40 results about "Medium resolution" patented technology

The invention provides a silicon wafer image defect detection method. The method comprises the following steps: acquiring a silicon wafer image; inputting the silicon wafer image into a defect detection network, wherein the defect detection network comprises a backbone network, a neck network and a head network; the backbone network processes the silicon wafer image to output high-resolution, medium-resolution and low-resolution features with sequentially reduced resolution and sequentially enhanced semantic information; the neck network performs enhancement processing on the high-resolution features to generate high-resolution enhanced features; the head network detects the high-resolution enhancement feature, the medium-resolution feature and the low-resolution feature and outputs defect information corresponding to the high-resolution enhancement feature, the medium-resolution feature and the low-resolution feature, and the defect information at least comprises defect classification of the silicon wafer image and the confidence coefficient of the defect classification; and determining a defect detection result of the silicon wafer image based on the defect information corresponding to the high-resolution enhancement, the medium-resolution feature and the low-resolution feature respectively. According to the method, defects under various resolutions can be identified, and the detection reliability is improved.

The present application relates to the technical field of remote sensing information extraction and snow monitoring, and particularly relates to a sub-pixel snow mapping method based on medium-resolution remote sensing data, comprising the following steps: obtaining medium-resolution remote sensing images, a digital elevation model, and ground meteorological station snow observation data, and preprocessing the remote sensing images to obtain surface reflectivity; identifying cloud-covered pixels, filling in the cloud-free dataset by temporal and spatial interpolation and ground object spectral similarity matching; selecting snow, vegetation, and water as spectral endmembers and extracting features; constructing a linear mixing model, and using the least square method to retrieve the snow proportion in the pixel to obtain preliminary data; and generating a high temporal and spatial accuracy snow distribution map through meteorological station data verification, digital elevation model terrain correction, and model parameter optimization, which solves the problems of mixed pixels, cloud coverage, terrain errors, and the lack of accuracy verification.

A video decoding apparatus and a method for adaptively setting a resolution are disclosed. According to one embodiment of the present invention, a method for adaptively setting a resolution on a per-picture basis comprises the steps of: decoding maximum resolution information from a bitstream; decoding, from the bitstream, resolution information about a current picture; and setting the resolution of the current picture on the basis of the maximum resolution information or the resolution information, wherein the resolution information has a size less than or equal to that of the maximum resolution information.

The present disclosure discloses an extraction method of raft culture area based on multi-temporal optical remote sensing images, including: constructing a raft culture marker sample library including culture types such as fish, shellfish, and algae; optimizing the deep learning model of the UNet network by using ASPP (Atrous Spatial Pyramid Pooling) and the shape constraint module; using the deep learning model to extract a corresponding multi-temporal raft culture area by using the multi-temporal optical remote sensing images with medium resolution in the target area; combining prior knowledge, fusing the extraction results of the raft culture area to obtain a final extraction results of the raft culture area.

The invention discloses a remote sensing residential area extraction method and system based on multi-source data fusion semantic segmentation, and particularly relates to the field of remote sensing image processing and land utilization mapping. A multi-source fusion automatic labeling strategy is adopted, and a rural residential area pixel-level training label is automatically generated by using an impervious surface product, an open source map interest point and a night light learning area-brightness threshold value; the method comprises the following steps: constructing an SELPFormer lightweight Transform segmentation model, introducing Lite-PPM, SCSE and ELA feature enhancement modules and a lightweight decoder on the basis of SegFormer, and outputting a rural residential spot pixel level extraction result for regional scale rural residential spot mapping.

The invention discloses a short-time wind speed prediction method and short-time wind speed prediction equipment based on satellite image information, belongs to the technical field of meteorological prediction, and realizes high-precision and dynamic prediction of short-time wind speed by combining a satellite remote sensing image and an image feature tracking technology and setting a distance threshold adaptive to an image resolution. According to the method, the motion trend of an extreme point in a wind speed field is extracted and tracked through the SIFT algorithm, the evolution rule of the extreme point is established through regression analysis, and the complete wind speed field is reconstructed in combination with optimal interpolation, so that the limitations of insufficient resolution and obvious hysteresis of traditional numerical weather forecast in short-time local wind speed forecast are effectively overcome, and the wind speed forecast accuracy is improved. The method significantly improves the timeliness and spatial details of forecasting, and is especially suitable for offshore wind plants and other application scenes sensitive to short-time wind speed changes.

The application discloses a lightweight multi-branch semantic segmentation method and system for complex road scenes, belongs to the field of computer vision, and comprises the following steps: taking an input image as a starting point, extracting multi-scale feature representations through a lightweight backbone network; constructing global semantic requirements on low-resolution features by an anchor semantic kernel ASK branch; performing pixel-level semantic prediction on medium-resolution features by an execution semantic kernel FSK branch; detecting boundaries and abnormal areas on high-resolution features by a boundary and outlier detection FOD branch; gating and fusing ASK and FSK features through a Qi-GFM gating fusion module; performing semantic negotiation in combination with FOD output through a Qi-Negotiation semantic negotiation module; and outputting an optimized semantic segmentation result. The application can effectively improve the prediction stability and robustness of a model in complex boundary areas and semantic uncertain areas.

This invention discloses an intelligent detection method for ecological violations in water source protection areas based on multi-source heterogeneous remote sensing spatiotemporal collaborative perception. The method includes: acquiring time-series data of medium-resolution multispectral satellite imagery and high-resolution imagery data; calculating spectral indices and establishing ecological base fingerprints based on the medium-resolution time-series data, and extracting suspected change hotspots through time-series anomaly detection; employing a cross-scale guided Transformer to perform cross-attention fusion using high-resolution texture features as queries and medium-resolution spectral features as keys and values; using a semantic segmentation network with distance field constraints to perform pixel-level fine segmentation of the violations; and vectorizing the segmentation results and overlaying them with the protection area zoning for analysis, outputting compliance assessment results. This invention employs a cascade mechanism of macroscopic scanning-targeted acquisition-fine analysis, significantly reducing monitoring costs, achieving effective identification of camouflaged targets, supporting high-frequency, routine integrated land-water monitoring, and meeting the operational needs of hierarchical management of water source protection areas.

A computer-implemented method for generating synthetic mammographic images using a modified diffusion model has as novelty three training phases in which modified diffusion is performed, namely: in a second stage 200 of local context generations, over an image representing a third channel, in a step 208 of adding noise to the image patch, and in a step 209 of training the neural network to remove the noise, and also modified diffusion is performed in a third phase 300 of generating full-resolution patches, in a step 308 of denoising the image patch, and in a step 310 of training a neural network to remove noise. Also, the method includes working with patches in the image in the second phase 200 in steps 204-210 and in a third phase 300 in steps 304-311. The method includes training in phase 100 on an image representing a single channel, a global context, with steps 101-106, then training over the images representing the first, second, and third channel: in phase 200 with steps 201-210 and in phase 300 with steps 301-311 and finally phase 400 of generating a full-resolution mammogram image. Phase 400 includes: subphase 401 where noise is programmatically generated and in step 403 the neural network removes it and provides an output image at a resolution of 256x256; subphase 405 with step 406 where the input image from the first phase is loaded and steps 407 are then performed-411; step 412 of integrating patches with local context in a way that ensures smooth transitions during integration, which is an innovative step; then subphase 413 where, in step 414, the medium-resolution image obtained by integrating the patches from the second phase 200 is loaded, after which steps 415-420 are performed. then step 421 of integrating the patches in the image follows, and finally, the full-resolution output image is obtained in step 422. The patch integration in step 412 is innovative and ensures smooth transitions in the image.

The invention provides a distributed SAR satellite follow-up bunching InSAR mode realization method and system. The method comprises the steps of obtaining a formation configuration and a working wave position; predicting the overhead time of the target area and the ground station; sAR working parameters are obtained; generating a task instruction and uploading the task instruction to a satellite; controlling the satellite to perform bunching mode imaging and record data when the satellite passes through the top target area; downloading the data when the ground station passes the top; and finally carrying out ground imaging and interference processing to generate a surveying and mapping product. The technical problems that a traditional InSAR system is insufficient in resolution and low in precision in small-area region surveying and mapping are solved.

The embodiment of the application provides a kind of river and lake shore zone remote sensing monitoring method and product, the river and lake shore zone remote sensing monitoring method includes: obtaining historical medium resolution remote sensing image data and historical high resolution remote sensing image data;From the historical medium resolution remote sensing image data, the end-member spectrum of typical ground object is extracted;The end-member spectrum is corrected according to the high resolution remote sensing image data and the interference of shadow or cloud layer is eliminated, and the target end-member spectrum matrix is obtained;According to the target end-member spectrum matrix, pixel unmixing is carried out on the medium resolution remote sensing image data to be decomposed, and the pixel unmixing result is obtained;According to the pixel unmixing result and water flooding frequency, water level amplitude area is determined;The shore zone monitoring range is determined according to the water level amplitude area;The image data corresponding to the shore zone monitoring range is input into wavelet transform U-Net deep convolutional neural network model, and the shore zone land use classification result is obtained;According to the shore zone land use classification result, the ecological shore zone ratio result of each river section is quantified.

The present application relates to the technical field of remote sensing image intelligent processing, and particularly relates to a multi-scale progressive land surface temperature fusion downscaling method and device, wherein the method comprises: preprocessing land surface temperature images in a multi-scale image library; constructing a multi-scale training data set containing land surface temperature and at least one auxiliary parameter; constructing a land surface temperature downscaling network based on multi-parameter fusion, and constructing a domain transformation network based on heterogeneous high-frequency information guidance; training the land surface temperature downscaling network and the domain transformation network based on a progressive double-network joint training strategy; fine-tuning a downscaling model meeting a preset low-to-medium resolution condition; and performing low-to-medium-to-high progressive downscaling on a low-resolution land surface temperature image to be processed, so as to generate a multi-scale progressive land surface temperature meeting a target high-resolution condition. The present application fully considers the downscaling difference problem under different resolutions, realizes the physical property retention of a land surface thermal field, and improves the processing efficiency of large-area remote sensing data.

The application belongs to the technical field of artificial intelligence and satellite remote sensing, and particularly relates to a cross-scale spatio-temporal fusion ground feature classification method based on a double-branch architecture, which comprises the following steps: preprocessing a remote sensing image; obtaining high-resolution optical images and multi-period medium-resolution time-series images, generating time-series data and labeling through processing, and cutting a data set; constructing a double-branch network, wherein the network comprises a spatial and temporal feature extraction branch and a fusion and decoding module; training and optimizing a model by using a weight optimizer and a loss function with dynamically adjusted class weights; post-processing a classification result, and outputting vector data. The method realizes the fusion of heterogeneous information, solves problems such as the same object with different spectra, improves the feature utilization rate and the recognition accuracy of a minority class, is convenient to design in an end-to-end mode, has strong generalization ability, and is fast in reasoning.

The invention belongs to the field of infrared measurement, and particularly relates to a precision optical adjustment and boresight device and a use method, targets and point targets with multiple medium resolutions are generated through an infrared multi-target simulator, and under the driving of a motor, a guide rail can be driven to precisely move in a 2 pi space and keep a precise spatial position, so that the precision optical adjustment and boresight device is obtained. Clear images can be formed in all directions of the detector, the sliding table can accurately feed back the position relation of each infrared point target in the space, the boundary position of the target in imaging of the detector can be found by continuously moving the point target, and the spatial orientation and the pitch axis of the distributed infrared equipment are calibrated. Meanwhile, the infrared multi-target simulation can simulate images with various resolutions, the sliding table 6 moves in the space 2pi range, imaging can be carried out at all positions of the detector, the imaging quality of target plates with various resolutions can be observed, and the resolution performance, distortion and imaging uniformity of the distributed infrared equipment detection system can be accurately calibrated.

The application discloses a resolution plate, a resolution test system and a method for measuring resolution capability, wherein the resolution plate comprises a plate body, at least one slit group is arranged on the plate body, the slit group comprises two parallel slits which are arranged on the plate body along a path, the slit has a width in a direction perpendicular to the path, the slit has a uniform width along the path, the two slits have a consistent shape and width, the two slits are arranged at intervals, and the interval of the two slits is equal to the width of the slit. By arranging at least one slit group with two slits on the plate body made of tungsten material, high-energy X rays can form at least one image with two strips on the imaging plate through the slit group, and the corresponding curve can be obtained through simple measurement and multiple tests, so that the resolution capability of the ray point source under the corresponding magnification ratio and the slit width can be intuitively and accurately reflected.

The application relates to a satellite image-based cross-domain ground object classification fine-tuning sample automatic selection method, which comprises the following steps: obtaining an initial interpretation model, to-be-interpreted images and medium-resolution ground surface cover classification results; obtaining an interpretation result and an interpretation category probability distribution; constructing a high-value sample selection framework; selecting candidate images from the to-be-interpreted images as a to-be-labeled sample set according to a sample screening strategy, artificially labeling the candidate samples to obtain a fine-tuning sample set; fine-tuning the initial interpretation model according to the fine-tuning sample set and model evaluation, and obtaining an optimal interpretation model; and obtaining a final fine-tuning sample set after a screening criterion is met. On the basis of reasonably utilizing an existing model, the application constructs diversified, representative and differentiated fine-tuning samples through ground surface cover prior knowledge, fully utilizes the prior knowledge, reduces the input of manpower and material resources, and can better complete a current satellite remote sensing image ground object classification task.

The invention discloses an image rain removal method based on phase perception and dynamic optimization, and the method comprises the steps: constructing a phase perception dynamic rain removal network, converting a rain-containing image into a rain-containing image of a large-resolution flow, a medium-resolution flow and a small-resolution flow, and carrying out the parallel processing of the rain-containing image; channel attention operation is carried out on space, frequency and biological visual features to generate multi-modal fusion features, the multi-modal fusion features are combined with rain-containing image input features of various resolutions to generate decoding features, details are reconstructed through INR after fusion, preliminary rain removal features are output, and raindrop complexity is evaluated to dynamically optimize and output rain removal images; when the phase perception dynamic rain removal network is trained, network parameters are optimized through a composite loss function; and inputting a to-be-processed rain-containing image into the trained phase perception dynamic rain removal network for multi-scale feature processing and dynamic optimization refinement, and outputting a clear image after rain removal. According to the invention, multi-modal feature fusion, dynamic rain condition adaptation and high-precision detail recovery are realized.

The invention discloses an electronic component quality online monitoring and evaluation method, system and device. Establishing a historical monitoring image set, and obtaining specific values of resolution data, edge curve data, gray data and quality grade data; dividing the historical monitoring image set into a training set and a verification set, inputting the training set and the verification set into an initial model for training, optimizing parameters in the initial model, and constructing a target evaluation model; obtaining a to-be-evaluated monitoring image, and marking the to-be-evaluated monitoring image with a high-resolution label, a medium-resolution label and a low-resolution label according to the resolution data of the to-be-evaluated monitoring image; calculating quality grade data of the to-be-evaluated monitoring image marked with the high-resolution label; calculating quality grade data of the to-be-evaluated monitoring image marked with the resolution label in the mark; and calculating quality grade data of the to-be-evaluated monitoring image of the low-resolution label.

Systems are configured for performing super-resolution processing for applications by a plurality of different hardware processing units that include hardware processing units that are native to the applications and hardware processing units that are non-native to the applications. Interfaces in the system generate different sets of instructions that are submitted to the different processing units with synchronization objects that synchronize the execution of the instructions.

The application provides a super-structured optical fiber for generating a spindle beam and an optical acoustic endoscopy imaging system, the super-structured optical fiber comprising a fiber body and a super-structured optical structure arranged at an exit section of the fiber body, the super-structured optical structure comprising an optical expander and a super-structured structure array arranged at the end of the optical expander; the super-structured structure array is periodically arranged by super-structured units in a sub-wavelength scale, and the super-structured unit is in the form of a regular quadrangular prism; by clear phase modulation and parameter optimization, the super-structured optical structure is integrated at the end face of the fiber body, the emission light wave front is accurately regulated, a long focal depth spindle beam is obtained under the condition of a limited aperture and a compact structure, and the problem that the focal depth is limited in optical acoustic endoscopy imaging is effectively relieved; based on the combination of Bessel beams and the lateral spot size, the application maintains a high lateral resolution ability while extending the focal depth, avoids the defect that the resolution is significantly reduced in the traditional zooming scheme, and improves the imaging consistency and the signal-to-noise ratio.

The invention relates to the technical field of remote sensing information extraction and accumulated snow monitoring, in particular to a sub-pixel accumulated snow mapping method based on medium-resolution remote sensing data, which comprises the following steps: acquiring a medium-resolution remote sensing image, a digital elevation model and accumulated snow observation data of a ground meteorological station, and preprocessing the remote sensing image to obtain surface reflectance; identifying cloud coverage pixels, and generating a cloud-free data set through space-time interpolation and ground feature spectrum similarity matching and filling; selecting accumulated snow, vegetation and a water body as spectrum end members and extracting features; constructing a linear hybrid model, and inverting the proportion of accumulated snow in the pixels by using a least square method to obtain preliminary data; through meteorological station data verification, digital elevation model terrain correction and model parameter optimization, a high-temporal-spatial-precision accumulated snow distribution map is generated, and the problems of pixel mixing, cloud coverage, terrain errors and precision verification deficiency are solved.

Microscopy methods and devices that use structured illumination are described. An example method includes providing multiple digital raw images of a sample that are recorded sequentially a microscope and obtained by illuminating the sample in different phases with periodically structured illumination light. The method includes, determining, using a structured illumination microscopy (SIM) reconstruction method, an intermediate imaging result for the sample with an increased resolution compared to the raw images, where the resolution is higher than the diffraction-limited resolving power of the microscope. In addition, the above method reduces or suppressed image artifacts.

The present application relates to the technical field of reservoir geological modeling, and is a three-dimensional reservoir attribute model resolution reconstruction method and device based on a perception-guided generative adversarial network, an electronic device, and a storage medium. The method comprises the following steps: obtaining three groups of two-dimensional low-resolution multi-attribute slices; inputting the three groups of two-dimensional low-resolution multi-attribute slices into a resolution reconstruction model to obtain three groups of two-dimensional high-resolution multi-attribute slices, wherein the resolution reconstruction model is obtained by deep learning of a Geo-Real ESRGAN model through a plurality of samples; and decoding and fusing the three groups of two-dimensional high-resolution multi-attribute slices to obtain a high-resolution three-dimensional reservoir attribute model. The two-dimensional high-resolution multi-attribute slices predicted and output by the resolution reconstruction model are more superior in structure restoration and attribute consistency, so that the accuracy of the reconstructed high-resolution three-dimensional reservoir attribute model is higher, and reliable technical support can be provided for fine characterization and engineering design of unconventional reservoirs.

A video decoding apparatus and a method for adaptively setting a resolution are disclosed. According to one embodiment of the present invention, a method for adaptively setting a resolution on a per-picture basis comprises the steps of: decoding maximum resolution information from a bitstream; decoding, from the bitstream, resolution information about a current picture; and setting the resolution of the current picture on the basis of the maximum resolution information or the resolution information, wherein the resolution information has a size less than or equal to that of the maximum resolution information.

This application discloses a method and device for monitoring a near-infrared band stable pixel simulated reference source. The method includes the following steps: performing a two-layer screening of primary data from a medium-resolution spectral imager, involving day / night determination and stable pixel site matching, to obtain target observation data; selecting atmospheric reanalysis data as the background field, completing spatiotemporal matching with the target observation data, and performing interpolation preprocessing; inputting the preprocessed atmospheric reanalysis data into a radiative transfer model, and performing synchronous reflectance simulation based on the screened stable pixel sites to obtain simulated reflectance data; calculating the average relative deviation between the observed reflectance and the simulated reflectance, and performing multi-dimensional statistical monitoring and accuracy evaluation on the average relative deviation.

[Problem] To provide an information processing device, an imaging device, and a program that enable compatibility between low power consumption and high detection accuracy. [Solution] An information processing device according to the present disclosure comprises: a motion detection unit that performs motion extraction and motion determination of a subject on the basis of a plurality of first pixel signals generated by a plurality of first pixels; a distance detection unit that performs distance determination to the subject based on a plurality of second pixel signals generated by a plurality of second pixels; and a mode control unit that converts a moving body detection mode in which low-resolution image information is acquired to a feature extraction mode in which medium-resolution image information is acquired or an imaging mode in which high-resolution image information is acquired, based on a result of the motion determination and a result of the distance determination.

The application discloses a lightweight SAR image ship target detection method and device, a server and a medium, and belongs to the technical field of target detection. The method comprises the following steps: obtaining shallow features, middle features and deep features by subjecting primary features to an adaptive down-sampling module, a C3k2_MSEE module, an SPPF module and an ECA attention module; fusing the shallow features, the middle features and the deep features through up-sampling, a cross-channel feature fusion module and the C3k2_MSEE module to obtain high-resolution fusion features, middle-resolution fusion features and low-resolution fusion features; and inputting the high-resolution fusion features, the middle-resolution fusion features and the low-resolution fusion features into a detection head to obtain small-scale ship detection results, medium-scale ship detection results and large-scale ship detection results. Through multi-scale feature enhancement and fusion and lightweight design, high-precision detection of ship targets in images on resource-limited devices is realized.

The application discloses a kind of southern single double-cropping rice identification method based on multi-source data and phenological characteristics, download satellite image of different remote sensing satellites, and obtain time-series image dataset by processing;S-G filter LSWI image, S-G filter NDVI image and HANTS filter NDVI image are obtained based on time-series image dataset;Obtain the cultivated land distribution layer in study area;Rice potential distribution layer is obtained based on S-G filter LSWI image and S-G filter NDVI image;Single double-cropping rice identification is completed based on HANTS filter NDVI image.The application cooperates multiple medium-resolution remote sensing satellites, by S-G filtering and HANTS filtering two filtering methods, the potential information characteristics of time-series image dataset are fully tapped, and the classification accuracy and identification effect of single double-cropping rice are improved.

The invention discloses a lightweight multi-branch semantic segmentation method and system for a complex road scene, and belongs to the field of computer vision, and the method comprises the steps: taking an input image as a starting point, and extracting multi-scale feature representation through a lightweight backbone network; an anchoring semantic core ASK branch constructs a global semantic demand on the low-resolution feature; executing a semantic core FSK branch to execute pixel-level semantic prediction on the medium-resolution feature; a boundary and outlier detection FOD branch detects a boundary and an abnormal region on the high-resolution feature; performing gating fusion on the ASK and FSK features through a Qi-GFM gating fusion module; semantic negotiation is carried out in combination with FOD output through a Qi-Networking semantic negotiation module; and outputting an optimized semantic segmentation result. The method can effectively improve the prediction stability and robustness of the model in a complex boundary region and a semantic uncertain region.