24 results about "Mixed noise" patented technology

This application belongs to the field of coherent diffraction imaging technology, specifically disclosing an ultra-low dose coherent diffraction imaging method and apparatus. Based on a blind source separation strategy, this application uses principal component analysis to process the acquired diffraction signals, performing noise separation and updating in reciprocal space. This effectively separates mixed noise energy into noise components, thus avoiding crosstalk to the reconstruction process and significantly improving the convergence stability and robustness of coherent diffraction imaging when reconstructing diffraction signals with extremely low signal-to-noise ratios under ultra-low exposure doses. Simultaneously, this application constructs noise components separately for each scanning position and correlates noise components at different positions through low-dimensional spatial projection, achieving non-stationary noise separation. This enables more effective handling of random noise caused by the low quantum efficiency of ultra-short band detectors, thus maintaining extremely high noise robustness and reconstruction accuracy even under ultra-low exposure doses, achieving an effective improvement in resolution.

This invention discloses a real-time rendering method and system for physically simulated volumetric clouds. The method includes the following steps: constructing a dynamic noise mixing model, and constructing complex cloud layers by mixing multiple noises based on the dynamic noise mixing model; constructing a multi-scattering illumination model, and using the multi-scattering illumination model to perform single-scattering calculations and multi-scattering approximations on the complex cloud layers to simulate illumination; based on the completed illumination simulation, dividing the cloud density field of the complex cloud layers into several voxel blocks, and performing adaptive light travel processing to complete the real-time rendering of volumetric clouds. This invention, without sacrificing image quality and rendering efficiency, integrates advanced cloud modeling theory with efficient GPU acceleration technology, and uses a mixed noise model to generate diverse cloud structures, accurately reproducing various typical cloud types such as high-altitude cirrus clouds, cumulus clouds, and stratus clouds.

The application provides an active noise reduction method and system for road noise and wind noise of an automobile, and the active noise reduction method comprises the following steps: S1, collecting a dynamic parameter signal and a total mixed noise signal during automobile driving; S2, constructing a dynamic calculation model of a Strouhal number according to a Reynolds number, deducing a real-time calculation model of a characteristic frequency of each noise source i according to the dynamic calculation model of the Strouhal number, and outputting a real-time characteristic frequency sequence corresponding to each noise source i; S3, constructing a global signal dictionary D, using an orthogonal matching pursuit sparse representation algorithm, and separating a single noise source signal in the total mixed noise signal by iteratively screening a dictionary atom with the highest matching degree with the characteristic frequency; S4, using a frequency tracking type fast adaptive filtering algorithm to generate a corresponding anti-phase cancellation signal; and S5, outputting the anti-phase cancellation signal in the automobile cabin, so that noise reduction can be performed on each noise source i.

This invention discloses an accurate detection system for noise and synchronization signals in A / D converters, specifically relating to the field of signal processing technology. It comprises four main modules: co-source timing allocation, multi-dimensional signal injection, dual-channel synchronous acquisition, and dynamic decoupling analysis. The co-source timing allocation module fans out a high-precision clock into multiple phase-correlated signals. The multi-dimensional signal injection module injects a composite excitation vector into the A / D converter under test. The dual-channel synchronous acquisition module, under clock constraints, synchronously captures the mixed noise data of the main channel and the jittered physical waveform of the reference channel clock. The dynamic decoupling analysis module uses a feedback calibration signal for time-domain registration, extracts intrinsic noise parameters through noise source stripping operations, and outputs timing compensation. This invention solves the problem of dynamic coupling and difficulty in separating synchronization jitter and intrinsic noise under high sampling rates, significantly improving the accuracy and robustness of A / D converter detection.

The application belongs to the field of laminated diffraction imaging, and specifically discloses a high dynamic range image fusion laminated diffraction imaging method and system, the imaging method being: initializing an illumination probe and a sample to be measured, constructing a joint distribution containing a diffraction light field, a dark field and an exposure time sequence, solving a maximum likelihood estimation of absolute irradiance based on a joint probability density function, fusing a diffraction field high dynamic range absolute irradiance from directly measured diffraction light field, dark field and exposure time parameters, replacing an analog diffraction light field amplitude and keeping its phase unchanged, and simultaneously reconstructing the illumination probe and the sample to be measured according to the analog diffraction light field before and after updating. The application considers the influence of mixed noise, effectively extracts high-frequency overlapping correlation signals without losing or reducing the signal-to-noise ratio of low-frequency region diffraction signals, significantly reduces the requirement for the dynamic range of a detector, and avoids the increase of imaging system uncertainty without additional complex parameter calibration and modulation equipment in the imaging process.

The application provides a measurement preprocessing method based on correlation entropy and attention mechanism. The dynamic weight is calculated through the Gaussian kernel correlation entropy of the historical innovation and the current innovation in the sliding window, and the abnormal measurement is adaptively identified and suppressed. The method adopts a feedforward inhibition mechanism to block noise propagation, while retaining the optimality of the KF framework, and realizes efficient anomaly detection with lower complexity. Simulation experiments show that in the Gaussian mixed noise and impulse noise scene, the application significantly reduces the mean square error of KF, the dynamic window mechanism is faster than the noise statistical modeling algorithm in response speed, the tracking accuracy is equivalent to Huber-KF, and the parameter adaptability is stronger, which verifies the robustness and tracking performance of the method in non-Gaussian noise, especially in the impulse noise environment, and provides theoretical support for subsequent expansion to the maneuvering target scene and deep learning fusion.

The core challenge of traditional scanning electron microscope (SEM) images is their susceptibility to Gaussian and Poisson noise and blurring, resulting in insufficient image resolution. To address this, this invention proposes a multi-model collaborative deep learning image processing method suitable for both high-pressure and low-pressure SEMs. The method proceeds in a progressive sequence of "denoising – deblurring – super-resolution." First, an improved DnCNN model removes Poisson-Gaussian mixed noise from the noisy image. Then, a generator model using a generative adversarial network (GAN) architecture deblurs the denoised image, restoring image detail clarity. Finally, an ESRGAN super-resolution model magnifies the deblurred image by 4 times, improving its spatial resolution. This invention allows for flexible selection of a fixed-round training mode or an early-stop training mode, and enables quantitative evaluation of processing results. It compares different processing sequences to identify the optimal process, providing technical support for the accurate analysis of SEM images.

This invention discloses an improved GAN-based EUR prediction method for volumetric fractured wells, belonging to the interdisciplinary field of oil and gas field development and artificial intelligence technology. The method includes: S1, data acquisition and preprocessing; S2, constructing a generative adversarial network model; S3, introducing Dirichlet mixed noise and back-comparison loss; S4, model adversarial training with multi-loss fusion; and S5, data generation and model evaluation. This invention constructs a single-negative-multiple-positive-sample back-comparison learning mechanism for Dirichlet mixed latent variables, strengthening the consistency of feature structure and parameter correlation between generated and real data, significantly improving EUR prediction accuracy under small sample conditions, and achieving higher SDMetrics and R... 2 The indicators are all superior to traditional methods, and can accurately capture the impact of key fracturing parameters on EUR, providing a reliable basis for the capacity assessment of volumetric fracturing wells and the optimization of development plans, thus helping to improve the efficiency and economic benefits of oil and gas resource extraction.

A multi-channel signal generator (200) is provided for generating a multi-channel signal (204) having a first channel (201) and a second channel (203). The multi-channel signal generator (200) includes: a first audio source (211) for generating a first audio signal (221); a second audio source (213) for generating a second audio signal (223); a mixed noise source (212) for generating a mixed noise signal (222); and a mixer (206) for mixing the mixed noise signal (222) with the first audio signal (221) to obtain the first channel (201), and mixing the mixed noise signal (222) with the second audio signal (222) to obtain the second channel (203). The present invention also provides an audio encoder, comprising: an activity detector (380) for analyzing a multichannel signal (304) to determine whether a frame in a frame sequence (381) is an inactive frame (308); and a noise parameter calculator (3040) for calculating first parameter noise data (p_noise, v_v) of the first channel (301, 201) of the multichannel signal (304). m，ind ), and calculate the second parameter noise data (p_noise, v) of the second channel (303) of the multi-channel signal (320). s，ind The coherence calculator (320) is used to calculate coherence data (404, c) indicating the coherence between the first channel (301, 201) and the second channel (303, 203) in the inactive frame (308); and the output interface (310) is used to generate an encoded multi-channel audio signal (232) having encoded audio data of the active frame (306) and first parametric noise data (p_noise, v_v) of the inactive frame (308). m，ind ), second parameter noise data (p_noise, v s，ind ), and / or a first linear combination of the first parameter noise data and the second parameter noise data and a second linear combination of the first parameter noise data and the second parameter noise data, and coherence data (c, 404).

This invention discloses a method for identifying the frost state of the target area of ​​a cryoablation needle based on image processing, relating to the field of cryoablation needle technology. The method involves constructing an original image sequence of the target area in a unified spatiotemporal coordinate system; outputting corresponding degradation stage information; obtaining a high-fidelity clear image sequence of the target area; aggregating signal subspace features through a channel gating mechanism to obtain a signal enhancement feature map set; generating a multi-scale semantic feature tensor of the target area; outputting an initial frost probability distribution map of the target area; performing morphological constraint segmentation optimization to obtain a morphologically consistent frost region mask; obtaining a temporally stable frost region mask sequence and constructing a dynamic evolution model of the frost state, displaying warning information in real time. This invention effectively suppresses the feature submersion problem caused by mixed noise and significantly improves the recognizability and stability of the underlying structural information of the target area in a low-temperature imaging environment.

The purpose of this disclosure is to provide a speech training noise enhancement system and method based on a hybrid noise generation model, comprising: an input module, a noise environment enhancement module, a speech noise enhancement module, and an output module; wherein, the input module is used to acquire simplified description information of the noise environment and clean speech data to be enhanced; the noise environment enhancement module converts the simplified description information of the noise environment into a structured noise event sequence with temporal features; the speech noise enhancement module generates multi-source hybrid noise according to the noise event sequence and adds the multi-source hybrid noise to the clean speech data according to preset rules to obtain noisy speech data; the output module is used to output the noisy speech data for use in speech model training. This disclosure fully integrates the interactive features of superposition, cancellation, and interference of different noise sources in the time dimension, solving the technical bottleneck that traditional mixing methods can only achieve linear superposition.

The application relates to the field of mixed noise noise reduction, in particular to a mixed noise scene noise sample segmentation identification noise reduction method and system. The method comprises the following steps: acquiring a mixed noise source data set of an industrial smelting scene; based on the mixed noise source data set, carrying out multi-dimensional feature fusion process stage identification and noise function role determination, and generating a dynamic noise scene stage information set; based on the dynamic noise scene stage information set, carrying out dynamic noise reduction strategy generation and adaptive execution for stage effect heterogeneity determination, and generating a real-time noise processing strategy execution set; based on the real-time noise processing strategy execution set, carrying out time sequence smooth transition of processing effect and closed-loop strategy verification based on audio feedback, and generating an adaptive noise control optimization result set. In the noise sample segmentation identification noise reduction process, the application can effectively distinguish and retain the acoustic signals containing key process information in the noise reduction process.

The utility model relates to a shock absorber noise reduction structure belongs to shock absorber technical field, including shock absorber main part, upper support, lower support, no. The utility model discloses a shock absorber noise reduction structure can carry out effective noise reduction processing to high frequency and low frequency mixed noise to further improve the noise reduction effect, and the overall noise reduction shell is convenient to dismount and handle, thereby being convenient for maintenance, and the telescopic protective shell is provided, can prevent the shock absorber from being exposed in the part of ageing abrasion, thereby can effectively improve the service life and prevent ageing and produce additional noise.

This invention discloses a training and application method for a Cherenkov video denoising model for radiotherapy, relating to the field of video image processing technology. Addressing the technical challenge of extremely low signal-to-noise ratios due to extreme physical limitations in Cherenkov imaging and the difficulty in obtaining noise-free reference ground truth, this invention obtains consecutive noisy video frames and the number of pulse accumulations from the linear accelerator, converting this number into a noise index characterizing the effective noise level. Using this noise index as a conditional variable, adaptive instance normalization is applied to feature modulation of the video denoising model. A pseudo-noisy image is generated by combining a pre-trained noise prediction model, and the network is updated solely based on the cycle consistency loss calculated between the noisy frames and the pseudo-noisy image. In the application phase, the denoised image is time-series accumulated according to control nodes and compared with the reference dose distribution. This invention achieves high-fidelity removal of extremely low photon-physical mixed noise under ground truth conditions, significantly improving the accuracy of in vivo radiotherapy applications.

The application discloses a low-dose medical image noise reduction enhancement system and an optimization method, relates to the technical field of medical image processing, and comprises the following components: an image input preprocessing module, a noise intelligent typing module, a kernel state acquisition module, a parameter dynamic adjustment module and a noise reduction enhancement feedback module. According to the complete closed-loop processing flow, the application generates strong adaptability of the noise reduction parameter instruction through accurate identification of the mixed noise in the low-dose medical image and real-time sensing of the system state, adopts differential processing to reserve the key details of the image and eliminate noise interference, improves the image definition and diagnostic usability, greatly improves the system adaptability and operation stability, solves the technical problems of the prior art, guarantees efficient and stable output of the system, and reduces resource consumption and processing delay.

The application belongs to the technical field of data privacy protection, and relates to a data privacy protection method and device, an electronic device and a computer readable storage medium. The data privacy protection method comprises the following steps: obtaining an adjacency matrix of graph structure data, and obtaining a noise matrix by adding mixed noise to the adjacency matrix; calculating an edge number Q used for screening matrix elements in the noise matrix according to an edge number Q between nodes in the graph structure data; marking the matrix elements in the noise matrix according to element values and the edge number Q; converting the noise matrix into a perturbation matrix used for describing edge information of the graph structure data according to positions of the marked matrix elements; and adjusting the edge information of the graph structure data according to the perturbation matrix to obtain private graph structure data. The application can protect private data with less computing power requirement.

This invention relates to the field of hyperspectral image processing, specifically to a hyperspectral image denoising method based on content-aware sparse cues. The method includes: constructing multiple typical noise degradation patterns and their linguistic descriptions; introducing a pre-trained text encoder to generate cue vectors; and adaptively evaluating the importance of the cue channel in conjunction with image content. Within the encoder-decoder framework of the denoising network, the cue is mapped to a feature map with a resolution consistent with the visual feature space, and fused with features from the corresponding level of the encoder at skip connections, thereby participating in the multi-scale feature modeling and reconstruction process. Through this approach, the denoising network can adaptively adjust the feature reconstruction path according to the image content when facing unknown noise types or mixed noise degradation, thus improving the network's adaptability. By sparsely modeling the high-dimensional cue features, the scale of redundant features involved in computation is reduced, improving computational efficiency while maintaining denoising performance.

The application provides a method for identifying internal leakage of a liquid hydrogen C-type ball valve based on acoustic emission frequency characteristics, and belongs to the technical field of internal leakage detection of liquid hydrogen C-type ball valves. In the application, an acoustic emission sensor array is arranged on the outer surface of the valve seat of the liquid hydrogen C-type ball valve to collect original signals. After adaptive multi-scale wavelet packet decomposition, mixed noise reduction and coherent accumulation enhancement are performed. A material dispersion compensation model is established to extract parameters and input a leakage characteristic identification model to obtain a leakage probability value. When the probability value exceeds a threshold value, fast independent component analysis and sparse reconstruction are used to estimate the number of leakage sources. The spatial angle of the leakage source is located by using a multiple signal classification algorithm. The leakage aperture is calculated by thermodynamic inversion calculation. The turbulent noise characteristics extracted by the fractal theory are fused and judged, thereby solving the technical problems that it is difficult to accurately identify a small leakage source and realize quantitative evaluation of the leakage degree in a strong background noise environment during internal leakage detection of the liquid hydrogen C-type ball valve.

The application obtains a data pair of a low-illumination image and a hyperspectral image without motion blur at the same time and the same angle in a real scene by acquiring an RGB image and a hyperspectral image data pair, combining a Poisson-Gaussian mixed noise model and an exposure decay model, constructing a progressive frequency domain collaborative spectral reconstruction network, decoupling the image into a low-frequency structure stream and a high-frequency texture stream by using the Laplacian pyramid principle, designing a dual-domain feature collaborative evolution unit, using the core component MASD to perform parallel optimization on the spectral consistency of the structure stream and the spatial details of the texture stream, and combining a gating interaction mechanism and residual accumulation to gradually refine the image from blur to clarity, so as to reconstruct an accurate spectrum. A spectral-visual mapping module based on content adaptive spectral weighting and neural color rendering network is further constructed to replace the traditional fixed chroma matching function and map the reconstructed spectral image into an enhanced RGB image conforming to the human eye visual characteristics.

The present application belongs to the technical field of image processing, and particularly relates to a method for generating pictures by intelligent shooting based on computer vision. The steps include: S1, collecting an initial image of a target person, using an improved YOLOv5 algorithm with a Neck layer introducing an attention module to identify key points and center coordinates of the person, calculating an offset and adjusting a shooting angle or position to obtain an initial composition; S2, detecting eye and limb states through key point detection, determining a shooting intention, and then shooting to obtain an original image; S3, calculating noise features by dividing the original image, classifying noise based on MobileNetV2, and denoising accordingly, and correcting mixed noise through a residual network; S4, segmenting image regions by using a full convolution network, optimizing by verifying an intersection-over-union ratio, fusing a mask, and generating a target picture by global color balance. The present application improves the composition accuracy, denoising effect and image quality, and meets the demand for high-quality intelligent shooting.

The application provides a layered robust discriminant learning method for hyperspectral remote sensing image anomaly detection. 1，1 The application effectively depicts the complex mixed noise introduced in the process of acquiring the hyperspectral remote sensing image in a real scene through the l norm and the Frobenius norm, and improves the anti-noise performance of the anomaly target detection model. In order to more accurately separate the deeply mixed background and anomaly target, obtain a robust and more powerful anomaly target detection model, and design a layered detection idea, the background and anomaly target components in the deeply mixed hyperspectral remote sensing image are gradually separated. The application can not only improve the distinguishability between the background and the anomaly target, but also has very strong noise suppression performance and detection robustness.

This invention provides a method, apparatus, and equipment for processing launch vehicle liquid level data, belonging to the field of rocket control technology. It solves the problems of filtering mixed noise and insufficient accuracy and stability in launch vehicle liquid level measurement. The method includes: acquiring raw telemetry data collected by a launch vehicle liquid level sensor; verifying and analyzing the raw telemetry data to obtain first liquid level height data; filtering the first liquid level height data using a series two-stage filter to obtain second liquid level height data; and determining the fuel volume data in the launch vehicle's propellant tank based on the second liquid level height data. This scheme achieves efficient filtering of mixed noise in launch vehicle liquid level measurement, improving the accuracy, stability, and reliability of liquid level analysis.

This invention discloses a method for denoising sensor data in flight control systems under electromagnetic interference environments, belonging to the field of flight control system signal processing technology. The method includes: acquiring a flight control sensor noise dataset and dividing it into a training subset and a real-time processing subset; extracting time-domain and frequency-domain features of each channel and associating them with interference intensity signal features to generate noise feature vectors; constructing a hybrid noise recognition model to identify noise patterns; performing hierarchical misjudgment correction through cosine similarity matching; marking signal segments suspected of residual noise and establishing a noise interference mapping table; performing differentiated denoising processing on marked signal segments and normal signal segments; performing secondary denoising on signal segments suspected of residual noise; collecting denoised sensor data and calculating three-dimensional indicators to evaluate the denoising effect; if the indicator meets the standard, verifying the improvement in flight control accuracy; if the indicator does not meet the standard, adjusting the parameters of the hybrid noise recognition model and iteratively optimizing it until the standard is met, thus achieving high-precision identification and adaptive denoising of flight control system noise under electromagnetic interference environments.