing, and particularly relates to an unmanned aerial vehicle monitoring data processing method and system based on an intelligent construction site, and the method comprises the steps: calling a YOLO model to obtain a target bounding box set of workers in an original image; the instantaneous motion blur degree is obtained according to the pixel gradient sudden change condition in the single-target bounding box; a candidate bounding box is obtained through the intersection-to-union ratio of the target bounding box and the same-worker prediction bounding box, and a candidate matching bounding box sequence is obtained by combining the comparison result of the Euclidean distance of the center point of the target bounding box and the Euclidean distance of the center point of the candidate bounding box and a threshold value; then, according to the average motion speed and the instantaneous fuzzy degree of the worker in the candidate sequence in the time window, obtaining the continuous motion fuzzy degree; weighting the target gradient map by using a normalization result to obtain a blurring kernel direction, obtaining a blurring kernel length through positive correlation mapping, and forming a motion blurring kernel; and finally, restoring the image through Wiener filtering to obtain a clear video stream.

The present application relates to the technical field of geophysical exploration of oil and gas resources, and more particularly to a gather amplitude-preserved frequency-raised detuning correction method based on complex domain Wiener filtering. The gather amplitude-preserved frequency-raised detuning correction method based on complex domain Wiener filtering comprises the following steps: constructing an analytical signal of a pre-stack gather complex domain and obtaining a maximum instantaneous amplitude of the pre-stack gather; selecting a target trace from the pre-stack gather; performing Wiener filtering on the remaining traces in the pre-stack gather except the target trace according to the target trace and by obtaining a Wiener filter operator of the analytical signal; obtaining a maximum instantaneous amplitude of the pre-stack gather after Wiener filtering, and determining a proportional coefficient of the maximum instantaneous amplitude before Wiener filtering and the maximum instantaneous amplitude after Wiener filtering; and obtaining a required pre-stack gather by performing AVO amplitude compensation on the pre-stack gather after Wiener filtering. The present application overcomes the problem of missing seismic data when processing the pre-stack gather.

The application relates to the technical field of satellite navigation, and particularly discloses a civil aviation satellite navigation anti-interference and anti-deception method and equipment, which comprises the following steps: constructing a multi-beam pointing and multi-stage Wiener filtering anti-interference method, solving an optimal anti-interference weight vector under the maximum output signal-to-noise ratio, and taking into account the maximum interference suppression degree and the minimum satellite signal receiving signal-to-noise ratio loss; detecting whether there is deception in the captured signal after anti-interference through a correlation peak ternary detection and attitude position auxiliary detection method; if there is deception, the deception is suppressed through a deception regeneration cancellation method, and satellite signal purification is realized. The application constructs a multi-beam pointing and multi-stage Wiener filtering anti-interference method, a correlation peak ternary detection, an attitude position auxiliary detection, a deception regeneration cancellation anti-deception method, and has the advantages of being suitable for multiple types of interference, having strong anti-interference ability, being suitable for retransmission / generation type deception, having strong anti-deception ability, and being strong in engineering practicability.

The invention discloses a signal filtering method based on an array signal processing technology, and the method comprises the steps: carrying out the sampling of a space signal field through constructing a multi-sensor array, and achieving the spatial filtering through a beam forming technology. The method comprises the following steps: firstly, designing an adaptive weighting coefficient according to a signal environment, enabling a main lobe of an array directional diagram to point to a target signal direction by adjusting the phase and amplitude of each array element receiving signal, and meanwhile, forming null in an interference direction by using a zero point forming technology to suppress an unexpected direction signal; aiming at a color noise background, a Wiener filtering criterion is adopted to optimize a weighting coefficient, and the filtering performance is improved; in combination with a matrix filtering technology, through setting passband and stopband response constraints, array element domain data preprocessing is realized, target signals are further enhanced, and interference is suppressed. The method breaks through the limitation of traditional time domain filtering, improves the signal resolution and the anti-interference capability by using the spatial domain sampling characteristic, is suitable for high-precision signal filtering requirements in the fields of radar, communication, sonar and the like, and effectively improves the signal-to-noise ratio of a system and the target detection accuracy.

This application provides a seismic data processing method and apparatus, belonging to the field of seismic exploration technology for oil and gas. The technical solution provided in this application involves obtaining a Wiener filter from the original wavelet data and the desired wavelet data. Based on the Wiener filter, the original wavelet data is band-extended to minimize the error of the band-extended wavelet data. Further processing yields a second frequency component, i.e., a low-frequency component, in the band-extended wavelet data. This second frequency component is then superimposed on the original wavelet data to obtain compensated wavelet data. The low-frequency component in the compensated wavelet data is compensated, avoiding the low-frequency reduction problem of seismic data after Q-migrating, thereby obtaining high-resolution, wideband seismic data.

The invention provides a mobile robot vision SLAM method and system, computer equipment and a storage medium, and belongs to the field of simultaneous localization and map construction.The method comprises the steps that an original environment image sequence of the position where a mobile robot is located is collected; dividing an original environment image sequence into a plurality of non-overlapped pixel blocks, and constructing a three-dimensional block group by searching similar blocks; sequentially performing three-dimensional transformation, hard threshold filtering and inverse transformation on the three-dimensional block group to generate an estimated pixel value of the pixel block after the first-stage denoising; forming a new block group through the estimated pixel value of the pixel block after denoising in the first stage and the original environment image sequence, and performing Wiener filtering and weighted average on the new block group in sequence to generate a denoised image; eliminating dynamic feature points in the de-noised image to obtain a final static feature point set; and optimizing the three-dimensional map points and the camera pose of the robot according to the final static feature point set, and generating a local three-dimensional point cloud map. According to the method, the visual SLAM performance in a complex environment can be comprehensively improved, and the positioning precision and the map stability are remarkably improved.