101 results about "Soft thresholding" patented technology

The invention relates to the field of image processing, and particularly discloses a rural highway pavement disease intelligent identification and positioning system comprising an image standardization module used for obtaining a standardized grayscale image; the wavelet decomposition module is used for obtaining a low-frequency sub-band and a plurality of high-frequency sub-bands; the high-frequency processing module is used for carrying out soft threshold processing on the high-frequency coefficient and retaining high-variance region features; the inverse transformation module is used for reconstructing the de-noised image; the edge enhancement module is used for highlighting crack and pit slot target contour information; the binarization module is used for segmenting a foreground disease candidate region; and the edge filling module is used for separating the disease from the background. According to the method, the core contradiction between background impurity removal and disease feature retention in rural highway tiny disease recognition is effectively solved, a traditional denoising algorithm either smooths tiny disease features or cannot thoroughly remove background impurities, and the system achieves the balance of the background impurity removal and the disease feature retention through cooperation of multiple modules.

The invention relates to the technical field of test and measurement, and discloses a series fault arc detection method, which comprises the following steps: collecting current data of fault arc waveforms and normal arc waveforms of a plurality of different loads; performing wavelet transformation on the preprocessed current data, dynamically extracting detail coefficients of a target layer number, calculating a standard deviation, a normalized energy ratio and an energy entropy based on the wavelet coefficients, splicing peak-to-peak values to form a four-dimensional feature vector, adding a normal or fault label, and converting the feature vector into a three-dimensional tensor; the residual shrinkage module is used for constructing a deep residual shrinkage network model based on a one-dimensional convolutional neural network and introducing an attention threshold generation and soft thresholding mechanism; training the model and monitoring the performance by adopting an early stop method; and inputting label-free sample data to the model, and outputting a detection result. The problems that in the prior art, deep features cannot be reflected, the training cost is high, and overfitting is prone to occurring are solved, and the purposes of improving the accuracy, being high in stability and efficient in detection are achieved.

The invention relates to the technical field of signal processing, and discloses a flip chip vibration signal denoising method and system, and the method comprises the steps: obtaining a vibration signal responded by a to-be-detected flip chip under the action of external ultrasonic excitation, carrying out the segmentation processing of the vibration signal, constructing an initial sparse dictionary according to the segmented vibration signal, and iteratively updating the sparse dictionary; in the iteration updating process, sparseness parameters are adjusted in real time along with the number of iterations, and soft threshold denoising is conducted on residual terms; introducing a feature projection matrix to perform joint modeling on each section of vibration signal, and constructing a joint optimization model according to the updated sparse dictionary and the corresponding sparse coefficient; alternately optimizing the sparse coefficient and the feature projection matrix in the joint optimization model to obtain an optimal solution of the sparse coefficient; and combining the updated sparse dictionary and the optimal solution of the sparse coefficient to reconstruct the vibration signal to obtain a denoised vibration signal. According to the method, transient features can be effectively extracted, expression of key features is enhanced, noise is suppressed, and denoising robustness and reconstruction precision are improved.

The invention relates to the field of data anomaly detection, and particularly discloses a multi-stage anomaly detection system based on multivariable time sequence data, which comprises an input data acquisition module used for acquiring time sequence multivariable data; the MTS-Mixer spatio-temporal feature extraction module is used for extracting multi-level time sequence correlation features through a feature mixing technology and generating low-rank compression prediction features; the time sequence anomaly detection module is used for realizing anomaly positioning and classification by adopting alternate stacking of an Angle-Attention layer and a feed-forward network, expanding anomaly differences in combination with prior constraints and adversarial learning and fusing reconstruction errors and association differences to generate anomaly scores; and the attention mechanism soft thresholding module is used for suppressing noise, enhancing key signals and outputting high signal-to-noise ratio representation. According to the method, multiple technologies are integrated, and real-time anomaly detection and interpretable positioning of the high-dimensional time series data are realized through multi-stage collaborative optimization.

The invention discloses a hydroelectric generating set vibration signal noise reduction method based on SW-EEMD (Single Wave-Ensemble Empirical Mode Decomposition) and Hansel-SVD (Hanker-Singular Value Decomposition). The method comprises the following steps: processing an original noisy signal by adopting an SW-EEMD method; screening effective IMF components through correlation coefficients; respectively constructing Hankel matrixes for the screened IMF components and residual components; respectively carrying out singular value decomposition on the constructed Hankel matrix; carrying out soft threshold processing on the obtained singular value; the method aims at restraining the end effect of EEMD, noise in the vibration signals can be effectively filtered out, signal features are enhanced, more real and effective signal components can be obtained easily, and more reliable data are provided for state operation and maintenance of the hydroelectric generating set.

The invention discloses an acoustic signal partial discharge detection system of a distribution network transformer, which is characterized by comprising a multi-sensor acquisition unit, a synchronous clock control unit, a signal preprocessing unit, an edge calculation processing unit and a communication and storage unit, the edge calculation processing unit integrates a cross-band denoising module, a multi-scale feature extraction module, a lightweight neural network diagnosis module and a diagnosis result fusion module, wherein the cross-band denoising module is used for performing joint denoising on audible sound and ultrasonic frequency band signals. According to the invention, a set of partial discharge detection scheme for a distribution network transformer scene is established, and the scheme flow comprises denoising, feature extraction, neural network training and diagnosis. According to the scheme, audible audio frequency band and ultrasonic frequency band data are covered, and interference generated by mechanical vibration of the transformer in the ultrasonic frequency band is removed through a soft threshold value of wavelet transformation and a pulse detection denoising algorithm.

The invention discloses a buoy-based water body apparent spectrum real-time acquisition method, device and equipment, a medium and a product, and relates to the field of marine environment intelligent monitoring, and the method comprises the following steps: preprocessing original marine spectrum data to generate high-quality marine spectrum data with time-space alignment; reconstructing a three-channel spectral data matrix through multi-scale discrete wavelet decomposition and soft threshold quantization compression, inputting the three-channel spectral data matrix into a dynamic weight model, and identifying a spectral abnormal mode; obtaining an abnormal feature mark set, calculating spectral shape parameters, and generating a shape parameter sequence; and fusing the abnormal feature mark set and the shape parameter sequence to generate a spectral morphological feature curve, integrating the three-channel spectral data matrix, the abnormal mode and the shape parameter sequence, and outputting a real-time water body apparent spectrum acquisition report. Through deep fusion of the dynamic weight model and the multi-dimensional morphological features, the accuracy and reliability of spectrum anomaly detection in a complex marine environment are remarkably improved.

The invention discloses a state monitoring and diagnosing method and system for a marine main engine, and the method comprises the steps: collecting the operation state parameters of the marine main engine, and carrying out the preprocessing; embedding a dynamic soft thresholding layer in each residual module of the residual shrinkage network, and executing channel-by-channel soft thresholding on the feature map; constructing a double-flow attention network, fusing the time-frequency domain characteristics of the vibration signals and the temperature field data of the space heat conduction model, and capturing a causal sequential relationship of multi-modal data through a gating mechanism; carrying out embedded optimization on the model by adopting depth separable convolution, adaptive load normalization and knowledge distillation; the model is carried on a shipborne industrial personal computer, and whether model updating needs to be triggered or not is judged by calculating the weight difference between the shipborne model and the global model of the cloud platform. According to the method, a whole-process closed loop from data acquisition to model deployment is realized, the anti-noise capability, causal association analysis, resource optimization and privacy protection are integrated, and the intelligent operation and maintenance requirements of the marine main engine are met.

The present application relates to a kind of unmanned aerial vehicle signal identification method and system, belong to unmanned aerial vehicle signal identification technical field.It includes: step 1: adaptive noise base estimation;The input unmanned aerial vehicle IQ signal, i.e.unmanned aerial vehicle signal is carried out short-time Fourier transform, and time-frequency matrix is obtained, generates unmanned aerial vehicle time-frequency diagram, estimates noise power spectrum, calculates the power spectral density of each frequency point;Step 2: time-frequency feature enhancement;5 order Butterworth band-pass filter is used to frequency domain filtering to signal;Soft threshold domain denoising of soft short-time Fourier transform result is used;Step 3: multi-feature fusion regression;Energy ratio feature, spectral peak sharpness feature are calculated, and deep learning feature is extracted, feature fusion and regression;Step 4: through the unmanned aerial vehicle signal classification model after training, unmanned aerial vehicle signal identification is realized.The present application method is high in precision in complex environment signal-to-noise ratio estimation, and unmanned aerial vehicle classification precision and generalization are excellent.

The present invention discloses a method for detecting and removing stripe noise in satellite-borne spectral images. The method comprises an image classification and characteristic analysis module, a stripe detection and analysis module (including a stripe detection unit, a characteristic analysis unit, and a degradation modeling unit), an image transformation module, and a soft threshold signal decomposition module. Based on independent analysis of the original space-spectral domain and the transform domain, the present invention comprehensively integrates the space-spectral domain analysis method and the transform domain analysis method to simultaneously detect and remove stripe noise. The method also fully analyzes the image characteristics and wavelet distribution characteristics, deploys an image decomposition method in the wavelet domain, effectively analyzes the stripe components, and retains image detail information. The present invention has the advantages of being adaptable to the complexity of spectral image sources and the diversity of application environments, taking into account the differences in different stripe noise distributions, and having strong generalization and robustness.

This invention discloses an underwater acoustic anti-interference transmission method and system based on sparse time-frequency feature mapping. The method includes: at the transmitting end, adaptively generating a fractional-order linear frequency-modulated waveform with a specific time-frequency shear slope based on the Doppler state of the underwater acoustic channel, achieving physical-layer focusing of transmitted energy; at the receiving end, constructing a hybrid observation model containing wide-block sparse channel components and narrow-block sparse burst noise components, and using a dual-channel variational Bayesian algorithm to jointly iteratively infer the posterior probability distribution of environmental burst noise in the fractional-order domain; finally, recovering the original signal through soft-threshold interference cancellation and fractional-order channel equalization. This invention effectively solves the communication failure problem caused by high-dynamic Doppler diffusion and marine biological impulse noise interference in underwater acoustics by actively mapping the waveform and using heterogeneous sparse joint inference at the receiving end, significantly improving transmission reliability in harsh underwater acoustic environments.

The present application relates to the technical field of transformer fault diagnosis, and in particular to a transformer body vibration signal analysis and fault diagnosis method, device and medium. The present application combines the kurtosis characteristics of the vibration signal, adopts a semi-soft threshold function wavelet denoising method based on the threshold selection method of the 3sigm rule, and achieves better denoising effect. Through the fault diagnosis model of the organic fusion of the two improved HHT transforms-mobilenetV2 model, combined with different feature extraction methods, it is more conducive to retaining the effective features of the vibration signal; the improved mobilenetV2 model designs a multi-scale deep convolution model, introduces a channel attention mechanism before the channel-by-channel convolution, and after multi-scale deep convolution feature extraction, a multi-source data attention mechanism is introduced; without affecting the safe and reliable operation of the transformer, the intelligent diagnosis of the vibration state fault of the transformer is realized.

The invention discloses a design method for ISTA sparse channel estimation based on a single-transmitting and single-receiving OFDM (Orthogonal Frequency Division Multiplexing) system. According to the method, the signal can be recovered under the condition of unknown sparseness, accurate reconstruction of the signal is realized, and a sparse channel is reconstructed through a small amount of training data, so that the training overhead is reduced, the spectrum efficiency is improved, and signal reconstruction is carried out by utilizing gradient updating and a soft threshold threshold function. A simulation experiment shows that the algorithm can realize better channel reconstruction, and meanwhile, the algorithm also has higher convergence speed and lower calculation complexity.

The invention provides a real-time synchronization and noise suppression method and system for a photoetching sensor array, and the method comprises the steps: capturing scattered light intensity fluctuation during photoetching exposure to generate a time sequence calibration pulse, and calculating a time sequence offset by combining the pulse with an original data packet of a sensor through an FPGA timestamp synchronization engine; adjusting a trigger signal through a digital delay line to realize time sequence calibration; inputting the calibrated data into a self-adaptive wavelet threshold filter, generating a dynamic threshold based on the speed and vibration intensity of the workpiece table, and outputting purified data through wavelet decomposition, specific layer detail coefficient soft threshold processing and reconstruction; and finally, feeding back the noise residual error of the purified data to the FPGA, and triggering secondary calibration when the noise residual error exceeds a threshold value. Through deep cooperation of the hardware-level timestamp engine and the physical constraint filter, the problems of time sequence offset and noise interference of the sensor array are thoroughly solved.

The application discloses a water acoustic signal time-frequency spectrum line spectrum extraction method and system, and belongs to the technical field of signal processing. A noisy time domain signal is generated through simulation, and a mask label of a time-frequency spectrum of the noisy time domain signal belonging to a line spectrum is generated through a soft threshold function; a denoising model is trained according to the time-frequency spectrum and the mask label; a target water acoustic signal is acquired, the time-frequency spectrum of the target water acoustic signal is input into the denoising model, and a mask label corresponding to the time-frequency spectrum input is output through inference; the time-frequency spectrum of the denoised target water acoustic signal is acquired according to the mask label and the time-frequency spectrum; an initial candidate point set of the time-frequency spectrum of the denoised target water acoustic signal is acquired, and an initial candidate point of a current frame time-frequency spectrum in the initial candidate point set is acquired; a correlation cost matrix is constructed, the initial candidate point and a trajectory are correlated and matched with the minimum difference as a target, and a line spectrum of the trajectory and the candidate point dynamic correlation is acquired. The method can balance denoising fidelity, detection accuracy and real-time performance.

This invention discloses an improved method for generating driver fatigue EEG data using a latent diffusion model, belonging to the field of EEG signal processing. The method includes: processing the original multi-channel driver fatigue EEG signal through multi-scale wavelet denoising, adaptive thresholding, and an improved soft thresholding function; then performing inter-channel covariance alignment and whitening to eliminate redundancy to obtain a preprocessed signal; performing short-time Fourier transform and logarithmic energy normalization to obtain normalized time-frequency features; inputting the time-frequency features into an encoder to obtain the mean and variance of latent variables, and obtaining latent variables through reparameterized sampling; reconstructing the time-frequency features using a decoder combined with fatigue state labels; training a conditional variational autoencoder by minimizing reconstruction loss and KL divergence loss; adding noise through forward diffusion in the latent space; training a denoising network to remove noise based on fatigue state labels during reverse denoising; inputting the denoised latent variables into the decoder, combining them with a specified fatigue state to generate new time-frequency features, and reconstructing them into the target EEG signal. This invention can enhance the training dataset and improve the accuracy of driver fatigue monitoring.

The invention discloses a signal processing and data extraction method and system for a rotary laser scanning measurement system, and the method comprises the steps: collecting an original electric signal outputted by a photoelectric sensor through a collector, and obtaining an original voltage-time sequence containing a scanning pulse, a synchronization pulse, and random noise; discrete wavelet decomposition is carried out on the collected original electric signals, pulse positions are positioned, and soft threshold processing and inverse wavelet transformation reconstruction mode processing are carried out on wavelet coefficients of all layers in sequence to obtain signal fragments containing pulse signals; performing nonlinear least square fitting by adopting a scanning pulse mathematical model and a synchronous pulse mathematical model to obtain a pulse time sequence of each transmitting station, classifying the pulse time sequences, and distributing each pulse time to the corresponding transmitting station to obtain a pulse time sequence of each transmitting station; and according to the rotation angle of the photoelectric sensor relative to each transmitting station and the accurate coordinate of the calibration point, solving the pose parameter of the transmitting station under the reference coordinate system or solving the three-dimensional space coordinate of the measurement point.

The invention relates to the technical field of electrocardiosignal quality evaluation, and discloses an electrocardiosignal quality evaluation method based on an unsupervised cascade adaptive network, which comprises the following steps: S1, preprocessing an obtained original electrocardiosignal: removing power frequency interference through adaptive notch filtering, removing high-frequency myoelectricity noise through soft threshold wavelet filtering, and obtaining a pre-processed electrocardiosignal; keeping a potential pathological characteristic waveform in a filtering process to obtain a preprocessed electrocardiosignal; s2, unsupervised multi-modal feature extraction: an unsupervised feature extraction network is constructed based on an auto-encoder; a normal electrocardiosignal distribution rule is automatically learned through an auto-encoder, so that the dependence on labeling resources of professional doctors is reduced; meanwhile, by means of a two-stage self-adaptive evaluation network, significant noise is filtered first, then pathological signals and noise are accurately distinguished, it is effectively avoided that abnormal waveforms caused by diseases such as myocardial infarction and atrial fibrillation are misjudged as noise, the retention rate of the pathological signals is remarkably increased, and more complete effective data support is provided for clinical diagnosis.

The invention discloses an underwater acoustic anti-interference transmission method and system based on sparse time-frequency characteristic mapping, and the method comprises the steps: adaptively generating a fractional order linear frequency modulation waveform with a specific time-frequency shearing slope at a transmitting end according to the Doppler state of an underwater acoustic channel, and achieving the physical layer focusing of transmitted energy; at a receiving end, constructing a hybrid observation model comprising a wide block sparse channel component and a narrow block sparse burst noise component, and inferring posterior probability distribution of environmental burst noise in a fractional order domain through joint iteration by using a dual-channel variational Bayesian algorithm; and finally, recovering an original signal through soft threshold interference cancellation and fractional order channel equalization. Through active feature mapping of the waveform and heterogeneous sparse joint inference of the receiving end, the problem of communication failure caused by underwater acoustic high-dynamic Doppler diffusion and marine organism impulse noise interference is effectively solved, and the transmission reliability in a severe underwater acoustic environment is remarkably improved.

The application discloses a kind of water turbine monitoring method and device, wherein the method comprises: using adaptive wavelet semi-soft threshold denoising method to the vibration signal data of water turbine is denoised;Characteristic data is extracted from vibration signal data using wavelet energy coefficient analysis method combined with wavelet decomposition coefficient mean square value statistical analysis method, and water turbine unit diagnosis sample database is established;Uncertainty bayesian neural network model based on deep learning is used to mine the feature relationship between water turbine data, which solves the problem that the existing technology detects water turbine by manual reinspection in offline state, with high implementation difficulty, low coverage, easy to miss detection, and unable to monitor water turbine unit in service state in real time.

The application discloses a kind of self-adapting enhancement and segmentation method of cracks in tunnel inner wall under dark light environment, comprising: collecting dark light tunnel inner wall gray image and normalizing;Adaptive contrast enhancement is carried out based on local brightness mean and standard deviation;Detail layer and base layer are obtained by using anisotropic orientation filter decomposition;Soft threshold denoising is carried out on detail layer based on local gradient adaptive threshold;Detail layer and base layer are fused according to local signal-to-noise ratio weighting;Crack saliency map is extracted by multi-scale phase consistency;Crack region segmentation is realized by using double-threshold hysteresis connection, and binary result is output.The application can significantly improve the accuracy and integrity of crack detection without relying on high-illumination imaging conditions.

The application relates to the technical field of ultrasonic detection and noise reduction, and provides an ultrasonic detection method for defects of an external delivery hose based on reciprocity difference and a hybrid neural network, which comprises the following steps: filtering an initial signal to obtain a first signal; decomposing the first signal into an approximate coefficient and a first detail coefficient; performing soft threshold processing on the first detail coefficient to obtain a second detail coefficient; reconstructing the second detail coefficient and the first approximate coefficient to obtain a second signal; performing normalization processing on the second signal to obtain a third signal; inputting the third signal into a convolutional neural network model to obtain a spatial feature vector; inputting the spatial feature vector into a recurrent neural network model to extract a time feature vector; fusing the spatial feature vector and the time feature vector to obtain a noise reduction signal; and integrating the convolutional neural network model and the recurrent neural network model with an ultrasonic detection system after training, so as to output a defect feature signal and improve the precision and detection efficiency of online detection of the external delivery hose.

This invention discloses a high-resolution range image feature preservation and enhancement method based on a deep unfolded network, comprising: preprocessing radar echo data into an initial feature tensor; constructing a low-rank sparse decomposition model and mapping the iterative solution process of the model to a deep neural network containing cascaded networks, with each layer corresponding to one iterative optimization step; in each layer, a low-rank near-end mapping module captures the global correlation between the range dimension and the Doppler dimension through a dual-path axial attention mechanism to update the low-rank clutter component matrix; a sparse near-end mapping module updates the sparse target component matrix using a complex soft thresholding operator to maintain target phase information while suppressing clutter; and a Lagrange multiplier update module is used to update the Lagrange multipliers. This invention overcomes the limitations of manual parameter tuning by introducing a deep neural network and a dual-path axial attention mechanism, enabling the acquisition of structurally complete and phase-accurate high-resolution range image features while effectively suppressing clutter.

This invention belongs to the field of computer vision and artificial intelligence technology, and discloses a sonar image target detection method and system based on soft thresholding and attention mechanisms. It extracts features from sonar images to obtain multi-scale feature maps, then fuses these multi-scale feature maps to obtain a fused feature map. A result prediction module then uses this fused feature map for detection, obtaining predictions of the target category and bounding box location. This invention performs dual optimization at the feature and loss function levels, reducing false alarms caused by noise and improving the detection accuracy and localization precision of small targets in sonar images without changing the image preprocessing workflow. It exhibits strong robustness and practical value.

This invention discloses a method, apparatus, device, and storage medium for estimating XL-MIMO near-field compressed channels. The method includes: first, obtaining the channel vector of the near-field compressed channel to be estimated; inputting the channel vector into a constructed channel estimation model, causing the channel estimation model to convert the channel vector into a polar-domain channel vector through a built-in sparse transformation matrix; compressing the polar-domain channel vector into a signal vector through a built-in sensing matrix; adding noise to the signal vector to obtain a received signal vector; inputting the received signal vector into a built-in LAMP layer, causing the LAMP layer to iterate over the received signal vector through a built-in soft thresholding function to calculate the estimated polar-domain channel vector corresponding to the polar-domain channel vector; and converting the estimated polar-domain channel vector into an estimate of the near-field compressed channel to be estimated through a built-in sparse transformation matrix. By implementing this invention, the number of pilot signals required by the system can be reduced, thereby reducing pilot overhead.

This application relates to a SAR sparse imaging method and system based on a dual-channel depth unfolding network. The method includes updating image features using a physical observation matrix; dual-channel parallel feature mapping, thereby focusing on the sparse recovery of strong scattering points and compensating for detail loss caused by soft thresholding; fusing the outputs of the two channels to obtain the final output of the layer; and network training and imaging. This application can achieve high-quality reconstruction of three-dimensional targets under sparse observation conditions through dual-channel fusion.

The invention discloses a lithium iron phosphate electrode material ferromagnetic impurity adsorption device and a detection method, and the method comprises the steps: collecting a multi-channel voltage signal of a powder flow in real time through a high-frequency electromagnetic induction sensor array, and carrying out the sliding slicing, and obtaining a digital time sequence matrix containing time-space information; a residual shrinkage convolutional neural network with an adaptive soft threshold noise reduction mechanism is constructed to extract waveform features, noise suppression is realized by automatically generating a channel-level soft threshold, and a high-dimensional waveform fingerprint feature vector is obtained; mapping the feature vector to a dichotomy probability space, and calculating a confidence probability value of ferromagnetic impurities; and comparing the confidence probability value with a dynamic sensitivity threshold value to carry out impurity judgment and output a detection result. According to the invention, high-precision online detection of ferromagnetic impurities in the lithium iron phosphate electrode material is realized, automatic impurity removal is realized by matching with a ferromagnetic impurity adsorption device, and a complete solution is provided for quality control of a new energy battery material.

The invention discloses a tracking method based on multi-layer Transform attention interactive fusion and scaling intersection-union ratio loss, and belongs to the technical field of target tracking. Aiming at the problems that multi-scale features captured by different levels of attention are neglected in the aspect of feature characterization and a weighting strategy adopted in the aspect of template updating has defects in an existing method, firstly, a multi-level attention fusion mechanism is designed, attention features of different levels are integrated, and the perceptual ability of a model to a target overall structure is enhanced; secondly, a super-resolution reconstruction module based on soft threshold attention is used, and high-fidelity updating of template features is achieved while noise interference is suppressed; and finally, innovatively providing a zoom intersection-union ratio loss function with geometric constraints, and effectively improving the positioning precision of a complex deformation target through dual optimization of a distance penalty term and a shape penalty term.

The application discloses a kind of based on optimization's guiding filter image fusion change detection method and device, method includes: by extreme minimum scale difference operator, pixel value difference value is to the picture of same scene different phase, obtains difference map;Using the way of guiding filter, difference map is fused on global scale, to obtain the optimal difference map;Mean filter is used to phase map I1 and I2, and the base layer difference map corresponding to each difference map is obtained, and the optimized brightness saliency map F1 and F2 are obtained by pca fusion as the image of generating saliency map, according to the principle of maximum saliency to determine weight map;Weight map is regularized for double-scale difference map reconstruction, and the final difference map is obtained;In clustering segmentation stage, by introducing soft threshold function, the final difference map is further processed, to suppress existing abnormal noise.The device includes: processor and memory.

The invention relates to the technical field of image noise processing, and provides an image denoising processing method and system fusing a wavelet frame and a sharpening operator, and the method comprises the steps: carrying out the sharpening processing of an obtained to-be-processed image; wavelet domain transformation is carried out on the sharpened image, and a denoising preprocessing mathematical model is constructed with the purpose of minimizing the weighted sum of a deblurring error item, an image sharpening consistency item and a wavelet domain sparse regular item; solving of the denoising preprocessing mathematical model is decomposed into a plurality of sub-problems, an alternating direction multiplier method is adopted for solving, and a restored image after denoising processing is obtained. According to the method, wavelet multi-scale analysis and sharpening operator edge enhancement are fused, an optimization model for minimizing the deblurring error, sharpening consistency and sparse regularization is constructed, and an alternating direction multiplier method and soft threshold processing are adopted, so that the image denoising effect and the detail retention capability are effectively improved; the problems of detail loss, fuzzy aggravation and low calculation efficiency in the image denoising processing process are solved.