113 results about "Mixed noise" patented technology

The invention provides a three-dimensional seismic data mixed noise suppression method based on an MSAT-Unet. The method comprises the following specific steps: constructing an MSAT-Unet network comprising a multi-scale expansion convolution residual module, a channel-space attention mechanism and a Transform convolution module; the encoder is improved into multi-scale expansion residual convolution, so that the receptive field is expanded, and the capability of capturing local details and global semantic information is enhanced; a channel-space attention mechanism is integrated behind the decoder, a direction sensitive context is extracted through multi-dimensional adaptive pooling, and details and edge recovery are enhanced; meanwhile, an improved decoder is a Transform convolution module, and the feature reconstruction and complex structure recovery capability is improved; the optimized model carries out training and reasoning on the three-dimensional seismic data, mixed noise can be effectively suppressed, a clear data basis is provided for subsequent interpretation, and the method is high in generalization and robustness and good in performance in the aspect of three-dimensional seismic data denoising.

The invention discloses a PET detector signal simulation generation method based on artificial intelligence, and the method comprises the steps: generating an initial two-photon signal pair which meets the physical constraints of initial energy and time difference through a two-photon signal generator which fuses Transform and U-Net; decoupling the mixed noise into a plurality of independent physical noise components according to a PET noise physical priori library by using a multi-branch decoupling network based on an attention mechanism, and outputting a pure signal and a noise component map; performing linkage adjustment on the pure signal and the noise component according to a target parameter set by a user through a space-time-noise cooperative regulator; and finally, a customized signal data format is adaptively output according to the downstream task type. According to the method, high-fidelity, interpretable and adjustable coincidence event-level signal simulation is realized, the core problems of complex modeling, noise distortion, lack of relevance and poor scene adaptability of a traditional method are effectively solved, and the efficiency and precision of PET detector research, development and test are remarkably improved.

The invention discloses a joint estimation indoor positioning method, a joint estimation indoor positioning system, a medium and equipment, and relates to the technical field of indoor positioning, and the joint estimation indoor positioning method comprises the steps that each anchor node sends a signal to a neighbor anchor node and collects RSSI data, the collected data is fitted, and the path loss index of each anchor node is estimated; performing k-means clustering analysis on the path loss index, and taking the mass center with the most indexes as a final path loss index estimation value; constructing a positioning problem by using a maximum likelihood estimation method and Huber loss, and converting the positioning problem into a joint optimization problem of hybrid semi-definite second-order cone programming; and dynamically updating a Gaussian mixture noise parameter through an EM algorithm, synchronously optimizing the position of a target node and the transmitting power of an anchor node in each iteration, and outputting a final positioning result. According to the method, the target node is positioned under the condition that malicious anchor nodes and Gaussian mixed noise exist, and the accuracy and efficiency of positioning are improved.

The invention provides an image noise reduction method and system, computer equipment and a storage medium, and belongs to the field of image processing, and the method comprises the steps: firstly converting an original strip mine coal rock image into a gray level image, and carrying out the image processing of the gray level image for the problem of edge blurring caused by mixed noise in the strip mine coal rock image; the method comprises the following steps: firstly, extracting a noise model, analyzing the noise type and distribution characteristics of the noise model to implement preliminary noise reduction, filtering out small-scale noise of an image subjected to preliminary noise reduction through Gaussian-Laplacian joint transformation to obtain an enhanced image, then calculating a gradient magnitude image of the enhanced image, dynamically generating a parameter matrix in combination with a normalized gradient value, and constructing an adaptive generalized overall variation noise reduction model; and taking the gradient amplitude image and the self-adaptive parameter matrix as input, updating the optimal estimation image through iterative optimization, and obtaining an optimal noise reduction image when the iteration energy variation is smaller than a threshold value. According to the method, while mixed noise is removed, coal rock texture details are remarkably reserved, and high-quality data support is provided for mining decisions of strip mines.

The invention discloses a joint blind denoising method and system based on self-heuristic learning and Bayesian reasoning, belongs to the field of computational imaging, and solves the problems that in the prior art, the mixed noise modeling capability is insufficient, the performance is degraded under the condition of low signal-to-noise ratio, the combination of uncertainty quantization and regularization is lacked, and the generalization capability is limited due to data dependence. Comprising the following steps: collecting an original image and preprocessing; generating a noise data pair; an enhanced residual attention U-Net model is constructed; a noise estimation sub-network is adopted to extract noise features, the noise features are fused with original image features, and the model is trained; adopting the trained model to carry out multiple times of forward propagation on the same input image to obtain multiple groups of denoising results; calculating a mean value and a standard deviation to obtain a de-noising prediction and uncertainty heat map; and training the trained model again based on the uncertainty heat map, optimizing network parameters, and obtaining a final denoising prediction result and uncertainty estimation thereof. The method is suitable for complex noise distribution processing scenes.

The invention relates to a voice signal filtering and noise reduction method based on a Mama generative adversarial network, and the method comprises the following steps: designing a noise synthesis strategy for different application scenes, collecting real environment noise to construct a mixed noise library, and constructing a voice data set with noise and a voice data set without noise, and the voice data set and the voice data set do not need to be matched; constructing a Mama-based generative adversarial network, and realizing end-to-end conversion from the noisy voice to a time sequence filtering result; the construction of the model comprises the steps of designing a generator network, designing a discriminator network and optimizing a loss function. According to the invention, the robustness and generalization ability of voice signal noise reduction can be improved.

The invention discloses a stone surface flaw automatic identification method and system based on an intelligent algorithm, and belongs to the technical field of machine vision and stone processing. The invention provides an automatic detection scheme for solving the problems that in the prior art, manual stone slab defect detection is low in efficiency and high in subjectivity, and a traditional machine vision method is insufficient in detection precision under the conditions of complex stone slab textures and mixed noise. The method comprises the following steps: constructing an image acquisition system and carrying out camera calibration and image correction; the method comprises the following steps: preprocessing a stone plate image by adopting a denoising method combining median filtering and non-local mean (NLM) filtering, and extracting a stone plate contour by combining an improved Canny algorithm; and then, solving the maximum inscribed rectangle in the contour through a histogram area method, performing histogram equalization enhancement on the rectangular region, and finally, segmenting and identifying defects such as color spots and color lines by adopting a region splitting and merging algorithm combined with morphology.

The invention discloses a near-field electromagnetic wave imaging multi-mode noise cooperative suppression method, relates to the technical field of electromagnetic wave imaging, and aims to solve the problems that the suppression effect on speckle, stripe and Gaussian mixture noise is poor and a target structure is easy to lose in the prior art. According to the method, a complex field speckle suppression-multidirectional fringe separation-cross-channel Gaussian suppression three-stage cooperation scheme is adopted, and firstly, the amplitude and phase of a complex field image are processed through an adaptive threshold value to remove speckle noise; respectively executing horizontal / vertical ADOM filtering on the real part and the imaginary part of the complex field to eliminate stripe noise; and finally, Gaussian noise is suppressed by combining BM3D filtering and three-dimensional transform domain optimization, and robustness is improved through multi-frequency point fusion. Experiments show that the SSIM of the method is improved by 21.7% compared with that of a traditional method, the GSSIM and the PSNR are optimal, the structural features of the target can be reserved in a strong noise environment, and the method is suitable for scenes such as defect detection of near-field electromagnetic wave synthetic aperture imaging.

The invention relates to the technical field of image processing, in particular to a hybrid noise removal method and system based on a wavelet framework, and the method comprises the steps: obtaining an original image, preprocessing the original image, converting the original image into a matrix, and decomposing a denoising problem into a plurality of sub-problems based on a denoising model; wherein the denoising model restrains the edge and texture structure of an image through a regular term, captures an abnormal point through a data fidelity term of impulse noise, and suppresses long-tail noise through a data fidelity term of Cauchy noise; each sub-problem is solved, the solving result of each sub-problem is applied to the next sub-problem for iteration, and when the error between the iteration results of the kth step and the (k + 1) th step is smaller than a set value, the iteration result of the last step is the restored image after noise is removed. The image is recovered by taking a summing item of the Cauchy noise and the impulse noise as a data fidelity item of the model and taking a wavelet frame as a regular item.

The invention discloses an AI-driven vehicle-mounted sound field real-time modeling and voice separation method, and relates to the technical field of voice signal processing. A main control unit comprising a time sequence synchronizer, a resource scheduler and a health monitor is constructed. 3D sound field modeling is carried out by adopting a lightweight STCN + bidirectional LSTM network, adaptive updating of the model is realized through EWC incremental learning, and a CNN-LSTM noise classification network and targeted suppression algorithms such as ANF / spectral subtraction are developed. The voice separation module adopts an improved Conv-TasNet architecture, 3D spatial constraint and a multi-task loss function are fused, and low delay is realized under INT8 quantization and pipeline processing. The system dynamically optimizes parameters through a real-time regulation and control unit, supports scene self-adaption, finally achieves a separation effect in a mixed noise scene, reduces the delay of the whole system, and effectively improves the definition and stability of vehicle-mounted voice interaction.

The invention discloses a wavelength modulation spectral signal denoising method based on an unsupervised auto-encoder. The method comprises the following steps: step 1, constructing a WMS harmonic signal data set; step 2, training an HA-CAE denoising network model; 3, the HA-CAE denoising network model is evaluated and optimized, and an optimal HA-CAE denoising network model is obtained; and step 4, integrating the optimal HA-CAE denoising network model to a sensor system to realize real-time denoising processing of the signal. According to the method, a targeted data set and an improved HA-CAE denoising network model are constructed, three attention mechanisms are fused to accurately capture signal local details, time sequence association and global channel characteristics, non-stationary mixed noise characteristics are adapted, the denoising effect is improved, the generalization ability and the real-time processing ability of the model are guaranteed, and the method is suitable for popularization and application. The method is suitable for wavelength modulation spectrum signal processing in various complex scenes such as industrial leakage monitoring and atmospheric environment detection.

The application discloses a near-field electromagnetic wave imaging multi-modal noise cooperative suppression method, relates to the technical field of electromagnetic wave imaging, and aims to solve the problems of poor suppression effect of spot noise, stripe noise and Gaussian mixed noise and easy loss of target structure in the prior art. The method adopts a three-stage cooperative scheme of "complex domain spot suppression-multi-direction stripe separation-cross-channel Gaussian suppression", first removes the spot noise by adaptively processing the amplitude and phase of the complex domain image through a threshold value; then eliminates the stripe noise by performing horizontal / vertical ADOM filtering on the real part and the imaginary part of the complex domain, respectively; finally, combines BM3D filtering and three-dimensional transform domain optimization to suppress Gaussian noise, and improves the robustness through multi-frequency point fusion. Experiments show that the SSIM of the method is improved by 21.7% compared with the traditional method, the GSSIM and PSNR are optimal, the target structure features can be reserved in a strong noise environment, and the method is suitable for defect detection and other scenes of near-field electromagnetic wave synthetic aperture imaging.

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.

The invention discloses a multi-modal denoising method for a hyperspectral image, which is characterized by comprising the following steps of: S1, respectively performing low-rank tensor representation on the hyperspectral image and a registered multispectral image by utilizing Tucker decomposition, and extracting core tensors of the hyperspectral image and the registered multispectral image; s2, establishing a correlation model between the hyperspectral core tensor and the multispectral core tensor through model-driven linear mapping or data-driven multi-layer perceptron network; s3, iteratively solving a core tensor, a factor matrix and correlation model parameters by adopting an alternating direction multiplier method; and S4, reconstructing a denoised hyperspectral image by using the optimized hyperspectral core tensor and factor matrix. Compared with the prior art, the method has the advantages that the spectral details of the hyperspectral image and the high-signal-to-noise-ratio spatial information of the multispectral image are fully mined and utilized, the restoration precision in the mixed noise scene is effectively improved through the double-Tucker decomposition framework and the core tensor association strategy, and the high-quality hyperspectral image is obtained.

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.

The invention discloses an aircraft fuel quantity measurement self-adaptive filtering method, system and device based on multi-dimensional statistic dynamic adaptation and a medium. The method comprises the following steps that S01, signals are collected and preprocessed; dividing a fuel quantity digital signal output by a sensor into continuous time blocks according to a fixed time window; s02, calculating a multi-dimensional statistical magnitude; comprising the mean value, variance and kurtosis of the current time block; s03, performing multi-stage filtering processing; s04, performing data fusion and conversion; and keeping the original value of the data pulse point, and combining the original value with the fine filtering result of the non-pulse point to obtain a complete filtered signal. By monitoring the multi-dimensional statistical characteristics (mean value, variance and peak value) of a fuel quantity signal in real time and combining a coarse filtering-pulse detection-fine filtering multi-stage structure, filtering parameters are dynamically adjusted, efficient suppression of mixed noise (low-frequency shaking, Gaussian noise and pulse noise) is achieved, and the precision of fuel measurement under complex working conditions is improved.

The invention relates to the technical field of railway vehicle noise analysis and processing, and particularly discloses a railway vehicle noise distinguishing and extracting device and method.The device comprises an acquisition module, a processing module and an output module, and the acquisition module is used for acquiring carriage mixed noise in the running process of a railway vehicle; the processing module is used for performing frequency decomposition, noise classification and independent loudness analysis of various types of classified noise on the collected mixed noise of the carriage; the output module is used for outputting and / or storing the loudness values and the frequency characteristics of various types of noise after classification; according to the method, wheel track noise, passenger voice and train station reporting voice can be accurately separated and subjected to loudness analysis in real time, the problem that the passenger voice and the train station reporting voice are difficult to separate and distinguish is solved, and a scientific noise monitoring and management means is provided for rail transit operation enterprises; the passenger comfort level is improved, the station reporting volume and the operation service quality are optimized, and complaint and transformation cost caused by noise is reduced.

The invention relates to the technical field of image style migration, and provides a diffusion style migration method and system based on latent distribution re-anchoring dynamic injection, and the method comprises the steps: carrying out the second-order statistical alignment of a content hidden variable and a style hidden variable through a potential covariance re-coloring method; automatically positioning an injection starting point of the style according to the edge intensity of the content image, and injecting hidden distribution reanchoring noise into the initialized mixed noise hidden variable to realize hidden distribution reanchoring; the content self-attention features and the style self-attention features are fused through a soft + dynamic style injection module, in the reverse sampling process of the calibrated hidden variables, the fused attention features are gradually injected into an attention layer in the diffusion process at the injection starting point, diffusion denoising is carried out, and a style migration image is generated. According to the method, the image with rich structure details is protected, and the excellent content structure maintaining capability and the accurate style reappearance are realized at the same time.

The invention relates to the technical field of computer vision and video image processing, and discloses a passive night vision full-color video image enhancement method and system based on AI learning, and the method comprises the steps: constructing a Poisson to Gaussian mixed noise model through sensor metadata, generating a signal-to-noise ratio confidence map, and quantifying the physical reliability of pixels; constructing an optical flow energy functional through a signal-to-noise ratio weighted data item and a semantic boundary constraint regular item in combination with a semantic label graph, and calculating a high-robustness optical flow field; in a time-space fusion stage, taking the signal-to-noise ratio confidence map as a gating condition, and dynamically adjusting a time domain recursion fusion proportion of historical features and current features; and finally, driving an adaptive normalization unit by using the semantic tag graph, and retrieving a semantic chrominance priori library to restore the inherent physical color of the object. According to the method, physical noise and scene semantic priori are utilized, the problems of random noise interference, motion ghosting and color loss under low illumination can be solved, and clear passive full-color night vision imaging with real color is achieved.

The invention discloses a self-supervised laser speckle denoising method based on a transform domain, and relates to the field of laser measurement. Laser speckle noise is modeled into a Poisson-Gaussian mixture model, statistical characteristics of the laser speckle noise are represented, and efficient training of a denoising model is realized on the premise that a clean image does not need to be labeled by adopting a parameter estimation network fusing a residual attention strategy with a U-Net architecture and a self-supervised denoising network containing an asymmetric convolution Inception structure. A mixed noise domain is converted into a Gaussian noise domain through generalized Ansu transform, multi-loss function optimization is combined, the algorithm denoising performance is improved, the peak signal-to-noise ratio of a light spot image is remarkably improved, and then the precision of a laser measurement system is improved. The method is simple and efficient, adapts to different laser measurement devices, can effectively improve the measurement precision in the fields of laser radar, industrial detection, medical imaging and the like, and provides core technical support for the industries of intelligent manufacturing, automatic driving and the like.

A kind of under-forest ginseng year nondestructive identification method based on convolutional neural network model and near-infrared hyperspectrum belongs to under-forest ginseng year nondestructive identification technical field.The method solves the problems of time-consuming, high cost, poor repeatability and often destructive to under-forest ginseng in the prior art. The method of the present application first acquires the near-infrared hyperspectrum images of under-forest ginseng of different years which have been marked with years, then performs irradiation correction, interested region extraction and format conversion, and then performs mixed noise reduction processing on the obtained original near-infrared hyperspectrum curve of under-forest ginseng, extracts characteristic spectral band using the uninformative variable elimination method, establishes a convolutional neural network model, and iteratively trains the convolutional neural network to obtain a trained neural network model. Finally, the trained neural network model is used to nondestructively identify the under-forest ginseng to be detected to obtain the year of the under-forest ginseng to be detected. The method can realize rapid, real-time, nondestructive and accurate identification of under-forest ginseng of different years.

The application discloses a low-quality data sentiment metaphor recognition method and system based on fuzzy granular ball modeling, and relates to the field of natural language processing.The method comprises the following steps: S1, obtaining low-quality text data and performing labeling and semantic preprocessing to obtain text data containing linguistic information; S2, inputting the text data containing linguistic information into a word embedding model to obtain sentiment metaphor word vectors and form a word vector matrix; S3, generating a granularity list satisfying a containment threshold by using fuzzy granular ball calculation, performing feature reduction on the word vector matrix by using a variable precision dependency function, and obtaining a reduced matrix; and S4, dividing the reduced matrix into a training set and a test set, training and predicting a convolutional neural network, and obtaining a sentiment metaphor recognition result.The application selects features by using fuzzy granular ball calculation, deletes redundant features, improves the feature extraction efficiency of the convolutional neural network model, and solves the problem of mixed noise information in a large amount of text information acquisition.

The application discloses a tunable diode laser absorption spectroscopy (TDLAS) signal denoising method and device. The method comprises the following steps: obtaining a noisy TDLAS spectrum signal to be processed; processing the noisy TDLAS spectrum signal based on a diffusion model to obtain a denoised spectrum signal, wherein the processing comprises a forward diffusion process and a reverse denoising process; jointly optimizing the diffusion model based on the denoised spectrum signal, in combination with physical constraints related to TDLAS spectrum absorption characteristics and / or frequency domain constraints related to spectrum frequency domain structure consistency; and outputting the denoised spectrum signal. The application can effectively remove complex mixed noise while ensuring that the denoised spectrum signal conforms to the physical absorption mechanism and retains the frequency domain structure characteristics by jointly optimizing the diffusion model in combination with the physical and frequency domain constraints, thereby improving the accuracy and reliability of TDLAS gas detection.

The application provides a time-frequency learning estimation method for mixed noise sinusoidal signal frequency, and belongs to the technical field of signal processing, comprising the following steps: obtaining and preprocessing a mixed noise sinusoidal signal; filtering to obtain a first signal; calculating a self-correlation function; calculating a preliminary period estimation value of the first signal to obtain a first frequency estimation value; inputting a frequency spectrum feature and a self-correlation peak value feature of the first signal and the first frequency estimation value into a pre-trained frequency fine estimation model to obtain a fine frequency estimation value, which is recorded as a second frequency estimation value; optimizing the last M layers of the model to obtain a first model; continuously optimizing to obtain a second model; continuously obtaining the frequency spectrum feature, the self-correlation peak value feature and the first frequency estimation value of the first signal and inputting them into the second model, and taking the output result of the second model as a final frequency estimation value of the mixed noise sinusoidal signal and outputting the final frequency estimation value.

The invention discloses a self-heuristic learning blind denoising method and system based on consistency difference guidance, belongs to the technical field of computer imaging, and solves the problems that the blind denoising performance of an SN2N method is insufficient under the condition of mixed noise distribution, the generalization ability of noise distribution is limited, and denoising of data with an extremely low signal-to-noise ratio is insufficient. The method comprises the steps that original picture data are collected and preprocessed, and an image data set is generated; generating a noise data pair by adopting an SN2N self-supervision method; performing consistency difference evaluation on the noise data pairs, and calculating a pixel-level difference chart; establishing a U-Net network based on a residual attention mechanism, wherein the network comprises an up-sampling module, a down-sampling module and a bottleneck layer; training the network by using a pixel-level difference graph, and constraining a loss function of the noise estimation branch according to the pixel-level difference graph; and adopting the trained network to predict and obtain a de-noising result. The method is suitable for living cell super-resolution microscope imaging and three-dimensional volume data denoising scenes.

Various examples are provided related to reconstructing images such as, e.g., medical images from low-dose image scans. Adversarial learning such as, e.g., a Cyclic Simulation and Denoising (CSD) framework can be used to address challenges of complicated mixed noise in real low-dose scans. The CSD framework can include a simulator model that can extract low-dose noise and features (e.g., tissue features) from separate image spaces into a unified feature space and a denoiser model that can learn how to remove noise and restore features, simultaneously. Both the simulator model and the denoiser model can regularize each other in a cyclic manner to optimize network learning effectively. The CSD framework in combination with phantom scans can embrace the realistic low-dose noise and features into a unified learning environment to address the challenge of real low-dose image restoration.

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 invention discloses a radar super-resolution imaging method in a mixed noise environment, and belongs to the field of radar detection and imaging. A new radar echo model is constructed by analyzing noise characteristics in an actual environment, and high-resolution target reconstruction in a mixed noise environment is realized by using the self-adaptive anti-noise capability of a correction loss function. According to the method, firstly, a traditional echo model is optimized, and an echo mathematical model closer to the actual environment is obtained; then, the mathematical characteristics of the correction loss function are utilized to realize the good suppression capability on the mixed noise; and finally, realizing closed solution of the target function through a generalized ridge regression estimation algorithm based on a maximum and minimum criterion, thereby completing accurate reconstruction of the target scene in the mixed noise environment. Compared with a traditional super-resolution method, the method of the invention can realize radar foresight super-resolution imaging in a mixed noise environment.

The invention discloses a time series data generation method based on a mixed noise correction flow model, and the method comprises the steps: introducing a learnable mixed noise mechanism, and carrying out the weighted fusion of Gaussian noise and conditional noise generated by a prior generation model, so as to enhance the prior guiding capability of a generation process; under a correction flow framework, through a double-peak adaptive sampling strategy based on path complexity, more time steps are distributed at the early stage and the late stage of a sampling path, so that global structure alignment and local detail recovery are both considered, and manual hyper-parameter setting is reduced. According to the method, the generation quality and diversity can be improved in the unconditional generation task, and the generation error is reduced under the condition of the same step number.