17results about How to "Clear details" patented technology

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 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 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.

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 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 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.