25results about How to "Clear details" patented technology

The invention discloses a photoelectric wafer defect detection method, and belongs to the technical field of semiconductors. The detection method comprises the steps that the surface of a photoelectric wafer is covered with a quantum dot precursor solution, annealing is carried out, after a quantum dot film is formed, irradiation is carried out under ultraviolet light, a displayed fluorescence signal can mark defects on the surface of the photoelectric wafer, and the purpose of detecting the defects of the photoelectric wafer is achieved. The quantum dots used in the invention have excellent fluorescence efficiency and stability, and can provide a high-contrast marking effect in an SEM image. Therefore, the detection of micron-scale and nano-scale defects is more accurate, and the details are clearer. A traditional fluorescence labeling solution is limited in infiltration capacity in nanoscale defects and can only cover the surface. The quantum dots can penetrate into the defect to form a mark with the depth ratio as high as 1: 3. The depth marking capability is helpful for comprehensively knowing the internal structure of the defect and the defect extension condition. In addition, the quantum dots have excellent adaptability and multifunctionality, and show good stability and coupling effect under different types of wafer conditions.

The invention discloses a calibration method of a polarization focal plane imaging system and an imaging system, relates to the field of photoelectric imaging and remote sensing detection imaging, and solves the problems of low correction efficiency and difficulty in correcting a system with a changed polarization state in the conventional polarization error correction technology. According to the calibration method, a plurality of coding patterns and corresponding polarization modulation matrixes are obtained based on the collected uniform light field and serve as calibration results. The polarization error correction method comprises the steps of obtaining an observation intensity vector based on a target scene; a corresponding polarization modulation matrix is obtained based on the coding pattern according to a coding polarization modulation matrix lookup table, and then a down-sampling Stokes vector after polarization error correction is obtained; and completing the correction of the polarization error through an imaging reconstruction method. A polarization focal plane imaging system comprises an imaging lens, a DMD digital micromirror array coding module, a focal plane DoFP polarization detector and an imaging reconstruction module. The method is suitable for the fields of remote sensing detection, target identification, environment monitoring and the like.

The invention discloses a frequency modulation and wavelet sub-band guided double-domain collaborative Transformer X-ray image denoising method, which comprises the following steps of: acquiring a noise-containing digital ray original image and a corresponding clear reference image, and constructing a data set after preprocessing the noise-containing digital ray original image and the corresponding clear reference image; constructing a network model of a double-domain collaborative coding-decoding architecture; performing 3 * 3 deep convolution on an input image to extract shallow layer features; in the encoding stage, ETB and AFMB are alternately stacked to represent local and global information, a WB-LKED module is embedded to strengthen fine-grained features, and WDB executes down-sampling and transmits high-frequency features to a decoding end; in the decoding stage, the WUB recovers the resolution through double-path up-sampling, integrates the same-scale features of an encoder, enhances details by using high-frequency features, splices the features, then carries out ETB and AFMB refining, obtains output features through 3 * 3 deep convolution, and combines a global residual error connection optimization result; and training the model by using the data set, inputting a to-be-denoised image, and outputting a final result. According to the method, the problems of weak complex noise interference resistance, poor detail retention effect and limited CNR improvement can be solved.

This invention discloses a differential VGA signal decoding and display device, relating to the display field. It includes a signal input module, an ESD protection module, a differential receiving module, a synchronization separation module, an image decoding driver module, a display module, an audio processing module, and a control module. These modules are interconnected and work together to decode, process, and display differential VGA signals. This differential VGA signal decoding and display device solves the problems of short transmission distance, weak anti-interference capability, limited resolution, and high power consumption associated with traditional VGA interfaces. It also achieves full compatibility with traditional VGA devices, integrating audio processing, local playback, intelligent signal enhancement, and remote monitoring and management functions. It achieves reliable conversion from analog to differential signals, supports high-definition display, and has a transmission distance of up to 50 meters, while maintaining full compatibility with traditional VGA devices. This technology is particularly suitable for professional fields such as industrial automation and medical imaging, meeting the diverse display and intelligent operation and maintenance needs of professional scenarios.

The invention discloses a rapid magnetic resonance imaging reconstruction and segmentation method and system, a medium and a product, and relates to the technical field of image processing. The method comprises the following steps: decomposing original three-dimensional magnetic resonance data into a plurality of independent two-dimensional magnetic resonance data slices along a slice sequence; image reconstruction is carried out on the two-dimensional magnetic resonance data slices through a pre-trained forward generator in the frequency domain Schrodinger bridge diffusion model so as to generate a two-dimensional magnetic resonance image, and the forward generator integrates a Fourier convolution module and is used for carrying out frequency domain transformation and feature enhancement processing on feature mapping in the image reconstruction process; inputting each two-dimensional magnetic resonance image into a pre-trained segmentation model in sequence, performing segmentation of a target region and a preset dangerous organ, and generating a corresponding two-dimensional segmentation mask; all the two-dimensional segmentation masks are rearranged and stacked according to the slicing sequence, and a three-dimensional segmentation result is generated through three-dimensional fusion operation. By implementing the technical scheme, magnetic resonance imaging reconstruction and segmentation can be quickly and accurately carried out.

The present invention belongs to the field of medical image processing, and provides a method, system and medium for processing medical endoscopic images based on GPU. The present invention utilizes the powerful parallel processing capability of GPU to significantly improve the speed and accuracy of endoscopic image acquisition, preprocessing, feature point detection, image correction and pathological feature recognition. The image quality is enhanced by executing GPU-accelerated filtering algorithms and histogram equalization, while the GPU-optimized SIFT algorithm effectively improves the accuracy of feature point matching. The estimation of the affine transformation model and the transformation correction of the image further correct the distortion and ensure the accuracy of the image data. Finally, the pre-trained deep learning model is used to implement fast convolutional neural network forward propagation on the GPU, identify pathological features in real time, and assist clinical diagnosis. The efficiency and accuracy of clinical diagnosis are greatly improved, providing doctors with instant diagnostic assistance, thereby improving the quality of medical services.

The application discloses a kind of thermosensitive microcapsule dispersions and its preparation method and application, it is related to microcapsule technical field.The application provides a kind of thermosensitive microcapsule dispersion, the thermosensitive microcapsule dispersion D90≤0.75 μm, thermosensitive microcapsule dispersion D100≤1.35 μm;The thermosensitive microcapsule dispersion includes thermosensitive microcapsule, the thermosensitive microcapsule includes capsule wall and capsule core, the weight percentage of the capsule wall and capsule core is (45-66) :(34-55);The capsule wall is polyurethane-polyurea compound, and the capsule core is chromogenic dye precursor and solvent.The application innovatively generates polyurethane-polyurea compound capsule wall by twice polymerization reaction, by capsule wall material polyisocyanate and polyether polyhydric alcohol primary polymerization reaction, polyisocyanate and polyamine secondary polymerization reaction, by the effective regulation of product species and generation amount, effectively control the temperature sensitivity of thermosensitive microcapsule, color range.

The invention belongs to the field of space target imaging, and discloses a space target three-dimensional reconstruction method for inhibiting dark color interference through combination of brightness and transparency, which is used for solving the problems of poor imaging quality and inaccurate pose estimation of a space target. According to the method, multiple requirements of improving the quality of an input image, adjusting a pose and training a three-dimensional model are jointly completed, and low-quality imaging of an incomplete imaging system is preprocessed based on lucky imaging; and then, dense feature points are extracted by using a pre-training network based on no detector, estimation of a three-dimensional initial point cloud and an initial pose of a camera is carried out, and pose joint adjustment is carried out by combining RANSAC and Bundle AdJustlement. During training, an MLP-based optimizer is used for calculating the deviation of pose adjustment, meanwhile, training of parameters of three-dimensional Gaussian primitives is carried out, and after various physical constraints are added, a one-stop assembly line can be implemented. According to the method, the good basic shape and the sharp edge of the model are kept, the good anti-noise capability is achieved, and meanwhile the real-time performance and the robustness are achieved.

The utility model relates to the technical field of transformer substations, and discloses a transformer substation inspection device which comprises a support, land walking assemblies are arranged on the outer walls of the two sides of the support through chain wheels, an inspection body is fixedly assembled on the top of the support, and one end of a machine arm is fixedly assembled on the outer wall of the inspection body. And an upper propeller is fixedly mounted at the other end of the vehicle arm. A clamping groove is connected with a driving wheel in a clamped mode, so that a clamping block can make contact with the ground, common obstacles can be easily handled, the device can be suitable for various terrains, a damping spring and a damping rod are arranged, and the damping performance of the device can be high by connecting a mounting clamping block with a driven wheel; when an obstacle difficult to surmount is encountered, the upper propeller and the lower propeller can be connected through the mounting piece and then combined to generate lift force to drive the device to lift off, so that the device can achieve the ground and air dual-purpose effect, and the practicability is higher.

The application discloses a ceramic tile cloth powder control method and system, and relates to the technical field of ceramic tile production. The method comprises the following steps: acquiring ceramic tile pattern data required for current production; converting the ceramic tile pattern data into bitmap data, and performing pixel optimization on the bitmap data according to a preset pixel optimization rule to obtain bitmap data of a target resolution; performing partition processing on pixel points in the bitmap data to obtain pixel region data after partition; pairing the pixel region data with a preset cloth powder control database to obtain cloth powder control data of a discharging mechanism; and controlling the discharging mechanism to perform automatic discharging work according to the cloth powder control data, so that powder fills corresponding cloth regions, thereby forming a ceramic tile powder layer with a required pattern. The application can adapt to the production requirement of rapid switching of ceramic tile patterns, has high production efficiency, and the texture and color effect of the produced ceramic tiles are good.

According to the multi-input hierarchical infrared image super-resolution reconstruction method based on the collaborative CNN and Transform, through ingenious combination of the advantages of the CNN and the Transform, clearer detail information and edge structures can be reconstructed in a reconstruction task of an infrared image super-resolution image, the contrast ratio and definition are improved to a certain degree, and the reconstruction efficiency of the infrared image super-resolution image is improved. Unnecessary artifacts and noise are reduced, a clearer appearance is kept while high-precision textures and structures are recovered, and a good visual effect is achieved.

The invention belongs to the technical field of image processing, and particularly discloses an image processing method and system based on histogram matching. Comprising the following steps: separating an original image into RGB channels, and respectively calculating histograms of the channels; calculating the cumulative distribution probability density of each channel according to the histogram of each channel; selecting the channel with the maximum energy as a standard channel, matching histograms of other channels to a standard histogram, obtaining a mapping curve of each channel, and obtaining a gain curve of each channel according to the mapping curve of each channel; calculating the gain of each channel according to the sigma principle and the gain curve of each channel; and respectively adjusting the RGB channels by using the calculated gains to obtain an image after white balance processing. According to the method, the linearity of the image is kept while histogram matching is carried out, so that effective white balance adjustment of the image is realized, and excessive change of source image information is avoided.

The application discloses a kind of based on multi-level supervision attention mechanism super network and random erasing data enhancement's occluded face image super resolution method, comprising: face region cutting, random erasing face content and size adjustment are carried out to public face dataset, construct low-resolution occluded face / high-resolution unoccluded face image pair dataset, and multiple data enhancements are carried out to training set;Multi-level supervision attention mechanism face image super resolution network based on face structure priori guide is constructed;Multi-level supervision attention mechanism face image super resolution network based on face structure priori guide is trained on training set;Low-resolution occluded face image in test set is carried out super resolution.This application overcomes the existing face super resolution method in processing accompanying occlusion input low-resolution face image when result image structure distortion, the problem of detail accompanying artificial artifact, can reconstruct structure complete, accurate, high-resolution face image with high fidelity.

The present application belongs to the field of scene reconstruction, and particularly relates to a 3DGS rendering method and system for HDR large scenes, comprising: acquiring a multi-exposure image sequence and camera pose parameters of a target scene; reconstructing a high dynamic range light field to obtain light field configuration data comprising a dynamic range compression curve, indoor and outdoor area segmentation masks, and a material reflectivity model; iteratively optimizing spherical harmonic coefficients of a Gaussian sphere in a three-dimensional Gaussian splatting model according to the light field configuration data until the area proportion difference between overexposed and underexposed regions is less than a first threshold value; real-time rendering and collecting pixel-level brightness distribution, calculating the average brightness ratio of overexposed and underexposed regions, and if the ratio is less than a second threshold value, directly outputting, otherwise adjusting the spherical harmonic coefficients and tone mapping operators and re-rendering until the requirements are met. Through the light field guided spherical harmonic coefficient optimization and real-time dynamic adjustment, the present application effectively solves the overexposure, underexposure and light and shadow abrupt change problems in HDR scenes, and significantly improves the visual realism and consistency of large scene virtual roaming.

This invention discloses an underwater image enhancement method based on cascaded correction and multi-scale fusion, mainly including the following steps: (1) establishing an underwater image dataset to form a first dataset; (2) constructing an underwater image enhancement model based on cascaded correction and multi-scale fusion; (3) inputting the first dataset into the model for image enhancement; (4) evaluating the enhanced underwater image to meet the index requirements, thus obtaining the final underwater image enhancement model based on cascaded correction and multi-scale fusion. Compared with the prior art, the underwater image enhancement method disclosed in this invention significantly enhances the clarity and color fidelity of underwater images, effectively improving the visual quality of underwater images.

The application discloses a mahjong machine with a control lighting lamp, and belongs to the technical field of mahjong machines, and has the technical scheme as follows: the mahjong machine comprises a mahjong machine main body, irradiation lamp strips are rotationally arranged on four sides of the mahjong machine main body, placing areas for placing mahjong hands are arranged on the four sides of the mahjong machine main body, the light beams of the irradiation lamp strips are obliquely shot from bottom to top to the mahjong hands in the corresponding placing areas, a controller is arranged on the mahjong machine main body, human body sensing modules are arranged on the four sides of the mahjong machine main body, the human body sensing modules are electrically connected with the controller, the controller is electrically connected with the irradiation lamp strips, when the human body sensing modules sense a human body, the controller controls the corresponding irradiation lamp strip to be turned on, and the four sides of the mahjong machine main body are provided with adjusting knobs for adjusting the brightness of the corresponding irradiation lamp strips, the mahjong machine forms effective light zones on the mahjong hands through the irradiation lamp strips, the recognition of the mahjong hands by users is enhanced, and the use experience is improved.

The invention discloses a user authentication method and system for an online education platform, and solves the problem that the existing method adopts frame-by-frame analysis of a face image, can only simply perform independent processing on each frame, and is sensitive to interference information, and the method comprises the following steps: performing normalization processing on target image information to obtain normalized target image information; a multi-target recognition model is constructed in combination with a multi-layer space displacement sensing mechanism and a cascade attitude regression model, the multi-target recognition model carries out noise reduction processing on target image information and then segments and extracts feature points, and the confidence degree of the target image information and the face standard image is calculated; according to the method, the attention of a background information dispersion model can be overcome through normalization processing of the target image information, background interference factors can be rapidly removed, the multi-target recognition model approximates a real face shape and a feature point position through a clustering analysis mode, correction, noise reduction and optimization are carried out on a result of a previous level, and the accuracy of face recognition is improved. Therefore, the positioning precision of the feature points is improved.

The visual enhancement method of a real scene that simultaneously removes haze and improves resolution described in the present invention first uses a rank-one prior to obtain an initial defogging picture and a scattered light map, and then combines the rank-one prior defogging and a super-resolution deep neural network prior for processing various image degradation problems under the framework of a variational model, and uses a semi-quadratic splitting (HQS) optimization algorithm to alternately iteratively estimate the scattered light map for removing haze and seek a high-quality visual enhancement image through a deep super-resolution network. The method described in the present invention can also handle other complex scenes and improve the clarity and recognizability of the acquired image.

The application relates to a wide-azimuth seismic data imaging domain phase consistency correction method, and relates to the field of seismic exploration. The method solves the problem of phase inconsistency caused by factors such as surface unevenness and different azimuth incidence angles in the existing seismic imaging process. The method comprises the following steps: S1: obtaining wide-azimuth prestack three-dimensional seismic data, and pre-processing the prestack three-dimensional seismic data; S2: performing radial domain multi-azimuth data subset efficient division on the pre-processed prestack three-dimensional seismic data, and obtaining a radial domain data subset; S3: based on the pre-processed prestack three-dimensional seismic data, constructing a reference gather through migration stacking; S4: based on the radial domain data subset, constructing a radial domain imaging gather; S5: based on the constructed reference gather and the radial domain imaging gather, obtaining a phase difference; and performing imaging domain phase consistency correction on the radial domain data subset. The method can effectively improve the phase consistency of seismic data, improve the resolution of seismic data and the imaging quality of underground structures.

This invention discloses an impression composition, an impression material, an impression, and a method for preparing the same, belonging to the field of photocuring technology. The impression composition comprises the following components in weight percentages: 10%-25% oligomer, 1%-5% acrylate monomer, 0.05%-1% photoinitiator, 0.5%-2% dispersant, and 62%-85% silica filler; wherein the oligomer includes polyurethane acrylate with a functionality of 3-9. The impression composition is prepared by uniformly mixing the above raw materials and further molded into a photocurable impression material. Before curing, this impression intermediate exhibits excellent flexural strength (i.e., tear resistance), detail reproduction, and storage stability. In use, the impression material can be directly cured using photocuring. The curing time is suitable and controllable, the degree of curing is high, and it exhibits good dimensional stability during curing. The prepared impression has high hardness.

The invention discloses a down feather recognition method and system based on deep learning, and the method comprises the steps: S1, obtaining an original down feather image, carrying out the preprocessing of the obtained original down feather image, and obtaining a processed down feather feature map; s2, the obtained feature map is input into an improved residual network WT-ResNet model, the WT-ResNet model at least comprises a WT-ResNet layer, and the WT-ResNet layer processes the input feature map and then outputs a final feature map containing deep semantic information; and S3, inputting the output final feature map into a classification activation layer, obtaining a probability value of down feather image classification through activation function processing, and judging whether the down feather image is fresh down feather or recycled down feather based on the probability value.

The invention provides an underwater image enhancement method based on brightness channel replacement and dual-scale fusion, and belongs to the technical field of underwater optical imaging and digital image processing. According to the method, underwater scene information is acquired through multi-band imaging, and a pre-trained contrast dominant band is utilized to search a network recommendation detail optimal band; then synthesizing a primary color image based on a three-primary-color principle, and performing color recovery by adopting a U-Shape Transform deep learning network; converting the color recovery image into a Lab color space, and replacing a brightness channel with a gray level image of an optimal detail wave band to realize fusion of colors and details; and finally, a high-quality underwater enhanced image is output through scattering removal enhancement processing based on dark channel prior. Compared with an existing underwater image enhancement method with a single feature, the algorithm has better effectiveness and robustness, and the visual quality and availability of the underwater image can be remarkably improved.

The invention discloses a distribution network unmanned aerial vehicle inspection image automatic acquisition method based on lightweight AI, and the method comprises the steps: 1, enabling an unmanned aerial vehicle to start to operate, and obtaining an inspection image when the unmanned aerial vehicle arrives at an inspection shooting point; 2, positioning target power grid equipment in the inspection image based on a target detection and recognition algorithm, and outputting characteristic parameters of the target power grid equipment; 3, calculating the position deviation between the target equipment and the center of the inspection image according to the characteristic parameters of the target power grid equipment, and adjusting the attitude of the holder and the position of the unmanned aerial vehicle according to the position deviation to enable the target equipment to be located in the center of the inspection image; and step 4, obtaining illumination features through an ambient light sensor, adjusting camera parameter combination through an automatic dimming algorithm based on the ambient illumination features, and then executing a photographing action to obtain a target power grid equipment image. According to the invention, distribution network inspection key component image acquisition can be completed without manual intervention, and the inspection image one-time shooting qualified rate and operation efficiency are significantly improved.

The invention discloses a second-hand commodity digital receiving and selling operation system, which comprises an information acquisition module, a data analysis module and a commodity transaction module, and is characterized in that the information acquisition module comprises a client information acquisition module and a client classification module; the data analysis module comprises a commodity determination module, a gray processing module, a defect identification module, a commodity pricing module and a management module, and the commodity transaction module comprises an information interaction module, a recovery application module, an aggregation pricing module, a commodity sales module and a display module. The invention further discloses an operation method of the second-hand commodity digital receiving and selling operation system. According to the system and the method for improving second-hand commodity recycling and selling, accurate pricing of second-hand commodities is realized, the management efficiency, the visualization degree and the customer experience of recycling and selling are improved, and the operation cost of an enterprise is reduced.

The invention provides an autonomous zooming and target tracking system based on reinforcement learning. The system comprises an image acquisition module; and a processing control module and an execution mechanism module. The invention also provides an autonomous zooming and target tracking method based on reinforcement learning. The method comprises the following steps: collecting current original image data of a current target scene; identifying current original image data by using a YOLOv11 target detection model and a multi-target priority ranking algorithm, selecting a current tracking target, obtaining a current tracking target prediction frame, and converting the current tracking target prediction frame into a current 512-dimensional feature vector; obtaining an optimal discrete decision and continuous control instruction based on the current state information by using an optimized hierarchical reinforcement learning model and a multi-target weighted reward function; and converting the pulse signal to drive the image acquisition module to move so as to realize zooming. According to the invention, the success rate is 0.812, the accuracy is 0.857, the tracking loss rate is lower than 0.5%, and the focusing error does not exceed + / -0.05 diopter.