77 results about "Noise map" patented technology

The invention relates to the technical field of noise maps, in particular to a noise map dynamic deduction method and system based on multi-source heterogeneous data fusion, and the method comprises the steps: carrying out the space-time alignment of a multi-cluster low-dimension feature vector in an edge calculation node, and obtaining a multi-cluster low-dimension feature vector; generating a three-dimensional sound field reconstruction parameter of the first target data sequence and a spectrum energy distribution parameter of the second target data sequence; based on the three-dimensional sound field reconstruction parameter and the spectrum energy distribution parameter, dynamically controlling the sensor cluster to sleep and generating a space-time difference parameter; hierarchical encryption is carried out on the time-space difference parameters and the two types of data sequences in the dormancy period, and a basic ciphertext sequence and a feature ciphertext sequence for verification are generated; according to the method, the noise deduction map with space-time continuity is generated, high-precision dynamic deduction and energy efficiency optimization of the noise map are achieved, the evolution rule of noise along with time and space is effectively reflected, the real-time performance and accuracy of noise data are improved, and the actual situation of the noise can be reflected more truly, efficiently and in real time.

The invention relates to the field of environmental noise monitoring, and discloses a noise monitoring point position automatic determination method fused with a noise map. The method comprises the following steps: acquiring a base map and sound source data of a target area, layering the base map according to a preset interval in a vertical direction, and carrying out gridding processing to obtain a three-dimensional grid; performing spatial analysis on the sound source data to obtain a sound source intensity matrix, and mapping the sound source intensity matrix to a three-dimensional grid to obtain a three-dimensional noise map; the base map comprises building structure data; performing three-dimensional path exploration on the three-dimensional noise map based on an ant colony algorithm to obtain a noise hotspot path and a pheromone concentration distribution diagram; and carrying out site selection analysis based on a genetic algorithm on the noise hotspot path and the pheromone concentration distribution diagram to obtain a Pareto optimal monitoring site set, the Pareto optimal monitoring site set being used for indicating site selection of the noise automatic monitoring site. By adopting the method, accurate and efficient automatic noise monitoring site selection in a complex urban environment can be realized, and the urban noise monitoring and treatment level is improved.

The invention discloses a noise exposure evaluation method and system considering an unmanned aerial vehicle, and the method comprises the steps: obtaining urban environment data and unmanned aerial vehicle related data, carrying out the sound source modeling of the noise of the unmanned aerial vehicle, simulating the propagation of the noise of the unmanned aerial vehicle in an urban environment, and generating an unmanned aerial vehicle noise map; combining and calculating the unmanned aerial vehicle noise map and the current city noise map in combination with the current city noise map to obtain a city noise map; identifying crowd distribution conditions in different time periods and mapping the crowd distribution conditions to geographic space to form crowd distribution maps in different time periods, and performing feature identification and analysis on crowds in combination with multi-source data to obtain a crowd distribution map with crowd feature information; and according to the urban noise map and the crowd distribution map, calculating the total amount of regional noise pollution and the per capita noise pollution index, and calculating the noise exposure levels of different crowds. According to the method, the noise exposure evaluation method is perfected, and the space-time accuracy of unmanned aerial vehicle noise exposure evaluation is improved.

The invention discloses a remote sensing image super-resolution reconstruction method and device, a storage medium and equipment, relates to the technical field of computer vision, and can solve the technical problem that the remote sensing image super-resolution reconstruction effect is poor. The method comprises the following steps: acquiring a sample remote sensing image and multi-modal auxiliary data, extracting spectral features, textural features and geometric features of the pre-processed multi-modal auxiliary data, and fusing to generate a condition feature tensor; performing forward diffusion on the sample remote sensing image to generate a noise-containing image sequence; in the process of training the UNet diffusion network by using the noisy image sequence, injecting the conditional feature tensor into preset levels of an encoder and a decoder of the UNet diffusion network, and optimizing network parameters through multi-objective loss to obtain a trained UNet diffusion network; and obtaining a to-be-reconstructed noise map, performing noise prediction on the noise map based on the trained UNet diffusion network to obtain target noise, performing reverse sampling on the noise map, and generating a target reconstructed image through iterative denoising.

The invention provides a marine acoustic environment monitoring system based on a noise map. The marine acoustic environment monitoring system comprises a buoy network module, a data fusion processing module, a noise map generation module, a noise source identification module and an edge response module, the buoy network module serves as a sensing tail end, multi-source information is gathered to the data fusion processing module through the first data bus for standardization processing, and then the noise map generation module is driven through the second data bus to construct a three-dimensional sound field model. The output of the model is transmitted to a noise source identification module through a third data bus for intelligent analysis and traceability; the alarm decision of the noise source identification module is output to the edge response module, and the edge response module dynamically regulates and controls the communication and acquisition strategy of each buoy node through a distributed control circuit to form a complete closed loop. According to the invention, high-precision and automatic monitoring of the marine acoustic environment is realized, the problems of limited monitoring range and single data dimension of the traditional means are solved, and the reliability and emergency capability of the system are improved.

Aspects of the methods, apparatus, non-transitory computer readable medium, and systems include obtaining a noise map and a global image code encoded from an original image and representing semantic content of the original image; generating a plurality of image patches based on the noise map and the global image code using a diffusion model; and combining the plurality of image patches to produce an output image including the semantic content.

The invention relates to an image forgery positioning detection method based on collaborative difference optimization and multi-modal perception, and belongs to the technical field of computer vision and multimedia evidence obtaining. The method comprises the following steps: obtaining an RGB image and carrying out random data enhancement; extracting a noise map by using a noise extractor; the RGB image and the noise graph generate multi-scale features through a pre-trained cross-modal information encoder; the multi-scale features are input into an exception encoder, the exception encoder comprises a multi-layer perceptron, a linear fusion block and a linear prediction block, multi-layer mapping features are obtained through processing of the multi-layer perceptron, and an initial prediction map is output; inputting the original RGB image into a ViT model to extract global features, and inputting the global features and the multilayer mapping features into a cross-scale feature enhancement module for interactive optimization to obtain a refined positioning map; fusing the initial prediction map and the refined positioning map to obtain a final prediction map; and iterative optimization is carried out through the loss function. According to the invention, the detection sensitivity of tampered areas with different sizes can be improved.

The invention discloses a low-illumination underwater image enhancement method based on Zero-shot learning and improved white balance, and the method comprises the following steps: S1, inputting an image into a three-branch full convolutional network, decomposing the image into a reflection image, an illumination image and a noise image through the three-branch full convolutional network, then constructing a loss function composed of reconstruction loss, texture detail loss and noise loss, carrying out iterative fusion on the reflection image by using the loss function, carrying out noise prediction and denoising by using a noise image in the iteration process, and carrying out illumination recovery and iterative convergence on the reflection image by using an illumination image to obtain a fused image; s2, carrying out secondary iterative calculation on the fused image through a gray world algorithm; the image is processed through the three-branch full convolutional network, data set training is not needed, then white balance processing is carried out on the processed image through the secondary gray world algorithm and the perfect reflection algorithm, the problems of color cast, chromatic aberration and contrast of the image are reduced, and meanwhile the method has the advantage of being high in processing efficiency.

Systems and methods are disclosed for image signal processing. For example, methods may include receiving a current image of a sequence of images from an image sensor; combining the current image with a recirculated image to obtain a noise reduced image, where the recirculated image is based on one or more previous images of the sequence of images from the image sensor; determining a noise map for the noise reduced image, where the noise map is determined based on estimates of noise levels for pixels in the current image, a noise map for the recirculated image, and a set of mixing weights; recirculating the noise map with the noise reduced image to combine the noise reduced image with a next image of the sequence of images from the image sensor; and storing, displaying, or transmitting an output image that is based on the noise reduced image.

The invention discloses a colored shaded relief map generation method and system fusing land coverage information. The method comprises the steps of obtaining DEM data and land coverage data of a target area, taking the preprocessed DEM data as DEM condition information, and taking the preprocessed land coverage data as land coverage condition information; the method comprises the following steps: inputting a randomly generated noise image and DEM condition information into a backbone network, inputting the noise image, the DEM condition information and land coverage condition information into a ControlNet fine tuning module to obtain a correction signal, inputting the correction signal into the backbone network to obtain predicted noise, and performing reverse denoising based on the predicted noise to obtain a colored shaded relief map. According to the method, the semantic correctness of the generated color can be ensured, and the generated colored shaded relief image better conforms to the visual effect of a real natural landscape.

The embodiment of the invention relates to an image processing method and device, equipment and a medium. The method comprises the steps that a first image and a first noise map are acquired; wherein the first image comprises a target object, and the transparency of the target object in the first image is different from the transparency of an area outside the edge of the target object; performing fuzzy processing on the first image to obtain a fuzzy image corresponding to the first image; generating a second image corresponding to the first image based on the transparency information of the blurred image and the first noise map; wherein the edge of the target object in the second image is obtained by acting the first noise map on the edge of the target object in the first image based on the transparency information of the blurred image. According to the invention, special effect processing on the object edge in the image can be efficiently and conveniently realized, and the realization efficiency of the burr special effect is effectively improved.

The control device (20) of the present application includes: an image acquisition unit (203) that acquires a radiograph of a subject (F) irradiated with radiation and photographed through the subject (F); a noise map generation unit (204) that derives an evaluation value based on relationship data representing a relationship between a pixel value and an evaluation noise value, from a pixel value of each pixel of the radiograph, and generates a noise map that corresponds to the derived evaluation value and each pixel of the radiograph; and a processing unit (205) that inputs the radiograph and the noise map into a learning completed model (207) constructed by machine learning in advance, and performs image processing for removing noise from the radiograph.

A method and apparatus with noise consideration are provided. The method includes generating, using a noise model, a non-normal noise map corresponding to a noise of an input image, and generating an enhanced image of the input image by implementing an image enhancement model based on the input image and the non-normal noise map, where the noise of the input image follows a non-normal distribution.

The present application relates to a kind of computing method for realizing wide range dynamic noise map system fast update, comprising the following steps: using the data information of system database, based on existing day and night period and city road grade, simplify the structure of traditional road sound level mechanism model;Using big data technology, using hybrid modeling method, regression each grade road sound source (day and night) sound level contribution amount prediction model;Road noise fast calculation method based on sound level contribution amount prediction model is constructed, the fast update of dynamic noise map is realized.Compared with prior art, while ensuring the calculation precision, the operation and update efficiency of noise map system is greatly improved, and the technical constraints of foreign core computing software are broken, the localization of noise map core computing technology is realized, which provides technical support for wide range deep development of noise map.

To provide a data processing method and a data processing system capable of improving accuracy of living body detection in a vehicle.SOLUTION: A data processing method for processing data collected by a millimeter-wave radar, comprising: collecting in-vehicle actual measurement data; and removing noise in the in-vehicle actual measurement data based on a noise map, wherein the noise map is generated based on in-vehicle environment data collected when it is determined that no living body exists in a vehicle.SELECTED DRAWING: Figure 3

The invention provides an image processing method and device and electronic equipment, and the method comprises the steps: obtaining an initial image, inputting the initial image into a pre-trained stable diffusion model, and generating a noise graph through the stable diffusion model; based on a self-attention mechanism, generating disturbance data corresponding to the noise map, and determining target prediction noise through a stable diffusion model and the disturbance data; and performing denoising processing on the noise map based on the target prediction noise to obtain a target image. According to the mode, the structural features of the original image are stored in the image generated by the stable diffusion model, and the image quality of the image generated by the stable diffusion model is improved.

Embodiments of the present application provide a scene special effect processing method and device, electronic equipment and storage medium, and relate to the technical field of image processing, the method comprises: acquiring scene data corresponding to a game scene, the scene data comprising a scene model and map data, the map data comprising a special effect mask map and a noise map; acquiring color control information for adjusting a gradient color, performing gradient processing on the color control information and the texture coordinates of the scene model to obtain a gradient color map corresponding to the scene model; acquiring noise control parameters for the noise map, performing flow control on the noise map according to the noise control parameters to obtain a flow noise map corresponding to the noise map; performing vertex displacement according to the noise map to obtain offset information corresponding to the scene model; and rendering and outputting the scene model according to the gradient color map, the flow noise map and the offset information, so that the game scene is more realistic and the visual effect is more rich.

This invention discloses a training method, system, and device for an underwater image enhancement model, relating to the field of image enhancement technology. The method includes: extracting the natural light field map and detail map of an underwater image; merging the detail map with the natural light field map after Gaussian filtering to obtain a detail degradation map; determining non-offset color channels in the green and blue channels of the natural light field map, performing color compensation on the red and non-offset color channels, and merging the color-compensated natural light field map with the detail map to obtain a color compensation map; reducing the brightness of the underwater image to obtain a low-brightness map; adding Gaussian noise to the underwater image to obtain a noise map; and using the detail degradation map, low-brightness map, and noise map as negative samples, and the reference clear image and color compensation map as positive samples, thereby completing the training of the underwater image enhancement model. This significantly improves the color calibration capability and contrast enhancement performance of the underwater image enhancement model for underwater images.

The invention relates to a noise map updating method and device based on noise monitoring data, and the method comprises the following steps: collecting the noise of a target region through an acoustic sensor, forming an original data packet, carrying out the time-frequency decomposition, and extracting a noise time-frequency feature vector; and carrying out sound source localization based on the feature vector to obtain noise source space distribution data. And calculating a sound ray propagation path, and analyzing a diffraction effect of an obstacle on the path to obtain a diffraction attenuation coefficient. And combining the diffraction attenuation coefficient with the noise source data to carry out sound field superposition, and generating noise distribution superposition data. And finally, incremental updating is performed on the original noise map by using the superposed data, and an updated regional noise map reflecting the real-time noise condition is finally generated, so that the problem of how to efficiently fuse the noise distribution information obtained by real-time analysis with the original noise map to realize low delay is solved.

The present invention provides a video generation method and system based on a large-scale text-based video model. Based on the descriptive text of multiple subjects in the generated video, their corresponding actions, and backgrounds, as well as the bounding boxes of the multiple subjects in the generated video in space, the large-scale text-based video model is used to generate videos for multiple subjects, especially those with different actions. The method first inputs the current step's latent space noise map sequence and text description into a noise prediction network; then extracts a cross-attention map sequence, and uses it to calculate spatial position constraints and grammar-aware contrast constraints; finally, backpropagation is used to calculate the gradient of the attention adjustment constraint with respect to the latent space noise map sequence, and this gradient is used to update the latent space noise map sequence. The method can achieve the effect of aligning the subjects and actions in the generated video while ensuring consistency between the generated video and the descriptive text.

A first aspect of the present disclosure relates to a device for unmixing an image of a sample having fluorescent dyes.SOLUTION: The device is configured to obtain a mixed image of the sample, determine an unmixed image based on the mixed image, determine a noise map image based on the mixed image, determine a signal-to-noise ratio image based on the unmixed image and the noise map image, determine a denoised signal-to-noise ratio image based on the signal-to-noise ratio image, and determine a noise-reduced unmixed image of the sample based on the denoised signal-to-noise ratio image and the noise map image.SELECTED DRAWING: Figure 1

The invention discloses a noise map generation method and device, a storage medium and a program product. Relates to the field of three-dimensional visualization, and the method comprises the steps: obtaining horizontal noise data which comprises the noise data of a plurality of monitoring points in the horizontal direction; according to the method, multiple target acquisition devices are used for noise data acquisition to obtain vertical noise data, and the multiple target acquisition devices comprise noise data acquisition devices deployed at different heights of a target facility; based on the horizontal noise data and the vertical noise data, a three-dimensional noise map is determined through a target network model, and the target network model comprises a neural network model adopting a sound source inversion strategy. Through the method and the device, the problem that the noise map is inaccurate due to the fact that the noise map ignores propagation of noise in the vertical direction in the related technology is solved.

The application discloses a kind of methods for drawing noise map, device and computer readable medium, the method includes the following steps: obtaining detection area, and the detection area is divided into several detection area units;Obtain noise sample, and according to the position of the acquisition equipment of the noise sample, the noise sample is classified into corresponding detection area unit;Based on the noise sample and the position of the detection area unit, obtain the effective noise value of each detection area unit;Based on the effective noise value, render corresponding detection area unit according to predetermined rules, to form noise map.The application effectively solves the technical problems that noise map in the prior art cannot well describe the noise distribution of the whole area, can only show the noise size of detection point, and the visibility is poor, and achieves the beneficial effects of good display effect and good visibility.

The invention discloses an image generation method and device and electronic equipment, and relates to the technical field of image processing, and the method comprises the steps: obtaining a reference image and a detailed text description corresponding to the reference image, and the detailed text description is used for describing the focus of the reference image; performing inversion on the reference image to obtain a noise map containing feature information of the reference image; and matching with the detailed text description, denoising the noise map, and generating a similar image similar to the reference image. The image data of a single scene can be enriched.

The application provides a denoising method and device of an image and electronic equipment. The method comprises the following steps: obtaining image data to be denoised; performing noise component evaluation on the image data to obtain a noise component map corresponding to the image data; performing multi-frequency band decomposition on the noise component map to obtain noise maps of a plurality of frequency bands corresponding to the noise component map; performing different denoising intensity transformations on the noise maps of the plurality of frequency bands based on a plurality of different preset denoising intensities to obtain candidate noise maps of the plurality of different denoising intensities; reconstructing a spatial domain noise map corresponding to the image data based on the candidate noise maps of the plurality of different denoising intensities; and processing the image data according to the spatial domain noise map to obtain a target denoising map corresponding to the image data. Thus, the image data is adjusted in different frequency bands, and the image data is processed in combination with the adjusted spatial domain noise map, so that the target denoising map corresponding to the image data is accurately determined, and the quality of the image is improved.

The present invention discloses a real image denoising method based on noise correction and guided residual estimation. The method mainly includes the following steps: constructing a noise predictor network to predict the noise level map of the noisy image; using the guidance of the noise level map, designing a feature domain residual estimation denoising network module to estimate the denoising residual of the noisy image to obtain a preliminary denoising result; constructing a noise corrector network to correct the noise level map using the denoising result; using a weight sharing method, simultaneously inputting the corrected noise level map and the denoising result of the previous stage into a new residual estimation network to update the denoising residual and obtain a better denoising result; iteratively performing noise correction and denoising residual estimation to ultimately construct a complete denoising network; training the constructed deep network; and inputting the noisy image into the trained deep network to obtain a clean image. The method described in the present invention can effectively remove noise from real images and is an effective image denoising method.

The invention provides a virtual special effect generation method and device, a program product and electronic equipment. The method comprises the steps of obtaining a to-be-rendered target star point diagram and a target noise diagram used for controlling the brightness of each pixel in the target star point diagram, and extracting a pixel value corresponding to each pixel in the target noise diagram; based on the pixel value corresponding to each pixel point in the target noise map and the current rendering moment, performing phase mapping processing to determine corresponding phase data of each pixel point in the target noise map on a preset flicker control function; wherein the preset flicker control function is a periodic waveform function; based on a preset flicker control function, mapping phase data corresponding to each pixel point in the target noise map on the preset flicker control function into light and shade control information; the target star point diagram is rendered based on the brightness control information corresponding to each pixel point in the target noise diagram at the current rendering moment, the target star point flicker effect is obtained, the implementation mode is simple, and computing resource consumption is low.

The present invention discloses a VSP borehole wave suppression method based on improving U-Net with physical constraints. By introducing apparent velocity constraints in the F-K transform domain, physical constraint conditions are constructed. At the same time, combined with the GRU network, the temporal feature extraction and prediction of borehole waves are realized, and the mapping relationship between the noise image and the clean image is learned by the network. By training the network, the details and structure of the noise image are accurately restored, and the difference between the real denoised image and the network output is minimized to achieve the purpose of image denoising. The model retains the effective signal through the residual learning mechanism and simultaneously achieves precise suppression of borehole wave noise. The method of the present invention can accurately identify and effectively suppress borehole waves and their reflected waves in a complex VSP wavefield, significantly improve the signal-to-noise ratio, reduce the residual noise, improve the data processing efficiency while improving the data quality, provide technical support for high-precision seismic imaging under complex geological conditions, and provide a feasible solution for the popularization and application in the actual work area.

The invention discloses a wafer defect detection method fusing feature-level and texture-level abnormal synthesis. The method comprises the following steps: obtaining self-adaptive normal features of a normal wafer sample at a normal branch through a frozen feature extractor and a trainable feature adapter; synthesizing global abnormal features by using Gaussian noise in the feature-level abnormal synthesis branch; synthesizing noise maps with different intensities through texture perception defect synthesis in the texture-level anomaly synthesis branch, synthesizing a local anomaly image through texture superposition, and obtaining local anomaly features through a feature extractor and a feature adapter; the three features of the three branches are jointly input into a neural network feature discriminator for end-to-end training; in the reasoning stage, normal branches are used for processing a test image to obtain adaptive features, the adaptive features are input into a neural network feature discriminator to obtain an abnormal score, whether the current test image has defects or not is judged, and abnormal positioning is conducted on the defects. The method provided by the invention can effectively improve the detection capability of weak defects and meet the real-time requirement of industrial production.

The invention discloses a photovoltaic sound barrier layout and power generation potential evaluation method for an expressway, and relates to the technical field of traffic environmental protection, and the method comprises the steps: obtaining spatial geographic data, constructing a model, generating a road noise map, determining a sound barrier layout road section, and calculating the power generation potential of a photovoltaic sound barrier. According to the method, the high-noise sensitive area is accurately identified by combining GIS spatial data analysis and sound environment quality standards through the layout logic driven by the noise map, so that the layout of the sound barrier is changed from experience decision to data drive, the problem that the layout position is not matched with the actual noise influence in the prior art is solved, and meanwhile, the layout accuracy is improved. According to the method, photovoltaic module design and sound barrier structure parameters are integrally considered, available space is quantified through an effective area calculation formula, energy waste caused by disconnection of photovoltaic and sound barrier design is avoided, and a quantifiable technical path is provided for fusion of traffic and new energy.