17 results about "Noise enhancement" patented technology

The invention relates to the technical field of image processing, and provides an underwater image enhancement method based on frequency domain enhancement and edge guidance, which comprises the following steps of: firstly, acquiring three images with different scales through image downsampling, and then converting the three images into a frequency domain to respectively extract a low-frequency spectrum and a high-frequency spectrum; constructing a distance mask and a mean value mask based on the distance and the amplitude mean value to enhance a high-frequency part so as to improve image details; meanwhile, the effect is enhanced by suppressing low-frequency part noise. In addition, a multi-stage residual feature aggregation module is provided, the module focuses on detail extraction, and information loss caused by global enhancement is effectively avoided. And finally, further enhancing image edge details in combination with an edge guiding strategy. Experimental results show that the method is superior to the current most advanced underwater image enhancement method in quantitative and qualitative evaluation of a plurality of public data sets.

The invention discloses a noise enhancement nonlinear system joint detection and estimation method under a Bayesian framework, and belongs to the field of signal processing. Firstly, independent additive noise is added to a nonlinear system input signal, and noise-corrected nonlinear system output is obtained after the signal passes through a nonlinear system. And secondly, under the Bayesian criterion, judging which hypothesis in the binary hypotheses is established by utilizing the output of a nonlinear system of noise correction, and estimating unknown parameters in the signal of which the judgment result is H1. On the premise that the detection performance is not reduced, a noise enhancement nonlinear system joint detection and estimation model which minimizes the estimation risk is constructed. The additive noise is the optimal solution of the model and is random distribution formed by not more than two constant vectors. According to the method, noise enhancement and nonlinear system joint detection and estimation under the Bayesian framework are combined, and the Bayesian estimation risk is further reduced under the condition that the Bayesian detection cost is not increased.

This invention belongs to the field of multimedia analysis technology and discloses a multimodal media tampering detection method, system, device, and medium based on multi-view contrastive learning. The method includes: acquiring a training dataset, which includes training image-training text pairs and corresponding tampering category labels; introducing a cross-encoder based on a visual-language model, setting several multilayer perceptron head structures, and designing three contrastive learning methods: noise enhancement, prototype-based, and multi-label tampering classification, to obtain an initial multi-view contrastive learning framework; training the initial multi-view contrastive learning framework based on the training dataset to obtain a trained multi-view contrastive learning framework; and performing a tampering detection task on the image-text pair data to be detected based on the trained multi-view contrastive learning framework. The technical solution of this invention can improve the accuracy and robustness of multi-label classification.

This application belongs to the field of interconnection network reliability and fault diagnosis technology, and discloses a method for intermittent fault diagnosis of low-bandwidth long interconnection networks based on graph attention mechanism. Targeting the hierarchical recursive structure and high connectivity of the network, under the PMC fault diagnosis model, a multi-round testing strategy is used to obtain test symptoms within the node's neighborhood. A feature vector is constructed for each node using local statistical feature extraction methods, and preprocessed using zero-padding and noise enhancement techniques. Finally, a graph attention network model is constructed, and the importance weights of neighboring node test results are dynamically learned using the attention mechanism to achieve accurate diagnosis of node fault states in the network. This application leverages the powerful local information aggregation capability of graph attention networks to overcome the diagnostic limitations of traditional algorithms, maintaining high diagnostic accuracy and robustness even with high fault rates, incomplete test symptoms, and large network sizes.

The invention discloses a remote sensing image unsupervised change detection method and system based on structure perception and noise enhancement. According to the method, shared feature representation is extracted from a dual-temporal remote sensing image, a structure perception contrast learning mechanism is introduced to enhance the perception capability of a model for real geographic structure change, noise disturbance consistency constraint is designed to avoid an optimization shortcut problem, and a frequency attention decoding mechanism is adopted to finely depict a change region boundary. The system comprises a preprocessing module, a feature coding module, a structure sensing module, a noise disturbance module, a frequency attention decoding module and an unsupervised optimization module. According to the method, under the condition that manual labeling is not needed, the problems that an existing unsupervised change detection method is short in optimization, insufficient in semantic representation capacity, not fine in boundary description and the like are effectively solved, the accuracy and robustness of change detection are improved, and the method is suitable for the fields of urban expansion monitoring, disaster assessment, environment change analysis and the like.

The invention aims to provide a voice training noise adding system and method based on a mixed noise generation model. The system comprises an input module, a noise environment enhancement module, a voice noise enhancement module and an output module. Wherein the input module is used for acquiring noise environment simple description information and clean voice data to be enhanced; the noise environment enhancement module converts the noise environment simple description information into a structured noise event sequence with time sequence characteristics; a voice noise enhancement module generates multi-source mixed noise according to the noise event sequence, and adds the multi-source mixed noise to the clean voice data according to a preset rule to obtain noisy voice data; and the output module is used for outputting the noisy voice data for voice model training. According to the invention, interaction characteristics of superposition, offset, interference and the like of different noise sources in the time dimension are fully joined, and the technical bottleneck that only linear superposition can be realized in a traditional mixing mode is solved.

The application provides a kind of electrically conductive sponge electromagnetic leakage dynamic monitoring method and system, by utilizing the leakage feature recognition degree of auxiliary noise enhancement based on spectrum attenuation coefficient, and utilizing the set threshold value, a plurality of IMF components are obtained by ensemble empirical mode decomposition, for each IMF component, determine the dominant frequency point and phase retention band;While the phase is retained in the retention band, the out-of-band phase is randomized, the proxy data set is constructed and the confidence interval is established, so as to screen out the leakage feature component, by extracting the weighted hilbert energy with the energy proportion in the phase retention band as the weight, and the multiscale sample entropy with the total bandwidth as the scale factor, a joint feature vector is constructed, and the leakage is judged based on the mahalanobis distance of the vector and the normal state.

The application provides a spoken English recognition method and system based on contrast learning and mixed attention, which comprises the following steps: acquiring audio of a spoken English test, adding noise of a random category to environmental recording to realize noise enhancement and further construct a positive sample; performing feature extraction on the data added with noise based on multi-scale and mixed attention; inputting the features after embedding and position coding into an encoder to perform context modeling; inputting the output of the encoder and the target features after embedding and position coding into a decoder to complete decoding; in the training process, calculating a contrast loss through input of the positive sample, calculating the loss of each sample at the same time, performing reverse transmission, and obtaining a recognition model; and inputting the audio of a candidate to be transcribed into the recognition model to obtain a recognition result.

The invention provides an SAR ship target detection method in a complex environment. An SAR ship detection model is adopted to identify an SAR ship target. The SAR ship detection model adopts a YOLOv11n backbone and comprises a backbone network, a neck network and a head network; the backbone network comprises five convolution modules, four lightweight feature extraction modules, an SPPF module and an anti-noise enhancement module; the neck network comprises five lightweight feature extraction modules, five connection modules, three dynamic sampling modules and two convolution modules; the head network comprises three detection modules; according to the invention, a plurality of innovative modules, including a lightweight feature extraction module (PCA), an anti-noise enhancement module (PSA-G), a dynamic sampling module (DySample) and a multi-scale small target fusion network (MSTFNet), are introduced into a backbone and neck network, so that noise resistance, light weight and high precision are taken into account.

The application belongs to the technical field of interconnection network reliability and fault diagnosis, and discloses a low-wrapping-length interconnection network intermittent fault diagnosis method based on a graph attention mechanism. Aiming at the hierarchical recursive structure characteristics and high connectivity characteristics of the network, under the PMC fault diagnosis model, the test signs in the node neighborhood are obtained through a multi-round test strategy. A local statistical feature extraction method is used to construct a feature vector for each node, and a zero padding and noise enhancement technology is used for pretreatment. Finally, a graph attention network model is constructed, the importance weight of the neighbor node test result is dynamically learned by using the attention mechanism, and the accurate diagnosis of the node fault state in the network is realized. The application uses the powerful local information aggregation capability of the graph attention network, breaks through the diagnosis degree limitation of the traditional algorithm, and can still maintain high diagnosis accuracy and robustness under the condition of high fault rate, incomplete test signs and large network scale.

The invention belongs to the technical field of system identification, and particularly relates to a multi-batch variable error system identification method based on noise enhancement contrast learning. The invention provides a noise enhancement auxiliary sequence contrast learning algorithm for solving the problems that deviation exists in input noise variance estimation and noise is amplified in the data conversion process. According to the algorithm, triple representation of a pseudo denoising sequence, original data and a noise enhancement sequence is constructed, and a contrast learning mechanism is utilized to enable the original data to approach the pseudo denoising sequence and be far away from the noise enhancement sequence, so that dependence on accurate noise variance is reduced, and feature reconstruction capability and noise robustness are improved. The encoder can effectively extract sequence features, and overcomes the adaptation limitation of a traditional method on changing operation points. The effectiveness of the algorithm is verified through a cascade water tank example.

The invention provides a clock data recovery method based on assistance of a lightweight equalizer, and a clock data recovery (CDR) process in digital signal processing of a receiving end comprises the following steps: carrying out interpolation on an input digital signal by using an interpolation filter, and outputting a resampling signal according to interpolation phase information provided by a numerically controlled oscillator; a feed-forward equalizer (FFE) is adopted to equalize the resampled signal, channel damage is preliminarily compensated, an output signal of the FFE is input to a single-tap noise eliminator, noise enhancement introduced by the FFE is suppressed by subtracting weighted adjacent symbol judgment errors, and a signal after noise elimination is obtained; the signal after noise elimination is input to a Mueller-Muller time sequence error detector (MM-TED), and time sequence error information is extracted; and the time sequence error information is filtered by the loop filter and then controls the numerical control oscillator, and the interpolation phase of the interpolation filter is adjusted to complete the CDR closed loop. According to the invention, noise enhancement caused by FFE in the CDR loop can be effectively suppressed, the time sequence error detection precision is significantly improved, and the transmission performance of the system is improved.

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 invention belongs to the technical field of modulation identification, and particularly relates to a data enhancement method for a modulation signal, which comprises the following steps: a receiver acquires an original signal sequence at a high sampling rate, and after filtering, down-conversion and down-sampling processing, the original signal sequence is respectively stored as I / Q signals; and fingerprint feature enhancement, phase shift and frequency shift combined enhancement, sample bias enhancement, noise enhancement and signal modal enhancement are sequentially performed on the I / Q signal to realize signal enhancement. According to the method, information of various aspects of signals can be enriched, the adaptability of a model trained by using a data set is enhanced, the method is more in line with real communication signal features, and meanwhile, the enhanced signal data can quantify various feature parameters and contain equipment fingerprint features.

This application relates to a method, apparatus, device, and medium for multi-scene audio noise enhancement. The method includes: an audio service system acquiring noise types in a target acoustic scene and the original audio requiring multi-scene noise enhancement; the audio service system transmitting each noise type as text embedding to a potential diffusion model in a noise generation system, using Gaussian noise distribution and text embedding as starting points in the potential diffusion model to progressively generate noise audio samples; copying each noise audio sample according to multiple preset volume multiple thresholds to determine the noise audio samples corresponding to the multiple preset volume multiple thresholds; randomly selecting one or more noise audio samples corresponding to each noise type's preset volume multiple thresholds and synthesizing them with the original audio requiring multi-scene noise enhancement to obtain the noise-enhanced audio. This application enables the model to better adapt to the actual environment and improves its robustness.

The invention discloses a noise enhancement nonlinear system joint detection and estimation method under a Neyman-Pearson framework, and belongs to the field of signal processing. Firstly, independent additive noise is added to a nonlinear system input signal, and noise-corrected nonlinear system output is obtained after the nonlinear system input signal passes through the nonlinear system. And secondly, under the Neyman-Pearson criterion, performing binary hypothesis judgment based on the output, and estimating unknown parameters in the signal of which the judgment result is hypothesis H1. And under the condition of ensuring that the detection probability and the false alarm probability meet certain constraints, constructing a noise enhancement nonlinear system joint detection and estimation model which minimizes the conditional estimation risk. The additive noise is the optimal solution of the model and is random distribution formed by convex combination of not more than three constant vectors with a certain weight. According to the method, noise enhancement and nonlinear system joint detection and estimation under the Neyman-Pearson criterion are combined, and the estimation performance is further improved under the condition that the detection performance is not reduced.

The invention discloses a novel power system-oriented regional load uncertainty perception modeling and prediction method, and particularly relates to the technical field of load prediction. According to the method, a three-level structured influence factor system and an influence relation model are constructed based on distributed new energy output characteristics, charging and discharging states of an energy storage device and charging behaviors of an electric vehicle, and a load uncertainty influence factor characteristic library is formed; then, a multi-scale feature representation library is generated by extracting distribution features, fluctuation features and time sequence response features; a Bayesian long-short-term memory network is combined with an attention mechanism to serve as a basic framework, and a regional load uncertainty perception model is constructed through scene disturbance training, noise enhancement training and multi-target optimization training; and finally, reasoning and outputting a load distribution range, a confidence interval and a fluctuation trend through the model. According to the method, multi-dimensional and precise representation and prediction of regional load uncertainty are realized, and reliable data support is provided for scheduling decisions of a novel power system.