169 results about "Mixed noise" patented technology

The invention relates to the technical field of audio noise reduction, in particular to a hybrid noise reduction method and system for a Bluetooth headset. The method comprises the following steps: obtaining multi-dimensional environment noise data based on an environment noise sensor, and carrying out noise component feature analysis to generate environment noise multi-frequency component features; performing intelligent noise reduction intensity and self-adaptive filtering noise reduction suppression on the multi-frequency component characteristics of the environmental noise so as to generate an external environmental noise reduction suppression strategy; a micro audio signal is sent based on an ear canal form sensor, and audio signal feedback information is collected; performing three-dimensional ear canal structure modeling according to the audio signal feedback information so as to construct a three-dimensional ear canal structure model of the user; and obtaining a noise reduction feedback log of the wearer, and carrying out personalized noise reduction demand analysis to obtain the noise reduction demand of the user in the current scene. According to the invention, through comprehensive and finer mixed noise suppression, the audio quality and stability of the Bluetooth earphone are improved.

The invention discloses a transformer bushing high-frequency partial discharge signal on-line monitoring method and system, and relates to the technical field of electrical equipment insulation state monitoring, and the method comprises the steps: synchronously collecting signals based on a high-frequency current sensor and an ultrahigh-frequency sensor; performing mixed noise reduction and feature extraction according to the collected signals; and carrying out discharge type identification on the processed data by constructing a lightweight convolutional neural network, and carrying out early warning and positioning in combination with a dynamic alarm threshold. The method can significantly improve the signal-to-noise ratio and enhance the weak discharge signal detection capability by combining the dual-mode sensor with a hybrid noise reduction strategy of wavelet packet decomposition and adaptive filtering, adopts the lightweight convolutional neural network, supports edge equipment to implement reasoning, realizes intelligent identification of the discharge type, and improves the detection accuracy of the weak discharge signal. The insulation state of the transformer bushing is monitored online in real time, off-line dependence is reduced, the service life of equipment is prolonged, and the intelligent level of operation and maintenance of a power system is improved.

The invention discloses a noise reduction method and system for a high-performance TWS Bluetooth audio chip, and relates to the technical field of acoustoelectric integration, and the method comprises the steps: collecting an environment noise signal, synchronously obtaining a Bluetooth radio frequency interference signal, converting the Bluetooth radio frequency interference signal into a digital signal through a 24-bit analog-to-digital converter, and outputting a multi-modal noise feature matrix; performing noise separation and reverse sound wave generation on the multi-modal noise characteristic matrix by using a pre-trained lightweight CycleGAN model, and outputting a reverse sound wave signal; monitoring auditory meatus tightness parameters in real time, dynamically adjusting phase offset and frequency response balance parameters of the mixed noise reduction signals, and outputting optimized noise reduction signals adaptive to the current wearing state; and performing parameter calibration on the optimized noise reduction signal through anechoic chamber scene simulation and laser interferometer phase detection to generate a noise reduction control instruction set. According to the invention, the noise interference problem of the TWS earphone in a complex environment is effectively solved.

The invention discloses a fabric defect detection method based on image reconstruction, which is characterized in that a U-shaped self-coding model with a space self-attention module, a channel self-attention module and jump connection is used, and only a defect-free normal image is used during model training; according to the method, a diffusion process of gradually adding noise and an anti-diffusion process of gradually removing noise are carried out, so that the model learns to gradually remove mixed noise in an image, a defect image with noise is reconstructed into a noiseless and defect-free image by the model in a test stage, and the steps of acceleration, visualization of a defect area and the like are realized by using a step length resetting method. Compared with the prior art, the method provided by the invention has the advantages that model training can be carried out only by using a normal image, the detection effect can be improved, a defect area with pixel-level fineness can be output, meanwhile, the denoising frequency during detection is reduced, the real-time performance of defect detection is improved, the problem of too long reconstruction time caused by repeated denoising of the model is relieved, and the reconstruction efficiency is improved. Good application prospects are realized.

The invention discloses an acoustic automatic recognition system and method for birds in a wetland environment, relates to the technical field of ecological monitoring and acoustic signal recognition, and provides a four-step flow of multi-source noise modeling, blind source separation and noise reduction, multi-label recognition and multi-modal fusion feedback for a wetland complex acoustic environment. The method comprises the following steps: 1, constructing an acoustic dictionary and mixing noise and twitter by using a contribution coefficient; 2, performing multi-sound-source unmixing by using a self-supervision method, and outputting a quasi-pure orbit; step three, identifying and labeling overlapped twitter by combining multi-label classification and dynamic spectrum enhancement time enhancement factors and frequency enhancement factors; and 4, judging the noise type through the microphone array and external environment parameter fusion, updating the noise template and the birdsong template to form closed-loop iteration, maintaining high recognition precision in a noisy and multi-species chorus scene, and providing efficient support for wetland ecological monitoring and protection decision.

The invention discloses a line path planning scheme generation method and system based on deep reinforcement learning, and the method comprises the steps: firstly constructing a grid map and an evaluation index system of a planning region, determining the index weight based on a Critic method to construct a grid comprehensive cost evaluation model, and then constructing a path planning model with the minimum total cost as a target, the method comprises the following steps of: firstly, extracting an optimal power transmission line planning path, describing the optimal power transmission line planning path as a Markov decision process, then introducing mixed noise and an epsilon-greedy strategy into a depth deterministic strategy gradient algorithm, improving a priority experience playback mechanism to obtain an improved depth deterministic strategy gradient algorithm, and finally training an intelligent agent based on the algorithm and outputting an optimal power transmission line planning path. According to the method, mixed noise is added, the exploring ability of the intelligent agent is improved, a greedy strategy is introduced to accelerate the learning efficiency of the initial stage of the algorithm and reduce the exploring frequency so as to reduce unnecessary trial and error, and finally, an instant reward value is fused into a priority experience playback mechanism to help the intelligent agent to accurately distinguish the importance of different experiences and accelerate the learning efficiency of the intelligent agent.

The invention provides an active suppression method and system for partial discharge ultrahigh frequency signal background noise. The method comprises the following steps: determining the arrangement positions of an ultrahigh frequency sensor and a noise sensor on the outer wall of a GIS (Gas Insulated Switchgear); collecting a partial discharge signal of the mixed noise through an ultrahigh frequency sensor; after the minimum collection period of the noise signals is determined, the noise signals are collected through a noise sensor; inputting the collected noise signal into a spectrum analyzer for spectrum analysis; extracting an obvious narrow-band noise frequency band in a frequency domain, and determining the frequency band and phase jitter of the narrow-band noise frequency band; and active suppression of the narrow-band noise is carried out on the frequency band with the narrow-band noise through the suppression formula of the narrow-band noise interference. According to the invention, the technical problem that the accuracy and the reliability of interference suppression are restricted due to poor narrow-band interference applicability of partial discharge ultrahigh-frequency signals of GIS equipment in a field environment is solved.

The invention discloses a method for suppressing magnetotelluric mixed noise in a shallow water area. The method comprises the following steps: reading a noisy ocean MT time sequence; setting phase space reconstruction parameters; constructing a phase-space vector to obtain a noisy data matrix; noise priori information is obtained through noise pre-estimation; constructing an original noisy data matrix after noise whitening; performing singular value decomposition to obtain a feature vector and a feature value matrix of a data covariance matrix after noise whitening; selecting a low-order principal component after noise adjustment principal component transformation to reconstruct a denoised data matrix; recovering the denoised data matrix into a time sequence; carrying out Fourier transform to obtain an ocean MT magnetic field component amplitude spectrum, and combining to obtain an ocean MT four-component amplitude spectrum; detecting the frequency point of the pulse in the four-component amplitude spectrum; and setting all amplitudes corresponding to pulse frequency points in the four-component amplitude spectrum to be 0, and performing inverse Fourier transform to obtain a processed four-component time sequence. According to the invention, the ocean magnetotelluric signals can be better processed, and the data quality is improved.

The invention discloses a spatial structure priori feature-based hyperspectral sparse unmixing method. The method comprises the following steps of S1, establishing a spectral linear mixing model; s2, based on a spectral linear mixing model, sparse unmixing is introduced into hyperspectral image unmixing, and an initial objective function is constructed; s3, obtaining a coarse-scale hyperspectral image by using the SLIC, and unmixing the coarse-scale hyperspectral image to obtain abundance estimation under the coarse scale for constructing a superpixel space weighting factor S; s4, constructing a gradient weighting coefficient total variation (GWCTV) regularization item; and S5, obtaining a sparse unmixing model based on spatial structure prior characteristics, and solving an abundance matrix A by adopting an alternating direction multiplier method. According to the method, space structure information can be better mined, pixels with the same or similar spectral information can be more accurately classified into a homogeneous region, and interference of complex mixed noise is effectively dealt with.

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 relates to the technical field of respiratory lung sound recognition, in particular to a respiratory lung sound auxiliary recognition method and system for clinical nursing. The invention provides a respiratory lung sound auxiliary identification method and system for clinical nursing, solves the technical problems of data scarcity, complex noise interference and insufficient cross-device generalization ability in lung sound signals through generative data enhancement and cross-modal migration, and combines self-supervised comparative learning and causal feature discovery to identify the respiratory lung sound. Multi-source signal feature fusion and noise robustness improvement are realized, a real-time noise environment is dynamically adapted, the limitation that a traditional method depends on annotation data and feature extraction is easily influenced by hybrid noise is broken through, a full-link closed-loop system from synthetic data generation, cross-modal alignment to causal-driven decision is constructed, and multi-source signal feature fusion and noise robustness improvement are realized. And the accuracy and clinical applicability of clinical lung sound analysis are effectively improved.

The invention relates to a noise filtering and vibration suppression method and system for a scanning micromirror, and the method comprises the steps: constructing a motion characteristic model of the scanning micromirror without vibration, and obtaining the model output of the scanning micromirror without vibration; modeling the acquired multi-source fusion noise and unknown vibration interference of the scanning micromirror into a Gaussian mixture model and vibration impact interference; gaussian mixed noise parameters are determined, and a single filtering updating part is converted into interactive multi-model fusion updating; on the basis of interactive multi-model fusion updating, constructing an updated part into a vibration suppression target function in a nonlinear regression form, and seeking to minimize the vibration suppression target function; and based on the filtering result of each part and the posterior covariance matrix, calculating an updated likelihood function and a model probability, and obtaining a fused filtering value, thereby realizing adaptive filtering and vibration suppression of the scanning micromirror. Compared with the prior art, the method has the advantages of effectively realizing adaptive filtering and vibration suppression without depending on an additional vibration measurement sensor and the like.

The invention discloses a de-noising system based on adaptive beam forming and ICA (independent component analysis). The de-noising system comprises a multi-channel sensor array, an adaptive beam forming unit, an independent component analysis unit and a cooperative control module, the multi-channel sensor array is used for collecting a mixed signal containing a target signal and noise; the adaptive beam forming unit suppresses noise in an interference direction through spatial filtering and outputs a preliminary enhanced signal; the independent component analysis unit is used for receiving the preliminary enhanced signal and separating residual independent noise components through a blind source separation algorithm; the cooperative control module dynamically adjusts a beam forming weight and an ICA separation parameter, and balances voice distortion and noise suppression effects; through cooperation of beam direction nulling and blind source separation, reverberation, multipath scattering and Gaussian / non-Gaussian mixed noise are effectively processed, the system is suitable for scene coverage speech enhancement, medical signal processing and radar anti-interference, and the denoising effect of the system is more excellent comprehensively.

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 satellite remote sensing image random stripe noise suppression method and system based on low-rank tensor approximation. The method specifically comprises the following steps: establishing a random stripe mixed noise image model; then converting a core task of satellite image denoising into an unconstrained optimization problem, and determining a target function; lRA operation is carried out on a noisy data matrix / tensor for random noise in a satellite image to realize random noise suppression, and stripe noise separation is realized through direction selective regularization by using a stripe noise removal model based on one-way total variation UTV for stripe noise in the satellite image; a UTV-LRTA mixed denoising model is established, and the mixed stripe noise in the satellite image is effectively suppressed through combination of low-tube-rank tensor constraint and one-way total variation regularization. According to the method, the random stripe mixed noise is effectively suppressed, the image quality is improved, and the visual effect is improved, so that the accuracy of information identification and analysis is improved.

The invention discloses a transform domain high-order tensor video denoising method for 3D machine vision inspection and measurement, and the method comprises the steps: carrying out the modeling of collected multi-channel multi-frame video data into a fourth-order tensor, carrying out the normalization processing of the fourth-order tensor, decomposing the fourth-order tensor into a third-order depth tensor and a fourth-order RGB tensor, and maintaining the image space, time and channel information. For different tensors, a transform domain high-order tensor denoising model is constructed, and mixed noise is removed by using a tube rank and constraint conditions, including suppression of low rank and sparse noise. An optimization problem is converted through an augmented Lagrange expression, and alternating optimization is carried out by adopting an alternating direction multiplier method frame until convergence is carried out to obtain denoised tensor data. And finally, splicing the de-noised depth and RGB information on a channel dimension through tensor inverse remodeling, and recovering a complete RGBD video data structure. According to the method, mixed noise is effectively removed, the data definition and reducibility are improved, high-quality input data are provided for a 3D machine vision system, and the detection and measurement precision and robustness 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 crack detection model capability enhancement method combined with a noisy learning strategy, and relates to the technical field of automatic crack detection, and the method comprises the steps: carrying out the standardization processing of an input crack image, and obtaining a preprocessing image; generating a weight parameter based on the preprocessed image, and fusing the random noise data and the conditional noise data according to the weight parameter to obtain mixed noise training data; combining the preprocessed image and the mixed noise training data to form a training sample; performing multi-scale feature extraction and model training on the training sample by adopting a loss function to obtain a crack detection model; and performing parameter updating and online learning on the crack detection model based on the newly added training sample. According to the method, the adaptive fusion of random noise and conditional noise is realized by adopting an automatic noise injection strategy of dynamic weight adjustment, and the adaptability of the model to different types of cracks and complex environments is 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 provides a colposcope collection image comparison method and system based on artificial intelligence, and the method comprises the steps: carrying out the mixed noise reduction and multi-scale enhancement processing of a 4K original image collected by a colposcope, and obtaining an enhanced noise reduction image; adaptively adjusting the scaling of the enhanced and denoised image based on image feature distribution, and focusing columnar epithelium and squamous epithelium cell features by using an attention convolutional network to obtain a feature focusing image; cross-modal feature fusion segmentation processing is carried out on the feature focusing image, columnar epithelium and squamous epithelium regions are segmented, and segmented region images are obtained; image convolution and topological feature extraction are carried out on the segmented region image, an image adjacent image is constructed, and an image feature vector is obtained; and performing fuzzy similarity measurement and comparison decision processing on the image feature vector to obtain a colposcope acquisition image comparison result. According to the invention, the defect that the consistency and the accuracy are difficult to guarantee when the colposcope collection image is identified manually at present is overcome.

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.