39results about How to "Eliminate artifacts" patented technology

The invention discloses a lattice structure light illumination microscope based on three-beam interference and an implementation method. By controlling the hexagonal lattice pattern formed by the light beam modulation system, the diffracted light after phase modulation passes through the space mask, and only three + 1-level or three-1-level diffracted light is reserved; the fast axis directions of all areas of the polarization modulator are different, the polarization state of each diffracted light beam is independently adjusted, the polarization state of each diffracted light beam is radial polarization, a pattern irradiated on a sample to generate interference is a hexagonal lattice pattern, and the amplitude of high-frequency information is enhanced; a self-adaptive compensation filter is adopted for the frequency spectrum of the SIM image, so that artifacts caused by a serious background in the SIM reconstructed image are effectively reduced; while isotropic resolution expansion is ensured, redundancy of acquired information is reduced, and phototoxicity is reduced, so that imaging time of a sample is prolonged.

The invention relates to the technical field of medical image processing, and particularly provides an SPECT bone scanning image restoration method, electronic equipment and a storage medium, and the method comprises the steps: S1, carrying out the skeleton segmentation of an SPECT bone scanning image, so as to obtain a plurality of skeleton regions; s2, segmenting the SPECT bone scanning image in each bone region into a first foreground and a first background, and then obtaining first foreground feature value information according to the first foreground; s3, according to the first foreground feature value information and a preset feature value of the corresponding skeleton region, analyzing whether there is an artifact in the skeleton region; s4, when artifacts exist in the bone area, acquiring a supplementary scanning image, and repairing the SPECT bone scanning image based on the supplementary scanning image; according to the method, the imaging quality of the SPECT bone scanning image and the diagnosis accuracy of the bone metastasis cancer can be effectively improved.

The invention relates to the technical field of image processing, in particular to a compression imaging method and system based on a Swin Transform network, and the method comprises the steps: carrying out the sampling processing of training data through employing a CC Hadamard matrix, obtaining a coding image, training a neural network based on the Swin Transform through employing the coding image, and obtaining an optimal compression reconstruction model; in the system, the optimal compression reconstruction model is loaded into a processing center, multiple low-resolution images collected by a detector are preprocessed to obtain coded images, and then the optimal compression reconstruction model is utilized to obtain reconstructed images. Compared with a common sampling matrix, the system and the method provided by the invention have the advantages that the reconstruction effect after sampling of the CC Hadamard matrix is better, and the reconstruction quality of the image is effectively improved.

The invention discloses an earth surface exposed geologic body reality sense three-dimensional reconstruction method based on a neural radiation field. The method comprises the steps that an earth surface geologic body image data set is acquired; performing feature matching on the image data set by using a feature matching strategy based on a nearest neighbor algorithm and a LightGlue neural network to generate feature point matching information and a feature description file; sparse three-dimensional point cloud data and camera pose information are obtained based on incremental SfM; and obtaining depth information of an image data set based on sparse three-dimensional point cloud data and camera pose information, and establishing a geologic body realistic three-dimensional reconstruction model by using a neural radiation field optimization method fusing the depth information and Gaussian position coding. According to the method, the feature matching precision and the camera pose calculation accuracy are improved, the texture detail expression ability of a geological structure can be improved, the reconstruction quality of a surface geologic body model is enhanced, and geological information support can be provided for the fields of geological research and engineering application.

The invention provides a cultural relic spectral image splicing method and system based on grid homography transformation, and the method comprises the steps: introducing an orthographic color panoramic image as a reference image, and taking a space plane where the reference image is located as a reference coordinate system for the projection transformation of a spectral image; carrying out SIFT feature registration on the reference image and the spectral image to obtain SIFT feature matching points, and screening and supplementing the SIFT feature matching points by using LBP textures to obtain accurate feature matching point pairs; grid homography transformation is adopted, the offset position of a target grid pixel point of the spectral image is calculated with the distance between the feature matching points as the weight, and projection transformation is conducted on the spectral image according to position offset information to achieve positioning; and selecting a reference sub-lens spectral image to sequentially carry out histogram matching on the other sub-lens spectral images to realize equalization, and fusing the equalized sub-lens cultural relic spectral images by adopting weighted fusion to obtain reliable panoramic spectral image data.

The invention belongs to the technical field of image processing, and discloses an image comparison test method, which specifically comprises the following steps: step 1, image input and processing: a user needs to firstly input an image needing to be compared, and provides a comparison parameter; and 2, image validity verification: before comparison, the application program carries out image validity verification. The method has the advantages that the accuracy and reliability of image similarity comparison are improved, and the conditions of errors and missing detection in practical application are avoided; the application range is wide, and the method can be used for comparison of various image types; the method is widely applied to the field of digital media, and can be used for video coding, image compression, image denoising and the like; image quality evaluation and optimization are supported, the method can be used for image similarity comparison, and the SSIM comparison method can also be used for image quality evaluation and optimization, so that the image is more vivid and real; the method can be applied to image search, image recognition and the like to carry out accurate search and recognition.

The invention provides a lung cancer image recognition method and system, and the method comprises the steps: obtaining a lung image of a patient, and carrying out the image correction processing of the lung image, so as to obtain a corrected lung image; performing iterative denoising processing on the corrected lung image to obtain a denoised lung image; performing feature image segmentation on the denoised lung image to obtain a lung feature image; the method comprises the following steps: acquiring a historical template lung cancer image set, inputting the historical template lung cancer image set into a preset recognition model for training, inputting a lung feature image into the trained preset recognition model for recognition, and outputting a recognition result. Noise and artifacts existing in the image are effectively removed, interference information existing in the image can be eliminated at the same time, and then the recognition accuracy is improved.

The invention discloses a method for improving video stitching quality based on an improved optimal stitching line algorithm, relates to the field of application of vehicle-mounted cameras, the field of panoramic image fusion, the field of reversing parking and the field of unmanned driving, and solves the common problems of image distortion, obvious stitching lines, discontinuous stitching areas and the like in the video stitching process. An improved optimal suture line algorithm is selected, and the method can automatically find the optimal overlapping area and the optimal suture line position, so that the distortion and discontinuity phenomena in the splicing process are reduced. By improving the splicing effect, the method can be widely applied to the fields of panoramic video production, virtual reality and the like, and the user experience is remarkably improved.

The invention provides an image mirror surface highlight removal method and device based on a gradient mask and electronic equipment. The method comprises the following steps: acquiring a mirror surface highlight image and prior semantic information thereof; based on the prior semantic information, performing mirror surface highlight detection on the mirror surface highlight image to obtain a highlight detection result; according to the highlight detection result and the gray scale and illumination information of the mirror surface highlight image, constructing a gradient mask used for representing highlight intensity change; and based on the gradient mask and the prior semantic information, performing image restoration on a highlight area of the mirror surface highlight image, and performing adaptive fusion on a restoration result and a non-highlight area in the mirror surface highlight image to obtain an output image after mirror surface highlight is removed. According to the technical scheme, by introducing the prior semantic information and the gradient mask, accurate detection and smooth restoration of the mirror surface highlight area are realized, and the visual quality of the image can be improved.

The invention discloses a single-layer Compton camera system and an imaging method, and relates to the technical field of nuclear radiation imaging. The system comprises a single-plane pixel type gamma ray detector used for collecting gamma ray interaction event data, including time information and energy information of each interaction and two-dimensional position information in a detector plane, and forming pixel counting distribution; the shielding structure is arranged on one side of the plane of the detector, is located in a local area of the detector and is used for enabling pixel counting distribution to generate a local counting missing feature when gamma rays are incident from one side where the shielding structure is located; and the judging and imaging module is used for judging a gamma ray incident side based on the local counting missing feature and executing Compton imaging reconstruction in a corresponding hemispherical view field range according to the judged incident side. According to the invention, partitioned omnidirectional imaging can be carried out on the radioactive source under the condition that a detector does not need to act on depth information, and meanwhile, generation of mirror image artifacts is avoided.

The present application discloses a PET image processing method, device, equipment and storage medium, including: acquiring matched PET image data pairs through a preset acquisition method to obtain a target image set; the PET image data pairs include standard quality images and preset quality images; processing the target image set according to a preset preprocessing method to obtain a target training set, constructing a neural network through a preset code, and determining a preset loss function; inputting the preset quality image into the neural network to obtain a corresponding output image, calculating the difference between the output image and the standard quality image through a preset loss function to obtain a target loss value; after back-propagating the target loss value, adjusting the neural network parameters based on the target loss value to obtain stable parameters and thus determine the target neural network. The present application determines stable parameters by calculating the target loss value between the output image and the standard quality image, determines the target neural network, improves the image signal-to-noise ratio and image quality, and eliminates artifacts.

The application provides a kind of three-dimensional induced polarization inversion and target identification method based on deep learning, comprising: preprocessing three-dimensional induced polarization observation data collected by multiple sources to obtain preprocessed induced polarization data;Based on non-structural multi-scale grid, a three-dimensional induced polarization forward and inversion model is constructed, the preprocessed induced polarization data is input into the three-dimensional induced polarization forward and inversion model, and iterative solution is carried out combined with grid self-adaptive encryption and joint inversion objective function to generate three-dimensional electrical property characteristic data;Apply mixed regularization constraint to three-dimensional electrical property characteristic data to generate high-resolution three-dimensional electrical property data volume;The mixed regularization constraint contains smoothing constraint and total variation constraint, and the weight proportion of the smoothing constraint and the total variation constraint is dynamically adjusted according to the detection depth;Input the high-resolution three-dimensional electrical property data volume into the deep learning identification model to output the target identification result.The application effectively eliminates the false anomaly of complex fault structure, greatly improves the accuracy and efficiency of deep mineral exploration, and is suitable for fine exploration scene of deep deposit.

The present disclosure provides an image enhancement processing method, device, equipment and storage medium, which relates to the field of artificial intelligence technology, and in particular to the field of cloud computing technology. The specific implementation scheme is: in response to the read input image, the color space corresponding to the input image and all color pixels of at least one color component in the color space are obtained; the sharpened pixel values ​​corresponding to the pixel positions of all color pixels are determined; based on the sharpened pixel values ​​corresponding to the pixel positions of all color pixels, the smoothed sharpened pixel values ​​corresponding to all pixel positions are determined; according to the smoothed sharpened pixel values ​​corresponding to all pixel positions, the image to be encoded corresponding to the input image is obtained.

The utility model discloses a dustproof motor, which comprises a motor body and an electrostatic adsorption mechanism arranged on the motor body and used for adsorbing small particles in the motor body. The electrostatic adsorption mechanism comprises a conductive unit and a dust sticking unit covering the surface of the conductive unit. According to the utility model, through the synergistic effect of active electrostatic adsorption and passive dust-sticking fixation, the limitation of the traditional motor dust-proof technology is broken through, the POG imaging problem caused by the fact that small particles fall on a BG is avoided, and the imaging quality and the reliability of the camera module are remarkably improved.

The present invention relates to the field of radar imaging technology, and discloses a synthetic aperture radar forward oblique imaging polar coordinate imaging algorithm, including: obtaining electromagnetic signals when radar waves are reflected when they arrive at a detection target and flight path data of the radar waves, performing data preprocessing, and establishing echo data. Convert the rectangular coordinate system of the echo data into a polar coordinate system, and perform grid processing. Remove the Doppler effect in the echo data after grid processing based on the range Doppler algorithm, and determine the azimuth of the echo data based on the polar coordinate fast Fourier transform. Divide the echo data after the Doppler effect is removed into several sub-areas based on a pre-configured viewing angle for block processing, and reconstruct into an image to be generated. Perform image filtering enhancement and eliminate artifacts on the image to be generated, and convert the image to be generated into a standard image format for output. The present invention can effectively improve the accuracy and reliability of radar imaging through signal processing and image reconstruction technology.

The present invention relates to the fields of computer graphics and deep learning, and in particular to a method for generating a human motion transfer image based on a neural radiation field, comprising: preprocessing a source human motion image and a target motion semantic mask, and inputting the preprocessed NeRF decoupling generator G after training, obtaining the semantic category probability S, density σ and color C to which each pixel point output by G belongs; generating a synthetic human image, a synthetic semantic mask and an inverted semantic mask based on the semantic category probability S, density σ and color C; adaptively adjusting the parameters of the G according to the synthetic semantic mask and the inverted semantic mask; and reacquiring a synthetic human image of the human motion transfer image that is finally output. The above method solves the problem of shape and appearance being adhered to each other during motion transfer in the existing method, and eliminates the dependence of human image synthesis on bone priors.

The invention belongs to the field of computer vision, and relates to an alternating iteration straight line matching and image splicing method. After pre-registration based on matched feature points is carried out by using an existing algorithm, firstly, global line-guided grid deformation is carried out to obtain a preliminary splicing result; secondly, obtaining line matching pairs through a new straight line matching method which does not depend on descriptors; then, point matching pairs are obtained from the line matching pairs according to the invariant feature number, and the line matching pairs and the point matching pairs return to the previous step to jointly guide global line guide grid deformation. Global line guide grid deformation and line point matching are continuously and alternately iterated. Finally, the optimal line point matching and the best splicing result are obtained, more accurate matching feature pairs can be generated through the result, clearer textures can be displayed, a remarkable natural structure in a spliced image is reserved, and the effect is superior to that of a traditional image splicing method and a latest method based on deep learning.

Embodiments of the present disclosure provide a solution for visual data processing. A method for visual data processing is presented. The method comprises: for a conversion between visual data and a bitstream of visual data, obtaining an intermediate representation of the visual data, the intermediate representation being different from a quantized implicit representation of the visual data, a quantized implicit representation and generated based on at least one of: at least one parameter, at least a portion of the quantized implicit representation, a prediction of at least a portion of the quantized implicit representation, or a difference between the prediction and at least a portion of the quantized implicit representation; and performing a synthetic transformation on the intermediate representation for the transformation, where the quantized hidden representation is generated based on applying the first neural network to the visual data.

The application relates to a crowd-sourced street view video three-dimensional reconstruction enhancement method based on a semantic evolution loop. The method comprises the following steps: obtaining an initial three-dimensional reconstruction result, identifying a rendering artifact area in the initial three-dimensional reconstruction result and generating an artifact mask; performing extension processing on the artifact mask to obtain a repair mask; combining the repair mask and a semantic repair instruction to perform semantic repair on the artifact area, thereby obtaining a three-dimensional reconstruction intermediate result; constructing a tubular constraint space based on an original collection track, generating a virtual camera track covering an unobserved area, performing perspective expansion on the intermediate result, and obtaining a reconstruction view under an expanded perspective; calculating a geometric compatibility index of the reconstruction view and a corresponding re-projection rendering view, and if the consistency requirement is met, the reconstruction view is used as supplementary training data to feed back optimization, the three-dimensional reconstruction result is iteratively updated, and the method is stopped until the stop condition is met. The method can improve the fidelity and geometric consistency of the three-dimensional reconstruction result of fragmented crowd-sourced data.

The invention discloses a radiology department digital gastrointestinal machine imaging control method and system, and relates to the technical field of radiology department digital gastrointestinal machine imaging control, and the method comprises the steps: carrying out the data collection based on image collection and processing; carrying out image optimization and personalized dose control; and generating a report according to the acquired image and the control result. According to the method, X-ray dose parameters are predicted and adjusted in real time based on a deep learning algorithm and data collected in real time, the radiation dose is controlled at the lowest level on the premise of ensuring that diagnosis requirements are met, noise and artifacts in images are effectively removed through the technologies of noise filtering, high-pass filtering, non-local mean denoising and the like, and the diagnosis accuracy is improved. According to the method, the definition and readability of the image are improved, the diagnosis report is automatically generated according to the collected image and the control result, the time of a doctor is saved, the diagnosis efficiency is improved, the report generation process is automated, the possibility of human errors is reduced, and the report reliability is improved.

The present application discloses a scene rendering method, device, electronic device, storage medium and program product, which belongs to the field of computer vision technology. The method includes: decomposing a scene to be rendered into a first scene area and K second scene areas, the first scene area is used to represent the global scene of the scene to be rendered, and the second scene area is used to represent the local scene in the scene to be rendered; constructing a global sampling search grid based on the first scene area, and constructing a local sampling search grid based on the second scene area; obtaining S first sampling points from the first scene area through the global sampling search grid, and obtaining T second sampling points from the target scene area through the target sampling search grid, wherein the target sampling search grid is one of the K local sampling search grids; training a neural network based on the S first sampling points and the T second sampling points; rendering the scene to be rendered based on the trained neural network.

The application discloses a kind of magnetic field inversion current methods based on near-field scanning, including steps: obtaining magnetic field data in circuit, and as input;The magnetic field is extended measurement plane, and the magnetic field matrix is obtained, so that the original magnetic field matrix H is continuous on the current of boundary;The relationship between magnetic field and current in frequency domain space is constructed;Using Tikhonov regularization method to reconstruct current distribution, and using GCV method to optimize the regularization parameter required by Tikhonov regularization method;Extract the structural information of static current model, use it as a mask, and apply it to the result of radio frequency current model;Output the current in the original measurement window as the current reconstructed by the final method.The application eliminates the current artifact on the boundary by extending the measurement plane, automatically solves the current distribution by regularization, avoids the error caused by empirical selection of filter parameters, improves the accuracy of reconstructed current, and finally suppresses spatial noise by mask method to realize accurate reconstruction of radio frequency current.

The present invention relates to an image stitching method based on an optimal stitching line self-selected area fade-in and fade-out algorithm. The present invention uses a SIFT algorithm to extract feature points from a target image and a reference image respectively, establishes a feature description subset, obtains an initial matching result by comparing the nearest neighbor distance with the next nearest neighbor distance, uses a RANSAC algorithm to remove outliers and retain inliers from the initial matching result to complete image registration; uses an improved fade-in and fade-out fusion algorithm based on the optimal stitching line to perform image fusion, including using a dynamic programming method to find the optimal stitching line, limiting the fusion range according to the optimal stitching line, and finally uses a fade-in and fade-out fusion algorithm to fuse within the limited fusion range to complete image stitching. The present invention better solves the problem of unnatural image stitching results and the presence of stitching seams, enhances image results, and can be used in image stitching.

The present application relates to a kind of single exposure Raw video HDR reconstruction method and system, belong to computer vision and image processing technical field.The method comprises: obtaining single exposure bayer format Raw video sequence;Channel decomposition parallel alignment module is constructed, and input frame is decomposed into full channel flow, red blue channel flow and double green channel flow three parallel branches, using the physical characteristics that red blue channel is not easy to saturate in highlight area and green channel high signal-to-noise ratio, respectively, different feature extraction and alignment are carried out;Exposure complementary mask guided repair module is constructed, by calculating overexposure mask and dividing timing complementary area, using normal exposure information of adjacent frame in complementary area to repair the overexposure area of current frame;Multi-stream features are fused using cross attention mechanism, and high dynamic range video frame is reconstructed by combining mask guided color loss.The present application effectively solves the problem of highlight overflow and dark noise without alternating exposure, avoids ghosting artifacts, and significantly improves the reconstruction quality of HDR video.

The EOTF brightness adjustment method, JND coefficient value range method and system relate to the field of image display technology, and solve the problems of perceived contrast loss and artifacts in low-brightness scenes caused by the existing EOTF under 10-bit PQ quantization. Specifically: based on the value range of the JND coefficient, combined with the adaptive adjustment factor and the Barten function, the value of the JND coefficient is determined; based on the value of the JND coefficient, the brightness and JND coefficient relationship function is repeatedly input until the peak brightness is obtained, and then the operation is terminated. The EOTF brightness adjustment method described in the present invention is applicable to any changes in peak brightness and parameter values.

The invention discloses an image segmentation recognition method and system based on a coupling model, and relates to the technical field of image recognition, and the method comprises the steps: collecting to-be-detected data, and training a YOLOv10 model through the preprocessed data; performing gamma distribution processing on the to-be-measured data, coupling the U-Net model to output a first feature, and training the coupling convolution model through the first feature; and splicing the YOLOv10 model and the coupling convolution model, and training and optimizing the spliced image segmentation recognition model to realize image recognition. The image quality is improved through data preprocessing. Performing image segmentation by using a U-Net architecture in combination with gamma distribution; according to the method, a YOLOv10 model and a coupling convolution model are spliced by adopting modified MobileNetV2 and DenseNet, and a spliced image segmentation recognition model is trained and optimized, so that image classification is realized, and the inaccuracy of manual detection is avoided.

The invention provides a standing posture balance muscle control evaluation device based on partial derivative coherence. The standing posture balance muscle control evaluation device is characterized by comprising a rotating platform, a VR helmet, an EEG collecting device, an EMG collecting device, a data processing module and a user interface. The invention further provides a standing posture balance muscle control evaluation method based on partial derivative coherence. According to the method, the device is combined, multi-sensory stimulation with consistent and inconsistent sensory is provided, a standing posture balancing task is carried out under different conditions, and data are synchronously collected. Through signal preprocessing, source reconstruction and partial derivative coherence analysis, a directional function connection network between cortex and muscle is constructed. Cortex-muscle coupling is decomposed into the somatosensory information transmission capability of muscles and the control capability of cortex on the muscles, and bidirectional understanding of a neuromuscular interaction mechanism is provided. Not only are scientificity and accuracy of balance control function evaluation improved, but also powerful technical support is provided for clinical diagnosis and rehabilitation of balance disorders.

This invention relates to brain-computer interfaces, specifically to a brain-computer interface-based emotion and stress management system. The control unit acquires the user's multimodal signals using a non-invasive multimodal signal acquisition device and preprocesses the EEG signals using an EEG signal processing module. The control unit then estimates the power spectral density of the preprocessed EEG signals using a power spectral density estimation module to obtain the power spectral density of the EEG signals. A relaxation level acquisition module obtains the user's relaxation level based on the power spectral density of the EEG signals, a coefficient of variation acquisition module obtains the coefficient of variation for the corresponding frequency band based on the power spectral density of the EEG signals, and a Schumann resonance energy index acquisition module obtains the Schumann resonance energy index corresponding to the first and second harmonics based on the power spectral density of the EEG signals. The technical solution provided by this invention overcomes the shortcomings of existing technologies, such as the difficulty in accurately identifying the user's emotional state and the inability to provide personalized real-time intervention.

The present invention discloses a network structure and method for multi-scale super-resolution reconstruction of a single image. The network structure of the present invention comprises: a feature extraction layer, the input of which is the original image, and the output of which is connected to the input layer of a dilated convolutional neural network; a cascade expansion layer, the input of which is connected to the output layer of a dilated convolutional neural network; an aggregation layer, which is used to fuse the low-level coding information of different scales of the feature extraction layer and the decoding information of the cascade expansion layer; a reconstruction module, the input of which is the output of the cascade expansion layer, and the output of which is the reconstructed image. The present invention adopts a back-end upsampling network to perform image mapping transformations in a low-resolution space, thereby reducing the computational complexity and spatial complexity.

The invention relates to the field of computer technology and optics, and discloses a multiplexing coaxial hologram reconstruction method based on an all-optical diffraction network, which comprises the following steps: holographic recording: after a plane wave with the wavelength of lambda passes through a polarization splitting prism, splitting two beams of light with mutually orthogonal polarization states, and respectively irradiating the two beams of light onto pictures of two recorded objects, the two beams of light coincide through a prism, a holographic plate is placed at the coincidence position of the two light fields, and an overlapped recording multiplexing coaxial hologram after interference of object light waves and reference light waves is obtained by recording the intensity of the object light waves and the intensity of the reference light waves on a plane; and through the reproduction of the all-optical diffraction network, that is, the recording multiplexing coaxial holograms are irradiated by plane waves with the same wavelength, the recording multiplexing coaxial holograms are respectively sent into the parallel trained all-optical diffraction network by the polarization splitting prism, and the reconstruction and reproduction of the multiplexing coaxial holograms are completed. According to the method, the multiplexing coaxial hologram can be successfully reconstructed, and twin images are eliminated.