64results about How to "Improve denoising effect" patented technology

The invention relates to the technical field of spectrum analysis, in particular to an intelligent protein quality detection method and system based on spectrum analysis, and the method comprises the following steps: collecting infrared spectrum data of a preset number of protein samples; determining a local fluctuation coefficient of the transmittance of each wavelength; determining a fluctuation coefficient of the infrared spectrum data of each protein sample; screening the infrared spectrum data of all the protein samples based on the fluctuation coefficient to obtain fluctuation data of each suspected impurity; equally dividing each piece of suspected impurity fluctuation data into each fragment; determining an abnormal index of the suspected impurity fluctuation data in each segment; correcting a spatial domain weight and a value domain weight of each filtering window in a bilateral filtering algorithm based on the abnormal index, and denoising infrared spectrum data of the protein sample; and determining a difference coefficient of the infrared spectrum data of each protein sample, and detecting the protein quality. Therefore, the accuracy of protein quality detection is improved.

The invention discloses a generative adversarial network sonar image denoising method and device fused with comparative learning, and the method comprises the steps: constructing a generative network, inputting a noisy image, and carrying out the image denoising processing through a fusion attention mechanism module and a comparative learning module; constructing a discrimination network, inputting a noise-free image without noise and a de-noised image generated by the generation network, and carrying out image discrimination; s3, training an adversarial network composed of the generative network and the discriminant network, and storing final parameters after training is finished; and S4, inputting the noise image into the trained generative network to obtain a denoised image. The fusion attention mechanism module and the contrast learning module can improve the ability of the generative network to understand and process the input image, enhance the ability of the network to capture and retain the image details, and accurately restore the main body features and detail information of the noise image.

The invention relates to the technical field of image processing, and discloses an image denoising method and device, computer equipment and a storage medium, and the method comprises the steps: carrying out the preliminary denoising of a noise-containing image based on a diffusion coefficient function model, and obtaining a preliminary denoised image; and performing second denoising processing on the preliminarily denoised image by using a denoising convolutional neural network, and outputting a denoised image. And according to the feature information of the image, adjusting the threshold parameter and the number of iterations of the diffusion coefficient function so as to adapt to the noise characteristics of different images, and finally outputting an optimized target de-noised image. According to the image denoising method, noise in the image can be effectively removed, and detail and edge information of the image can be accurately reserved. The optimal balance between noise removal and detail protection is realized by adaptively adjusting the threshold parameter and the number of iterations, and the denoising effect is remarkably improved. And the finally generated image is higher in quality, richer in details and clearer in edge.

The invention provides a transverse wave leakage noise suppression method and device based on multi-component pattern recognition, and belongs to the field of seismic data processing, and the method comprises the steps: obtaining a P component, an X component, a Y component and a Z component of original seismic data; respectively rotating the X and Y components to obtain an R component and a T component; performing pattern recognition on the P component and the Z component to obtain a noisep noise model; performing pattern recognition on the P component and the R component to obtain a noisser noise model; performing pattern recognition on the P component and the T component to obtain a noisset noise model; and sequentially subtracting the three models from the Z component to obtain the Z component after transverse wave leakage noise suppression. Through the method provided by the invention, other noises in the Z component can be better extracted, and a better denoising effect is achieved.

The invention provides an adjustable and controllable image denoising method and system based on a cascade neural network, and particularly relates to the technical field of image processing, and the scheme comprises the steps: collecting a plurality of to-be-processed images; and based on preset noise reduction intensity, performing noise reduction processing on each to-be-processed image by using the trained cascade noise reduction network to obtain a noise reduction image corresponding to each to-be-processed image. According to the scheme, the noise reduction intensity of the to-be-processed image can be dynamically adjusted so as to adapt to the images with different noise levels and characteristics, and the personalized requirement of the cascaded noise reduction network for noise reduction processing of the image can be improved.

The invention discloses a seismic data blind denoising method based on adaptive distribution estimation constraint. The method comprises the following steps: S1, constructing an initial noise distribution prediction network model and an initial generation diffusion denoising network model; s2, collecting seismic data with noise, and obtaining an ideal noise distribution prediction network model; s3, inputting a test data set in the noisy seismic data into the ideal noise distribution prediction network model for processing to obtain noise distribution estimation data; s4, processing the noise distribution estimation data through an adaptive noise distribution matching algorithm to obtain a matching time step; s5, inputting the test data set, the noise distribution estimation data and the matching time step into the initial generation diffusion denoising network model to obtain an ideal generation diffusion denoising network model; and S6, inputting the test data set into the ideal generative diffusion denoising network model to obtain the seismic data with the high signal-to-noise ratio.

The invention provides a radar signal denoising method based on an adaptive threshold and an improved threshold function, and relates to the field of signal denoising. The method comprises the following steps: S1, carrying out multi-scale wavelet transformation on an original radar signal to obtain a wavelet coefficient under multiple scales, and solving a logarithmic energy entropy of a wavelet coefficient matrix; s2, segmenting the original radar signal s (n) into a normal radar signal and a noisy radar signal; s3, processing a normal radar signal; s4, processing the noise-containing radar signal; s5, reconstructing the wavelet coefficients processed in the steps S1 to S4; and S6, obtaining a de-noised radar signal d (n). When wavelet denoising is carried out, an original radar signal is divided into a normal radar signal and a noisy radar signal, and denoising processing is carried out on the noisy radar signal, so that resource waste caused by blind denoising processing on all signals is avoided, and denoising is more targeted; based on the adaptive wavelet threshold and the improved semi-soft threshold function, the signal-to-noise ratio of the radar signal is improved, and the denoising effect is improved.

The invention discloses a critical dimension scanning electron microscope image processing method and electronic equipment, and the method comprises the steps: employing a pre-trained image processing model to carry out the processing of an input image, and obtaining an optimized image; the pre-trained image processing model comprises a de-noising branch and an edge enhancement branch, the two branches are both formed based on a U-Net network architecture, the de-noising branch comprises a first encoder module and a first decoder module which are connected in sequence, and the first encoder module and the first decoder module both adopt an SC block module to extract local and global features of an image; the edge enhancement branch comprises an edge information extraction module, and a second encoder module and a second decoder module which are connected in sequence; and the feature fusion module is used for fusing all levels of output of the first encoder module and all levels of output of the second encoder module to serve as all levels of input of the first encoder module. Compared with a traditional image denoising algorithm, the algorithm solves the problem of edge blurring caused by excessive smoothness of the image while denoising.

The invention relates to the technical field of data processing, in particular to a blowing flow adjusting system in the metal powder machining process. The system comprises the steps of collecting flow data, obtaining a mutation factor of each data in a main body flow data sequence, and obtaining a noise factor of each data in the main body flow data sequence according to the mutation factor of each data in the main body flow data sequence; based on the noise factor, adaptively obtaining a judgment threshold value of a Mahalanobis distance of each datum in the main body flow data sequence; according to the judgment threshold value of the Mahalanobis distance of each data in the main body flow data sequence, noise data is obtained for smoothing, the denoising effect is improved, and the influence of abnormal flow data on noise data recognition is avoided.

This application discloses an image processing method and apparatus in the computer vision field of the artificial intelligence field. The image processing method includes: obtaining a channel image corresponding to a to-be-processed raw (raw) image, where the channel image includes at least two first channel images; performing image restoration processing on the first channel image to obtain the restored first channel image; and performing image restoration processing on the to-be-processed raw image based on the restored first channel image to obtain a raw image obtained through joint demosaicing and denoising processing. The technical solution of this application can improve image quality of the raw image obtained through joint demosaicing and denoising processing.

A method for enhancing steel surface images based on a regional autonomous strategy belongs to the field of computer vision and image processing technology. The steps are as follows: 1. Image preprocessing: Acquire the original image of the steel surface and convert the input RGB color image to a grayscale image; 2. Defect region detection: Use the Canny edge detection algorithm to perform edge detection on the grayscale image and extract potential defect boundary information; 3. Regional CLAHE enhancement: Based on the region mask generated in step 2, perform contrast-limited adaptive histogram equalization on the defect region and the background region respectively, and set different enhancement parameters; 4. Median filtering; 5. Weighted image fusion: Perform weighted fusion processing on the enhanced image obtained in step 3 and the filtered image obtained in step 4 to obtain the final output image. This invention can improve the overall quality and defect identifiability of steel surface images through the above method.

This invention discloses a partial discharge (PD) signal denoising method based on image information entropy and multivariate variational mode decomposition (MMD). The method involves converting the noisy signal into a grayscale image, calculating the image information entropy, and optimizing the number of modes K in the MMD by combining Pearson correlation coefficient and execution efficiency to determine the optimal value. The noisy PD signal is then decomposed. The kurtosis value of each intrinsic mode component is calculated, and the nature of the mode component is determined based on a threshold, classifying it as either a dominant PD component or a noise component. A mathematical statistical method using the 3σ criterion is employed to filter out normally distributed white noise. The reconstructed signal is then denoised using an improved wavelet thresholding method to obtain the denoised PD signal. This denoising method accurately reduces noise in noisy PD signals, achieving good noise suppression and restoring the waveform characteristics of the PD signal while maintaining high execution efficiency.

The invention discloses a blood oxygen signal denoising method and system based on adaptive filtering, and the method comprises the steps: synchronously collecting an original blood oxygen signal and a three-axis acceleration signal through a sensor module, and obtaining an original blood oxygen signal sequence and a three-axis acceleration signal sequence which are aligned in time; the three-axis acceleration signals are fused into one-dimensional motion artifact reference noise signals used for representing the limb motion state of the user; the method comprises the following steps: dynamically calculating an adaptive step length factor sequence through a nonlinear mapping function based on multilevel energy analysis of a motion artifact reference noise signal; constructing a time-varying step size adaptive filter, and calculating noise components in the original blood oxygen signal by using the motion artifact reference noise signal and an adaptive step size factor; and subtracting the calculation noise signal from the original blood oxygen signal, and reconstructing to obtain a pure blood oxygen signal without motion artifacts. According to the method, accurate calculation and efficient suppression of motion artifacts can be achieved, and the purification quality and processing efficiency of blood oxygen signals are remarkably improved.

The invention discloses a high-dimensional seismic data denoising method and device. The method comprises the following steps: segmenting a high-dimensional seismic data volume into a plurality of data blocks by using a sliding window; taking the data block with the corresponding de-noised data as a sample to obtain a sample set, and training a Transform model to obtain a de-noising model; and inputting a plurality of data blocks obtained by segmenting the high-dimensional seismic data volume into a denoising model, and obtaining a denoised seismic data volume of the high-dimensional seismic data volume according to an output result of the model. According to the method, intelligent suppression of high-dimensional seismic data noise can be realized by using high-dimensional features of seismic data based on a deep neural network Transform model, and the denoising precision is high.

The invention provides a single photon reconstruction method and device and electronic equipment, and relates to the technical field of single photon imaging, and the method comprises the steps: obtaining a photon distribution observation value, and constructing a photon counting model according to the photon distribution observation value; a negative Poisson maximum likelihood function is used as a loss function, the photon counting model is optimized, and an optimized constraint form is determined; determining a sub-problem of a photon counting model according to the loss function and the optimized constraint form; carrying out secondary iteration solution on the sub-problems to obtain photon distribution under the target ideal condition; according to the method, the depth image and the intensity image are generated according to the photon distribution under the target ideal condition, so that short-distance and long-distance adaptive rapid imaging can be realized, the denoising effect can be effectively improved, and more accurate three-dimensional space distribution of photons can be statistically realized.

The invention provides an image classification model training method and an image classification method. The method comprises the following steps: acquiring an image classification model; wherein the image classification model comprises a frozen visual coding network, a field prompt parameter corresponding to at least one first image generation field and a classification network; in response to monitoring of a newly added second image generation domain, freezing the visual coding network, and adding a learnable domain prompt parameter corresponding to the second image generation domain and a classification network in the image classification model; based on the first sample image and the second sample image, performing incremental training on a domain prompt parameter and a classification network corresponding to a second image generation domain; therefore, full retraining of the backbone network is avoided, generalization and adaptability of the model to the heterogeneous generation algorithm are improved, and efficient and stable detection performance can also be achieved under the scene that the newly generated model only has a small number of samples.

The application discloses a double-parameter HP filtering denoising algorithm and process for infrared spectrum signal processing, wherein upper and lower limits Lamda_1 and Lamda_2 of parameter scale estimation are given in advance, two filtered signals are obtained by performing traditional HP filtering respectively, the two filtered signals are introduced to comprehensively construct a new HP filtering expression, HP filtering is performed under the new comprehensive reconstructed HP filtering expression, and the parameter value when the expression takes a minimum value is taken as an output result in the range of [Lamda_1, Lamda_2] for optimization searching. The local extreme value is searched in the two parameter estimation ranges, and HP filtering is performed under the new expression. Since the application constructs the HP filtering expression of the comprehensive parameter upper and lower limits and performs the optimization processing in the parameter upper and lower limits, the scale estimation of the signal noise can be better, the problem that the uncertain selection of the parameter in the HP filtering denoising process leads to the difficulty in grasping the scale of the high-frequency noise denoising can be solved, and better denoising effect can be obtained.

The invention relates to the technical field of illegal cooking oil identification, in particular to an illegal cooking oil identification method, device and equipment adopting illegal cooking oil CatBoost. The method comprises the following steps: acquiring Fourier infrared spectrograms of different types of illegal cooking oil; preprocessing the collected fourier infrared spectrum of the illegal cooking oil through self-adaptive wavelet transform, and constructing a fourier infrared spectrum set of the illegal cooking oil; extracting swill-cooked dirty oil feature information in the swill-cooked dirty oil Fourier infrared spectrum set by adopting a multi-layer RNN model, and constructing a swill-cooked dirty oil feature information set; a cross entropy loss function is added, an improved sand dune cat swarm algorithm is adopted to optimize parameters in CatBoost, an illegal cooking oil CatBoost model is constructed, and the illegal cooking oil CatBoost model is adopted to classify and recognize the extracted illegal cooking oil feature information set; and outputting the identification result of the illegal cooking oil. The illegal cooking oil CatBoost model is adopted for illegal cooking oil identification, and the accuracy of illegal cooking oil type identification can be improved.

An image processing method, device, equipment and storage medium are provided in the embodiments of the present application. The method comprises: collecting a sequence of exposure images under a target exposure condition, the sequence of exposure images comprising a plurality of exposure images; determining a reference exposure image from the plurality of exposure images; calculating an image difference parameter between a target exposure image and the reference exposure image in the sequence of exposure images, the target exposure image being an exposure image other than the reference exposure image in the sequence of exposure images; determining a plurality of fusion exposure images in the target exposure image according to the image difference parameter; and obtaining a denoising image according to the plurality of fusion images. The image processing method provided in the present scheme reduces the workload of human participation when obtaining the denoising image, and improves the efficiency of obtaining the denoising image. On the other hand, the quality of the denoising image is also effectively improved, the fitting degree of the denoising image and the real scene is improved, and thus the effect of network denoising is improved.

The present disclosure provides a keyword recognition model training method and device. The method comprises: obtaining a training sample; the training sample comprises: a dish name of an initial dish, dish multi-domain information, and a plurality of initial keywords associated with the initial dish; inputting the dish name, the dish multi-domain information, and the plurality of initial keywords into a to-be-trained keyword recognition model; calling the to-be-trained keyword recognition model to process the dish name, the dish multi-domain information, and the plurality of initial keywords, and obtaining a prediction probability of the plurality of initial keywords output by the to-be-trained keyword recognition model with respect to the initial dish; calculating a loss function of the to-be-trained keyword recognition model according to the prediction probability; adjusting model parameters of the to-be-trained keyword recognition model according to the loss function; iteratively performing the steps of obtaining the training sample and adjusting the model parameters of the to-be-trained keyword recognition model according to the loss function until a target keyword recognition model is obtained. The present disclosure can improve the accuracy of keyword prediction.

The application provides a time-frequency feature extraction method and device for an engine vibration signal. The method comprises the following steps: dividing the vibration acceleration signal of the engine into a noise sub-signal and a vibration sub-signal, filtering the noise sub-signal by using a non-local mean method, and recombining the filtered signal and the vibration sub-signal in the time domain, processing the recombined signal by using a TVF-EMD method, further suppressing the noise signal by constructing a plurality of different filters, and obtaining a vibration acceleration signal composed of a plurality of intrinsic mode function sub-signals. The application removes the noise interference information of the vibration signal from two directions of time and frequency domains, strengthens the denoising effect, and realizes accurate extraction of vibration characteristic information.

A quantum data processing system and method are provided. A computer-implemented method may include: storing multiple copies of a quantum state in a quantum memory, wherein the quantum state encodes properties of a target system; loading the multiple copies of the quantum state in the quantum memory into a quantum computer; processing the multiple copies of the quantum state by the quantum computer to obtain a purified quantum state; and measuring the purified quantum state to determine properties of the target system.

The application relates to the technical field of signal processing, and specifically provides a magnetocardiogram denoising method, a storage medium, an electronic device and a product. The method can comprise the following steps: acquiring a training data set containing a noisy magnetocardiogram signal sample and a denoised magnetocardiogram signal sample; processing the noisy magnetocardiogram signal sample by using a multistage gating network model to obtain a predicted denoised magnetocardiogram signal; wherein the multistage gating network model comprises an encoder, a decoder and a feature fusion module; and after the multistage gating network model is optimized by using a signal loss value between the predicted magnetocardiogram signal and the denoised magnetocardiogram signal sample, a target denoising network model is obtained; wherein the target denoising network model is used for denoising a real noisy magnetocardiogram signal to obtain a denoised magnetocardiogram signal. Some embodiments of the application can realize high-quality denoising of signals and have high practicability.

The present application relates to the technical field of photogrammetry, and specifically to a deformation monitoring method, system and device for urban road engineering, which specifically include: calculating the first possibility that each edge pixel is a noise pixel through the similarity between feature points in any two urban road SAR images and the matching degree of edge pixels in their corresponding two edge images; calculating the second possibility that each edge pixel is a noise pixel based on the irregular distribution characteristics of the noise pixels; correcting the grayscale value of the edge pixel based on the above characteristics and the grayscale value of the edge pixel before and after denoising, and enhancing the denoised image based on the corrected grayscale value, thereby improving the denoising effect of the denoising algorithm; based on the enhanced denoised SAR image of the urban road, combining with the surface deformation detection algorithm to perform deformation monitoring of the urban road, thereby improving the accuracy and reliability of the deformation monitoring results of the urban road.

This invention discloses an infrared spatiotemporal noise generation method based on hybrid neural representations. The method includes establishing an infrared denoising dataset containing paired indoor noise-clear video and unpaired outdoor noise-clear video; constructing an infrared spatiotemporal noise model based on hybrid neural representations and its training loss function; and building a spatiotemporal noise generator G and a spatiotemporal discriminator. This invention employs a divide-and-conquer approach, independently exploring the noise synthesis path from both spatial and temporal dimensions. By constructing a hybrid neural representation of noise, it deeply integrates the spatial and temporal embeddings of noise and implicitly models the complex spatiotemporal distribution of infrared noise through recurrent adversarial learning. This comprehensively and deeply characterizes the spatiotemporal properties of noise, providing new ideas and tools for noise modeling and analysis in the field of video processing.

The application discloses a human body breathing information extraction method, device and equipment based on radar and a medium. The method comprises the following steps: mixing and filtering echo signals and transmission signals to obtain original data, wherein the transmission signals are radio frequency signals transmitted by an X-band radar, and the echo signals are signals received by an antenna; processing the original data, and determining whether there is a stationary human body according to the processed data; performing phase correction on the original data with the stationary human body, and extracting phase signals; performing phase unwrapping on the extracted phase signals; extracting a chest wall displacement-time signal from the unwrapped phase signals, and obtaining a breathing frequency of a human body target. Compared with the prior art, the application has a better anti-interference effect in extracting breathing information from echo signals of an X-band radar.