13 results about "Noise correction" patented technology

The application relates to the technical field of video data processing, and relates to an audio-video synchronous noise reduction method and system based on AI visual perception, which comprises the following steps: pre-processing audio data to obtain pre-processed audio data; performing time-frequency analysis on the pre-processed audio data to obtain a speech feature set and a background sound feature set; performing noise reduction on video data to obtain primary noise reduction video data, performing visual perception on the primary noise reduction video data to obtain a video feature set; performing time axis correction operation on the speech feature set based on a mouth shape feature according to the video feature set to obtain an updated time axis; performing active noise reduction operation on the progress correction audio data according to the updated time axis and a pre-constructed background sound adaptation degree sequence to obtain noise correction audio data; and performing merging operation on the noise correction audio data and the primary noise reduction video data to obtain synchronous noise reduction audio-video. The application can improve the clarity of images and sounds in a video.

This invention discloses a direct snow depth inversion method based on a spaceborne photon-counting lidar, comprising: extracting the target snow-covered area, removing areas with thick cloud cover and non-target surface point clouds, aligning the surface, and performing noise correction; performing deconvolution to obtain a corrected snow attenuation backscattering profile; setting an initial snow absorption coefficient, inverting the backscattering path length distribution of non-absorbing snow, and calculating the first and second moments of this distribution; based on the first and second moments, simultaneously inverting snow depth, snow albedo, and diffuse scattering coefficient; combining the snow albedo and diffuse scattering coefficient to calculate an updated snow absorption coefficient; if the updated snow absorption coefficient does not meet the set convergence threshold, using the updated snow absorption coefficient as the new initial snow absorption coefficient, and repeating the iteration; if it meets the threshold, outputting the final snow depth. Using this invention, along-track snow depth inversion can be achieved directly without relying on any other data.

A kind of hot infrared non-uniform noise correction method based on motion scene, comprising: recording multiple continuous motion scene images with hot infrared camera;Motion scene image is divided into multiple non-overlapping regions;Estimate the difference of non-uniform noise corresponding to different pixels at a time;Establish noise mathematical model, solve actual non-uniform noise using least square method;Design mask, remove non-uniform noise estimation error part;After removing outliers, non-uniform noise graph is filled, original image is subtracted from noise graph, and non-noise image after correction can be obtained.The application proposes a new non-uniform noise estimation modeling idea, and designs improved median extraction algorithm, which can effectively extract the difference relationship of non-uniform noise between different pixels.All processing processes only need a few original dynamic video frames, avoid the strict requirement of traditional non-uniform correction algorithm to scene and video frame number, so that it can be widely applied to non-uniform correction in different environments.

The invention relates to an image noise reduction method and device, equipment and a storage medium, and relates to the technical field of image noise reduction. The method comprises the following steps: acquiring a to-be-processed first image; determining depth information of the first image; calculating a noise correction parameter based on the depth information; and based on the noise correction parameter, performing noise reduction processing on the first image to obtain a noise-reduced image. The method comprises the steps of obtaining a to-be-processed first image; determining depth information of the first image; calculating a noise correction parameter based on the depth information; and based on the noise correction parameter, performing noise reduction processing on the first image to obtain a noise-reduced image. The noise correction parameter is calculated by using the image depth information, and three-dimensional space information is considered during noise reduction processing, so that noise and real details can be distinguished more accurately, and the image noise reduction effect is improved.

The application discloses a small-area BDS-3 real-time normal high intelligent measurement method and an intelligent measurement system based on deep learning, and comprises the following steps: 1, preprocessing and standardizing space remodeling of sampling point data measured by BDS-3 RTK; 2, stripping a wide-area background field component from original observation; 3, introducing a spatial correlation measure model with a second-order continuous characteristic, introducing an adaptive observation noise correction operator, and constructing a full-field covariance matrix to absorb random observation noise and constrain a fitting process; 4, introducing a super parameter group and constructing a target loss function of deep learning; 5, establishing a back propagation path under a deep learning architecture; performing deep optimization on an evidence function of the constructed model to obtain an optimal super parameter group; 6, performing multi-scale linear superposition on the wide-area background field component and a local refinement correction component to realize reconstruction; and 7, outputting a system result. The model constructed based on the Beidou positioning technology effectively improves the robustness of the model through deep learning.

A temperature noise correction method for CMOS space cameras based on attention mechanisms and LSTM is presented. This method relates to the field of optical remote sensing technology, specifically to the field of temperature noise correction for CMOS space cameras. The core of the method is the use of a multi-level Long Short-Term Memory (LSTM) network with an attention mechanism to explore how temperature changes affect the noise performance of CMOS space cameras under two different operating conditions: dark and bright fields. This deep learning model possesses powerful autonomous learning capabilities, capable of mining and understanding complex noise patterns hidden within massive amounts of data, thereby improving the accuracy of noise identification and calibration. Its key advantage lies in its end-to-end learning approach, automatically learning and establishing a deep, nonlinear relationship between temperature and noise directly from the raw data, without requiring manual intervention for feature extraction or designing complex correction algorithms. This significantly enhances the model's generalization ability and adaptability to unseen data scenarios.

The application provides a correction system and method of a shock absorber simulation model and a road noise correction system. The correction system comprises a shock absorber simulation model, a modal test component and a modal analysis device. The modal analysis device is configured to obtain standardized first modal data and second modal data, perform matching analysis on the second modal data and the first modal data, and correct multiple simulation parameter values of the shock absorber simulation model until a matched shock absorber simulation model is determined when it is determined that the second modal data and the first modal data do not match. A two-stage progressive correction system framework of "test-parameter coupling correction" is proposed, which reduces the complexity of the system. A multi-dimensional parameter synchronous correction mechanism of the shock absorber simulation model is proposed, which integrates three types of parameter collaborative correction of damping nonlinearity, bushing frequency variable stiffness and main structure modal. Performance correction is performed through modal fitting, which improves the modeling accuracy of the shock absorber simulation model.

This application discloses a fixed-pattern noise correction method, device, and storage medium. The method includes: acquiring an image to be corrected; and acquiring a pre-calibrated noise response function and digital gain. The noise response function describes the mapping relationship between pixel values ​​and fixed-pattern noise. The method substitutes the pixel value of the current pixel in the image to be corrected into the noise response function to obtain the fixed-pattern noise corresponding to the current pixel. The fixed-pattern noise and digital gain are combined to obtain a final composite coefficient. Based on the composite coefficient, the pixel value of the current pixel is multiplied once to obtain the corrected value of the current pixel. By simultaneously performing fixed-pattern noise correction and digital gain correction within a single module using the composite coefficient, and directly rounding the product of the composite coefficient and the pixel value, the rounding error is linearly amplified with the digital gain to less than 1, avoiding image stripes under high digital gain and improving the image correction effect.

The application discloses a modeling method of a consciousness core model with signal noise correction, and belongs to the technical field of brain-computer interfaces. The application takes a consciousness core evolution model as a bottom theory, adds an adaptive noise correction term aiming at noise interference of brain signals, and the corrected core evolution relation satisfies M_corrected=M_core+alpha*exp(-beta*sigma^2), wherein Mcore is an original consciousness core model, sigma is noise intensity of input brain signals, and alpha and beta are characteristic calibration parameters corresponding to a signal scene. The application can improve the signal-to-noise ratio of EEG signals by more than 20%, improve consciousness decoding accuracy by more than 15%, support small sample rapid adaptation to different brain signal scenes, is suitable for all brain-computer interfaces, neural medical diagnosis and other fields, and has extremely high commercial value.

This invention discloses a method and apparatus for detecting and guiding irregular stripe noise removal in nighttime light remote sensing images. The method includes: dividing the original image into areas with strong light sources, no light, and areas to be processed, and enhancing the edges to obtain an enhanced image; performing iterative detection of stripe noise based on the enhanced image using frequency domain analysis and multi-constraint Hough transform to obtain a stripe mask; aligning the stripe mask with the binary image of the original image based on morphological operations and then performing inter-band filtering to obtain a noise-free mask; obtaining the noise target region based on the noise-free mask and the original image; performing progressive noise correction on the noise target region based on a dynamic window and an adaptive correction algorithm to obtain an initial result; and performing residual correction based on the initial result and the strong light source region to obtain a destriped image.

The application discloses a kind of astronomical imaging stripe noise correction method and system based on low-frequency directionality feature, first, realize column level brightness equalization through robust statistics, preliminarily eliminate global vertical stripe;Again, through the frequency domain analysis of sliding window, extract low-frequency directionality feature and generate heat map, obtain fixed trapezoidal mask after threshold segmentation and morphological processing;Finally, under the constraint of mask, through star point suppression, frequency domain filtering generates stripe template, and the image is compensated and corrected.This method combines spatial equalization and frequency domain template estimation, and accurately constrains the effective correction area through trapezoidal mask, which not only retains the true star point signal, but also efficiently suppresses residual stripe noise, significantly improves the signal-to-noise ratio of astronomical image and the reliability of subsequent celestial body detection.

A non-uniform noise correction method, an electronic device, and a computer program product. After correcting an output of an infrared imaging device at a current temperature each time, a non-uniform noise fpn_M is learned on the basis of a correction result (101). On the basis of the learned non-uniform noise fpn_M, background data corresponding to a highest temperature value and background data corresponding to a lowest temperature value in a temperature interval where the temperature is located are adjusted (102).

The application relates to the field of fusion positioning, and discloses a weight fusion indoor and outdoor positioning method, equipment and a storage medium. The method comprises the following steps: according to a preset inertial navigation algorithm, performing state prediction processing on a state vector to obtain a state prediction vector and a prediction confidence matrix; according to a preset factor analysis algorithm, generating satellite credibility factors, super-bandwidth credibility factors and IMU credibility factors; according to the satellite credibility factors, obtaining a satellite noise correction matrix, and according to the super-bandwidth credibility factors, obtaining a super-bandwidth noise correction matrix; performing Kalman fusion update processing on the state prediction vector and the prediction confidence matrix to obtain a state update vector and an update confidence matrix; and performing quality labeling on the update positioning data to generate fusion positioning data with quality labeling. In the embodiment of the application, the update positioning data approximates to the theoretical optimal accuracy under the current environment, and the positioning trajectory is seamlessly connected without abrupt breakpoints when crossing different technical coverage areas.