77 results about "Denoising algorithm" patented technology

The invention provides a comprehensive noise reduction performance evaluation method for a nonlinear ultrasonic detection signal noise reduction algorithm, and the method comprises the steps: firstly constructing a multi-working-condition noise reduction performance pre-screening mechanism based on error band analysis based on early-stage experimental data, and then introducing a radar map as an evaluation tool for the comprehensive noise reduction effect of the signal noise reduction algorithm. The noise reduction effects of a moving average method (MA), a spectral subtraction method (SS), a short-time Fourier transform method (STFT), a wavelet transform method (WT) and an orthogonal matching pursuit algorithm (OMP) are compared, and finally a signal noise reduction algorithm with the optimal comprehensive efficiency is screened out. On the basis of algorithm optimization, quantitative mapping rules between microcrack three-dimensional geometric parameters and relative nonlinear coefficients are analyzed through regression modeling, and the significant level of the correlation degree of the relative nonlinear coefficients and microcrack size parameters is effectively improved; and a high-confidence theoretical support is provided for quantitative nondestructive detection of the microcracks in engineering practice.

The invention provides a Prony algorithm-based cable segment wave velocity acquisition method, device and system, and a medium, and the method comprises the steps: testing a to-be-tested cable, and obtaining a cable signal transfer function; on the basis of the cable signal transfer function, in combination with cable joint distribution, constructing a cable signal approximation function based on a Prony method; constructing a linear prediction model of the cable signal based on the cable signal approximation function; performing denoising processing on the linear prediction model based on a singular value decomposition method to obtain a denoised linear prediction model; solving the denoised linear prediction model to obtain an attenuation coefficient; and calculating the segmented wave velocity of the cable based on the attenuation coefficient. According to the method, the Prony estimation method is combined with the singular value decomposition noise reduction algorithm, high-precision extraction of attenuation constants and segmented wave velocity decoupling are achieved, and then the electrical distance positioning precision is improved.

The invention discloses a method for detecting tea saponin in a tea extract, and relates to the technical field of chemical analysis and computer science, and the method comprises the following steps: eliminating high-frequency noise of tea extract data by using a Gaussian filtering algorithm, eliminating baseline drift by using a baseline correction algorithm, and generating a tea extract sample data stream; the method comprises the following steps: performing dynamic gradient separation through a chromatographic signal simulation algorithm on the basis of a tea extract sample data stream to generate a virtual chromatographic peak sequence, calculating a peak area through a sliding window integration algorithm on the basis of the virtual chromatographic peak sequence, and generating chromatographic signal data by using a wavelet transform threshold denoising algorithm, the chromatographic signal data are input into the deep convolutional neural model, the feature pyramid network is obtained through the multi-scale feature fusion algorithm, the feature information of the chromatographic peaks is extracted from different scales by constructing the deep convolutional neural model and combining the multi-scale feature fusion algorithm, and therefore the recognition precision of the feature peaks is improved.

The invention belongs to the field of image processing and computer vision in computer intelligent information processing, and discloses an industrial product unsupervised anomaly detection method and system based on a conditional control diffusion model, and the method comprises the steps: constructing a diffusion model based on conditional control and a multi-scale double-attention mechanism, reconstructing the to-be-detected sample image by using a diffusion model based on condition control and a multi-scale double-attention mechanism to obtain a reconstructed image with the same size as the to-be-detected sample image; and extracting feature maps of different scales from the to-be-detected sample image and the reconstructed image through the same pre-trained feature extraction model. Performing a two-stage background denoising algorithm on each scale feature map to obtain an initial abnormal feature map; calculating an average value of a plurality of initial abnormal feature maps obtained by using the same method to obtain a final abnormal feature map; and processing the final abnormal feature map by adopting a thermodynamic diagram generation algorithm to generate a final abnormal thermodynamic diagram, thereby realizing the anomaly detection of the to-be-detected sample image.

The invention discloses a method and a system for detecting internal defects of a bathroom glass door based on multispectral imaging. The method comprises the following steps: carrying out multi-view and multiband image acquisition on the bathroom glass door by adopting a combined spectrum light source; performing adaptive reflection correction on the acquired image to generate a multi-band basic image; optimizing the multi-band basic image through a hybrid denoising algorithm to generate a multi-band detection image; performing multi-modal feature extraction and fusion on the multi-band detection image to obtain a multi-modal fusion feature vector, inputting the multi-modal fusion feature vector into a multi-feature fusion model combining a convolutional neural network and a support vector machine, and identifying and grading defect types by adopting a dual-stage feature enhancement-classification architecture; and outputting a defect type and severity grading result to a terminal in real time. The method is used for solving the technical problems of visual angle and spectrum blind areas, insufficient interference suppression capability, wave band crosstalk, feature deficiency and poor small sample generalization in existing bathroom glass door defect detection.

The invention discloses a damage identification method and device for an in-service steel wire rope type horizontal lifeline, and belongs to the technical field of high-altitude operation safety facilities. The method comprises the following steps: synchronously acquiring a magnetic flux leakage signal and a surface image of an in-service steel wire rope through magnetic flux leakage detection equipment and a high-definition camera which are carried on a steel wire rope inspection robot; preprocessing the acquired magnetic flux leakage signal, wherein the preprocessing comprises singular value elimination and trend term removal processing; carrying out de-noising processing on the pre-processed signal by adopting an improved wavelet threshold de-noising algorithm fused with a Sigmoid function; extracting a characteristic value for representing the damage of the steel wire rope, and performing normalization processing to form a characteristic vector; and inputting into a BP neural network identification model optimized by a genetic algorithm for identification, and outputting an assessment result of the damage type and positioning of the steel wire rope. The method can realize automatic and quantitative detection and accurate identification of internal and external damages of the steel wire rope.

The application provides an intelligent image processing system and method for a car based on a perception algorithm model, and the method comprises the following steps: S1. spatio-temporal reference double anchoring and dynamic intrinsic extrinsic parameter calibration of a vehicle-mounted image acquisition node; S2. multi-node image heterogeneous domain normalization and adaptive preprocessing based on a self-adaptive kernel regression non-local mean denoising algorithm; S3. hierarchical image feature extraction and semantic anchoring based on a graph neural network dynamic feature interaction network; S4. cross-node and cross-frame feature mutual checking and pseudo-feature elimination; S5. full-scene semantic completion and dynamic target trajectory prediction based on a variational autoencoder trajectory prediction model; S6. dynamic lightweight adaptation and algorithm power adaptive scheduling of the perception algorithm model; and S7. risk scene grading identification and image targeted enhancement output based on semantics and trajectories. The application provides stable, accurate and efficient vehicle-mounted image perception support for intelligent driving of a car, and improves the safety and adaptability of environmental perception of intelligent driving.

This invention discloses a vibration signal denoising and feature extraction method based on wavelet adaptive thresholding, belonging to the field of vibration sensor signal processing technology. The method includes the following steps: Step 1, performing multi-level decomposition of the signal using a filter bank constructed based on discrete wavelet transform; Step 2, adaptively calculating the noise threshold; Step 3, implementing wavelet coefficient shrinkage denoising based on the SureShrink algorithm; Step 4, reconstructing the vibration signal based on quasi-discrete wavelet transform; Step 5, automatically extracting signal features based on a multi-layer recurrent neural network. This invention, through signal decomposition based on discrete wavelet transform and an adaptive thresholding denoising algorithm, reduces the error impact caused by the vibration sensor's own noise and environmental disturbances, retains most of the features of the original vibration signal, and establishes a multi-layer recurrent neural network for the denoised vibration signal to achieve automatic extraction of time features, providing an important foundation for the subsequent development of fault diagnosis and health monitoring algorithms.

The invention discloses a self-adaptive anti-interference water quality detection method, system, equipment and medium, belongs to the technical field of water quality detection, and aims to solve the technical problem of low accuracy of a water quality detection result in the prior art. The method comprises the steps of task parameter loading, global path cruising, pollution core area identification, pollution core area sampling, digital twin map generation, map transmission and signal denoising. When the pollution core area is identified, suspected pollution points are screened preliminarily, the comprehensive pollution index of the suspected pollution points is calculated, and the pollution core area with the highest pollution degree is locked; and when map transmission and signal denoising are carried out, the signal receiver carries out denoising and purification treatment on the transmitted signal through a Dig Flow denoising algorithm, and uploads the signal to a shore-based platform after the signal is verified to be correct, so that water quality detection is completed. A pollution assessment scheme of multi-parameter algorithm fusion, CPI comprehensive assessment and a DigFlow denoising algorithm is adopted, the pollution identification accuracy is improved, the traceability error is small, the signal error rate is reduced, and the signal-to-noise ratio is improved.

In accordance with a method of detecting a pressure induced sensor artifact (PISA) in an analyte trace, a measured analyte trace having a plurality of data samples obtained over a period of time from an analyte sensor is received. A reconstructed analyte trace and an associated confidence window is generated from the measured analyte trace using a Bayesian denoising algorithm that includes a model that models the measured analyte trace as a sum of an unknown true analyte trace and a measurement error. The measured analyte trace is compared to the reconstructed analyte trace to identify data samples in the measured analyte trace that are located outside of the confidence window as being associated with a PISA.

A method for segmenting and denoising a triangle mesh, the method comprising: reading triangle mesh data containing N triangular patches, determining the noise level of the triangle mesh data, and optimizing data at a noise level higher than a preset value; segmenting the triangle mesh data by using a region growing segmentation algorithm, such that a plurality of sub-regions of the triangle mesh data are formed; optimizing the segmented triangle mesh data by using a hole-filling algorithm; and filtering the segmented triangle mesh data by using a denoising algorithm.

The application provides an audio signal denoising method based on improved variational mode decomposition and a related product. The method comprises the following steps: obtaining a target input audio signal, and using an improved particle swarm optimization algorithm with a local optimal solution jump-out mechanism and a fuzzy entropy search variational mode decomposition to search for a target component number and a target penalty factor; decomposing the target input audio signal according to the target component number and the target penalty factor to obtain a plurality of first target components; calculating the fuzzy entropy of each first target component, and using a wavelet threshold denoising algorithm to denoise the first target components with fuzzy entropy higher than a preset fuzzy entropy threshold to obtain first components; and obtaining a target output audio signal according to the first target components with fuzzy entropy not higher than the preset fuzzy entropy threshold and the first components. The application can solve the problem of poor denoising effect caused by unreasonable parameter setting in the prior art, thereby realizing more efficient and accurate audio signal denoising processing.

The application discloses a point cloud registration method based on a curved surface characteristic region constraint, comprising a point cloud downsampling module, a characteristic point extraction module, a characteristic point description and characteristic matching module, and a registration module based on a curved surface characteristic constraint region, and is used in a registration task with noise and unordered point cloud data. The point cloud downsampling module is subjected to denoising treatment by adopting a denoising algorithm based on KD-tree. The characteristic point extraction module adopts an extraction detection method based on a curved surface change index. The characteristic description and characteristic matching module uses a fast point feature histogram and a random sampling consistency matching algorithm to complete initial pose transformation. The registration module based on the curved surface characteristic constraint region adopts a method of adding a curved surface characteristic region constraint, accelerates the matching rate of icp characteristic point pairs by a KD-tree algorithm, completes point cloud registration, and improves the accuracy of point cloud registration. The application can be applied to model registration tasks in a complex part digitization detection process.

The invention discloses a point cloud denoising optimization method based on no-reference geometric quality evaluation. The method comprises the following steps: constructing a point cloud quality evaluation data set comprising an objective evaluation data set and a subjective evaluation data set; constructing and training a point cloud sorting quality evaluation network, and extracting multi-scale geometric features of the point cloud and predicting a quality score by using a twin network architecture and a sorting learning mechanism; based on the trained evaluation network, constructing a quality-guided denoising optimization framework, and introducing the predicted non-reference quality score as an auxiliary loss function into the training process of the denoising network; and by dynamically adjusting the de-noising intensity, optimizing de-noising network parameters, and outputting a final de-noising point cloud. According to the method, the problem that a traditional evaluation index is inconsistent with human eye visual perception is solved, a non-reference quality evaluation measure is provided, the measure is used for guiding the optimization of a denoising algorithm, and the geometric detail retention capability and the visual perception quality of the denoised point cloud are remarkably improved.

The invention relates to the technical field of agricultural / forestry pest monitoring, in particular to a trunk borer early warning detection method based on audio signal time-frequency feature fusion, which comprises the following steps: acquiring an audio signal generated by boring vibration of trunk borers on the surface of a trunk, sequentially carrying out noise reduction, pre-emphasis, framing and windowing treatment on the audio signal, and sending the audio signal to the trunk borer. Generating a bidirectional logarithmic Mel spectrogram through a bidirectional Mel filter; wherein the noise reduction adopts a wavelet threshold noise reduction algorithm. According to the method, audio frequency domain and time domain features are respectively extracted through the double-branch network, time frequency information complementation is realized after fusion, the complex features of the trunk borer audio can be better captured compared with a single model, the early weak signal recognition rate is improved, the model parameter quantity is reduced to 0.3 M or below through convolutional layer pruning, lightweight convolution (DWConv + GConv) and parameter-free fusion, the calculation amount is greatly reduced, and the method is suitable for large-scale popularization and application. The method can be deployed in an embedded edge device, and the problems of'recalculation power and difficulty in landing 'of a traditional model are solved.

This invention discloses a remote monitoring method and system for flammable and explosive gases based on big data. It acquires raw datasets collected in real time from multiple sensors; preprocesses the raw datasets using a denoising algorithm, and obtains a first multi-parameter dataset through normalization; generates a second fusion feature set based on the first multi-parameter dataset; if the parameter values ​​of the second fusion feature set exceed a preset threshold, a classification model is used to identify gas leak events, and a diffusion model is combined to generate leak probability and range data; the leak probability and range data are compressed and encoded, and the transmission frequency and data packet size are dynamically adjusted according to network status parameters to form a stable transmission stream; the stable transmission stream is decoded at the monitoring center, and a dynamic visualization interface is generated using a visualization engine; the system reliability is judged based on the interface parameters to determine whether it meets preset standards. This invention achieves real-time monitoring, intelligent identification, and early warning of gas leaks.

The present disclosure provides an image denoising method, a device, an electronic equipment, and computer-readable storage medium, wherein the method comprises performing a variance-stabilizing transform on the to-be-denoised image based on preset noise model parameters to obtain a first intermediate image; denoising the first intermediate image based on a preset signal-to-noise fluctuation curve and a preset denoising algorithm to obtain a second intermediate image, wherein the signal-to-noise fluctuation curve is used to characterize differential information in the noise variance corresponding to the to-be-denoised image at different exposure gains; and performing a variance-stabilizing inverse transform on the second intermediate image based on the noise model parameters to obtain a denoised image.

A computer-implemented method for enhancing the image quality of a medical image region comprising medical image data acquired by a medical imaging modality, the method comprising the steps of: (a) receiving image data from within the medical image region; (b) applying a denoising algorithm (4) to the image data of the medical image region to generate denoised image data (5); (c) applying a deblurring algorithm (6) to the denoised image data (5) to generate enhanced image data (7); and (d) providing the enhanced image data (7).

The application discloses a noise suppression method and system for hyperspectral medical image acquisition, and relates to the technical field of hyperspectral imaging signal processing. An initial hyperspectral medical image is obtained by pretreating a sample to be detected; a pre-trained CNN network is used to screen the initial hyperspectral medical image; a denoising algorithm combining median filtering and wavelet transform is used to perform median filtering on the screened initial hyperspectral medical image, then the filtered image is decomposed through wavelet transform to obtain a wavelet coefficient matrix, a new wavelet coefficient matrix is generated according to the principle of median filtering, image reconstruction is performed through the obtained new coefficient matrix, and finally, a final hyperspectral medical image after noise suppression is obtained according to wavelet threshold denoising. The application can enhance the edge and detail features of the hyperspectral medical image and improve the imaging quality.

The invention discloses an oil-immersed transformer hot-spot temperature real-time monitoring method and system, and relates to the technical field of temperature monitoring, and the method comprises the steps of multi-mode sensing fusion and oil flow field reconstruction, adaptive Kalman filtering-wavelet packet combined denoising, and physical information neural network dynamic compensation. By deploying a multi-parameter embedded sensor array, winding temperature field distribution is captured in real time, and the hot spot tracking capability is optimized; temperature signal distortion caused by oil flow disturbance and electromagnetic interference is eliminated in combination with a time-frequency domain combined denoising algorithm; and a time-varying thermal parameter and harmonic loss model is embedded, so that high-precision temperature compensation is realized, and complex working conditions are adapted. According to the invention, the problem of monitoring misalignment of a traditional monitoring method under a dynamic working condition can be solved, and high-precision data support is provided for intelligent operation and maintenance of the transformer.

This application discloses a focusing noise optimization method, apparatus, storage medium, and product, relating to the field of interferometric noise optimization technology. The focusing noise optimization method includes: acquiring an initial interferometric image containing multiple types of focusing noise and inputting it into a preset noise error correlation model to obtain the noise influence coefficients of each type of focusing noise. The noise error correlation model is obtained by training a model to be trained based on interferometric image samples, which include multiple types of focusing noise and actual focusing errors. Based on the noise influence coefficients, a corresponding denoising algorithm is used to remove the focusing noise of the appropriate type. This application enables the model to be trained to learn the relationship between various types of noise and focusing errors, thereby determining the influence coefficients of each type of focusing noise on the focusing errors. Based on the noise influence coefficients, the corresponding types of focusing noise are selectively removed to avoid affecting the wavefront curvature extraction accuracy, thereby improving the focusing accuracy of the wavefront curvature interferometry method.

The invention relates to the field of computer vision, in particular to a point cloud denoising method based on improved radius filtering and local plane fitting, which comprises the following steps of: dividing outliers into far noise points and near noise points according to Euclidean distances from the outliers to a target point cloud; removing far noise points from the classified point clouds by adopting improved radius filtering to generate point clouds after the far noise points are denoised, and then removing near noise points from the point clouds after the far noise points are denoised by utilizing a deviation denoising algorithm based on a plane of the point clouds and local fitting to generate denoised point clouds; and evaluating the denoising performance of the denoised point cloud according to a predetermined evaluation standard. The method improves the noise removal rate, reduces the false deletion of the target point cloud, avoids the problem of volume distortion or feature ambiguity caused by a traditional smoothing method, and can also adapt to different point cloud data characteristics.

The invention discloses a vegetation coverage quantitative analysis method based on GIS and unmanned aerial vehicle surveying and mapping, relates to the technical field of image data processing, and can solve the problems of splicing boundary blurring and detail loss caused by uneven illumination and shadow shielding in batch surveying and mapping of unmanned aerial vehicle hyperspectral images at the present stage. The method comprises the following steps: acquiring a plurality of hyperspectral images and GIS data of a target area; constructing an edge consistency constraint item; according to the plurality of hyperspectral images of the target area, carrying out area division on the target area and distributing an adaptive denoising weight; constructing a low-rank decomposition model according to the edge consistency constraint term and the adaptive denoising weight, and solving according to the low-rank decomposition model to obtain a plurality of denoised hyperspectral images; and carrying out registration and fusion on the plurality of denoised hyperspectral images and GIS data to generate a high-precision orthoimage of the target area, and carrying out inversion calculation on the vegetation coverage based on the orthoimage.

The invention discloses a bearing fault intelligent diagnosis system and method, and relates to the technical field of mechanical fault diagnosis, and the system comprises a multi-source data collection module, a signal preprocessing module, a feature extraction module, a deep learning diagnosis module, and a self-adaptive decision module. The multi-source data acquisition module is used for synchronously acquiring a vibration signal, an acoustic signal, thermal imaging data and multi-spectral image data of a bearing, the signal preprocessing module is used for denoising and enhancing the multi-source data, and the vibration signal adopts a wavelet threshold denoising algorithm. According to the bearing fault intelligent diagnosis system and method, the accuracy and the early warning capability of bearing fault diagnosis are remarkably improved through the synergistic effect of multi-modal data fusion and the adaptive deep learning algorithm. According to the system, dynamic feature weight distribution and real-time intelligent decision making are innovatively realized, and the problem of insufficient adaptability of a traditional method under complex working conditions is effectively overcome.

The application discloses a no-reference evaluation method for pulse wave signal quality based on heart rate continuity, and steps of the method comprise the following steps: firstly, acquiring video images and defining a plurality of face regions of interest, then acquiring corresponding pulse wave signals by using a denoising algorithm; then, converting the pulse wave signals into a time-frequency domain by using synchronous compression wavelet transform, extracting time-frequency domain instantaneous heart rate variance features calculated based on the synchronous compression wavelet transform, sorting and screening the quality of the pulse wave signals, fusing time-frequency spectrums of selected high-quality pulse wave signals, extracting a time-frequency ridge on the fused time-frequency spectrum, estimating heart rate again by using a heart rate continuity assumption, and finally calculating uncertainty of the time-frequency spectrum of the high-quality pulse wave signals to evaluate the reliability of predicted heart rate. The application can effectively evaluate, screen and fuse the quality of pulse wave signals obtained by each sub-region, thereby realizing robust extraction of video heart rate.

The invention relates to the technical field of medical image processing, in particular to a CT image automatic noise reduction processing method based on multi-scale feature fusion. The method comprises the following steps: performing frequency domain analysis on a CT image, dividing the CT image into a plurality of scale spaces, performing edge detection on each scale space, determining a coincident edge of adjacent scale spaces, determining a real tissue edge by calculating gradient direction consistency and cross-scale gradient stability of pixel points on two sides of the coincident edge, and marking other edges as noise; and determining an optimal scale combination according to the noise coverage rate and the noise priority, carrying out noise reduction processing by adopting different denoising algorithms, and finally fusing the denoised scale space and the non-denoised scale space to obtain a denoised CT image. The real tissue structure and the noise can be effectively distinguished, the details of the tissue structure are kept to the maximum extent while the noise is restrained, and the reliability of CT image diagnosis is improved.

The application discloses a double-parameter HP filtering denoising algorithm and process for infrared spectrum signal processing, wherein upper and lower limits Lamda_1 and Lamda_2 of parameter scale estimation are given in advance, two filtered signals are obtained by performing traditional HP filtering respectively, the two filtered signals are introduced to comprehensively construct a new HP filtering expression, HP filtering is performed under the new comprehensive reconstructed HP filtering expression, and the parameter value when the expression takes a minimum value is taken as an output result in the range of [Lamda_1, Lamda_2] for optimization searching. The local extreme value is searched in the two parameter estimation ranges, and HP filtering is performed under the new expression. Since the application constructs the HP filtering expression of the comprehensive parameter upper and lower limits and performs the optimization processing in the parameter upper and lower limits, the scale estimation of the signal noise can be better, the problem that the uncertain selection of the parameter in the HP filtering denoising process leads to the difficulty in grasping the scale of the high-frequency noise denoising can be solved, and better denoising effect can be obtained.

An improved systems and methods for generating a denoised inspection image are disclosed. An improved method for generating a denoised inspection image comprises acquiring an inspection image; generating a first denoised image by executing a first type denoising algorithm on the inspection image; and generating a second denoised image by executing a second type denoising algorithm on the first denoised image.

This invention relates to the field of aircraft assembly technology, and more particularly to an aircraft door repair method based on measured data. It includes: processing the original point cloud data using filtering and denoising algorithms, and filling in sharp boundary missing areas using point cloud interpolation algorithms to enhance point cloud quality; identifying the geometric boundary features of the door and fuselage frame using a feature-aware boundary extraction algorithm; calculating the door repair amount based on the minimum clearance principle, and outputting a precise door repair line. This invention achieves accurate geometric identification and repair trajectory generation for thin-walled, complex curved surface door structures. This method effectively solves the geometric deviation problem caused by manufacturing errors and structural deformation in door repair, significantly improving the automation level, assembly accuracy, and processing efficiency of aircraft door repair, and providing highly reliable data support and technical path for digital manufacturing in aviation assembly.

The application discloses a method for measuring super-large curvature radius, and relates to the field of optical measurement, which comprises the following steps: placing two autocollimators in parallel on one side of an optical element to be measured, calibrating the parallel state by using optical flat crystals; then placing the autocollimators near the curved surface to be measured, synchronously adjusting the pitch angle and position to make the light spots on the same CCD axis; calculating the centroid offset of the reflected light spots, and reducing the noise influence by combining with a denoising algorithm; finally, calculating the curvature radius according to the distance between the two autocollimators and the centroid offset distance of the light spots; the method is compatible with concave-convex mirror measurement, does not need to replace equipment or adjust the optical path, has a simple optical path structure, and the algorithm is easy to realize; the method does not depend on a high-precision optical platform, can be applied in an optical workshop, compared with the traditional autocollimation method, does not need a large-stroke displacement table, and can realize the measurement of the super-large curvature radius only by fine adjustment, so that the operation is simplified, the repeatability is good, and the precision is high.