1310 results about "Real image" patented technology

The invention provides a scene reconstruction method based on delayed rendering and three-dimensional Gaussian. The method comprises the following steps: S1, generating initial three-dimensional point cloud data based on a multi-view image; s2, constructing a trainable structural body for three-dimensional Gaussian modeling; s3, normal initialization and residual optimization are carried out on the Gaussian ellipsoid primitives, depth consistency constraint is combined, and a differentiable and learnable normal reconstruction mechanism is realized, so that the geometric expression ability of illumination modeling is enhanced; s4, introducing a reflection training mechanism based on ambient light and a reflection direction, and generating a Gaussian attribute based on a visual angle; and S5, a final image is generated through a differentiable Gaussian sputtering rendering algorithm, and optimization is carried out through pixel loss of the final image and a real image. According to the method, the reality sense and geometric consistency of the Gaussian sputtering model under the complex illumination condition are remarkably improved, and the technical problems of unreal rendering effect, inaccurate surface normal estimation, weak propagation capability and the like of the existing three-dimensional Gaussian sputtering model under the complex illumination condition are solved.

The invention discloses a multi-target scene visual SLAM (Simultaneous Localization and Mapping) method fusing target semantics and Gaussian splashing. The method comprises the steps that input data are preprocessed, two-dimensional space masks and semantic information corresponding to the two-dimensional space masks are extracted, global semantic identifiers are distributed to the two-dimensional space masks corresponding to a background and each target, and a target semantic segmentation map is obtained; reading first frame RGBD data as a current frame, acquiring a target semantic segmentation map corresponding to the current frame, and initializing camera pose parameters; respectively establishing an initial target Gaussian splashing model for the background and each target; adopting a Gaussian splash algorithm to render all the target Gaussian splash models to a next frame, and generating an RGB rendering image, a depth rendering image and a target semantic rendering image of the next frame; and designing a loss function between the next frame of rendered image and the corresponding real image, optimizing the pose of the camera, and updating the parameters of the target Gaussian splash model. According to the invention, simultaneous positioning and map construction of multiple target scenes are realized.

The invention discloses a three-dimensional Gaussian sputtering method for sparse visual angle semantic priori. The method comprises the following steps of: 1, acquiring a target scene image, constructing a real image set as a data set, and manually selecting an interested object in a target scene to perform semantic three-dimensional reconstruction to obtain an initial semantic image of a sparse view angle; a multi-view image is collected, camera external parameters and scene sparse point clouds are obtained, a plurality of views are selected and input into the SAM2 segmentation model, and a view set Is with semantic images is obtained; 2, using a pre-trained SAM2 segmentation model as an interactive image sequence segmentation model, and initializing a three-dimensional Gaussian primitive according to the scene sparse point cloud; and step 3, training parameters of semantic three-dimensional Gaussian sputtering based on the trained three-dimensional Gaussian sputtering model and the interactive image sequence segmentation model, and reconstructing a target scene. According to the method, the high-quality semantic model is efficiently reconstructed. And the reconstruction result can be easily corrected through the interactive graphical interface in the training process.

The invention discloses a deep counterfeiting detection model training method, a deep counterfeiting detection method and a deep counterfeiting detection system, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a training data set containing a real image and a plurality of counterfeit images, and enabling the image to be provided with a label for representing the authenticity; in the training process, the deep forgery detection model can be in contact with various types of image samples, so that wider and more complex image features and forgery modes can be learned, a forgery reason is further marked for a forgery image, and the deep forgery detection model can be helped to deeply understand essential features of forgery content in the training process. Therefore, the problem of insufficient detection capability for well-designed and high-quality counterfeited contents can be solved, and the technical effect of improving the accuracy of counterfeited detection is achieved.

The invention discloses a deep forgery detection method based on a visual language model, and relates to the field of image forensics. The deep forgery detection method based on the visual language model aims to combine multi-source information to improve the discrimination capability of the model on a real image and a generated image. The method comprises the following steps: firstly, extracting image features through an image encoder of a pre-trained CLIP model; meanwhile, a frequency domain enhanced counterfeit perception adapter is embedded in the image encoder to mine potential anomalies of counterfeit images in the image domain and the frequency domain. Secondly, a manual feature extraction module is provided, discriminative low-dimensional features are extracted from the four aspects of the edge, the texture, the frequency and the symmetry of the image, and the discriminative low-dimensional features are used as auxiliary information input in the forgery detection process, so that the robustness and the interpretability of the model are improved; meanwhile, the text cue words are converted into feature vectors through a text encoder of a pre-training CLIP model; and finally, the model predicts a forgery score by calculating the cosine similarity between the image features and the text features so as to realize the discrimination of the authenticity of the image. According to the method, the problem that the detection capability of the model on the cross-dataset is insufficient is effectively improved.

The invention relates to an introspection lifelong SLAM method and system based on visual language model hidden space representation, and the method comprises the steps: extracting a semantic tag based on an RGB-D image through a semantic encoder, and generating a scene map and a semantic topological graph based on the RGB-D image and the semantic tag; generating a dynamic mask based on the scene map, obtaining a dynamic mask coverage rate, and screening key frames with high static confidence values based on the coverage rate; calculating camera pose estimation corresponding to the key frame in real time, sampling the key frame to realize layering of the key frame, and performing layering rendering by using a NeRF model to obtain a virtual view; the hidden space difference degree of the virtual view and the corresponding real image is calculated, whether error introspection needs to be carried out or not is judged based on the hidden space difference degree, and the system is used for achieving the method. Compared with the prior art, the method has the advantages that open semantic reasoning of VLM, high-precision reconstruction of NeRF and real-time positioning of SLAM are combined, and positioning and mapping accuracy is improved.

The invention discloses a flow field measurement method based on an event camera, and the method comprises the steps: generating a PIV data set, each time sequence sample sequence comprising a plurality of frames of continuous particle images, a corresponding velocity vector field, and particle event data at all moments; establishing a flow field data acquisition device based on an event camera and a high-speed camera, acquiring real event data and real image data which are synchronous in time so as to adjust parameters of an event simulator, and verifying and updating particle event data in the PIV data set according to the adjusted event simulator so as to obtain a flow field data acquisition result; obtaining the updated PIV data set as a training data set; building an event camera optical flow method model, and training by adopting the training data set; and on the basis of the trained event camera optical flow method model, event sequences in two adjacent time periods are used as inputs to calculate a velocity vector field corresponding to a middle moment. According to the invention, the flow field velocity field at the required moment can be obtained based on the event data within a period of time.

The invention discloses a high-precision three-dimensional scene and object reconstruction method, system, equipment and medium, belongs to three-dimensional scene reconstruction in the technical field of computer vision, and aims to solve the technical problem of low three-dimensional scene reconstruction quality in the prior art. The method comprises the steps of calculating external parameters of a camera, determining coordinates and colors of sampling points closest to the surface of a scene, updating and optimizing parameters and generating a scene geometric model and a rendering graph, the rendering graph is generated based on an SDF MLP network and a Color MLP network in combination with a Gaussian sputtering technology, and parameter optimization is carried out on Gaussian primitives, the SDF MLP network and the Color MLP network by utilizing the generated rendering graph and a real graph. And the Gaussian primitive after parameter optimization, the SDF MLP network and the Color MLP network are utilized to finally generate a high-precision scene geometric model and a new visual angle image with high fidelity rendering quality, so that the technical problem that geometric precision and rendering quality cannot be obtained at the same time in a three-dimensional reconstruction process is effectively solved, and the reconstruction quality of a three-dimensional scene is remarkably improved.

The invention belongs to an image processing technology, and particularly relates to a deep forgery detection method and system based on face embedded difference guidance, and the method comprises the steps: generating a forgery image according to an input real image, and calculating a difference image and a semantic difference vector of two images; learning the features of the real image and the forged image by using a first classifier, and learning the features of the real image and the difference image by using a second classifier; splicing and fusing the semantic difference vector and image features extracted from the difference image through a channel to obtain fused features, and learning the difference between the real face features and the fused features by using a third classifier; and calculating the distillation loss between the fusion feature and the image feature of the forged image, and training in combination with the loss of each classifier. According to the method, multi-level feature fusion, a three-classifier architecture and a knowledge distillation mechanism are adopted, the strong generalization ability across data sets is achieved, and the system can output a high-precision deep forgery judgment result.

The invention discloses a road and bridge settlement displacement monitoring system and method based on image detection, and the method comprises the steps: selecting a plurality of static background reference points in a stable background region of a monitoring scene, so as to construct a virtual and stable image internal reference system; furthermore, by accurately tracking image coordinate changes of the background reference points in the initial reference frame and the current frame, a transformation matrix capable of accurately describing disturbance of the camera from the initial pose to the current pose is reversely calculated, and the transformation matrix is applied to observation coordinates of a monitored target point; therefore, the virtual displacement component introduced by the camera disturbance is accurately stripped from the total displacement, and finally the real image displacement generated only by the motion of the structure is obtained. By means of the mode, the system can effectively resist interference of external factors such as environment vibration and temperature change, it is ensured that the height of the finally calculated physical displacement is close to the real settlement value of the structure, and therefore the accuracy and reliability of the monitoring result are greatly improved.

The invention discloses an image segmentation method for evaluating a hepatocellular carcinoma neutron therapy dose, and relates to the technical field of neutron therapy. A 3D U-Net GAN network model is trained through a medical image data set; a generator model and a discriminator model in a 3D U-Net GAN network model are alternately trained by using a real image and a false image randomly generated by the generator model, so that the trained generator model can generate a segmentation result which is more accurate and rich in details for a medical image; the trained discriminator model can more accurately evaluate the authenticity of the output of the generator model, and the trained network can accurately reflect the anatomical structure of the patient, the distribution condition of 10B in the body and the radionuclide dynamics condition when segmenting the medical image, so that the information in the medical image can be accurately and fully displayed, and the medical image segmentation efficiency can be improved. The application is convenient.

The invention belongs to the technical field of sea temperature complementation, and discloses a sea temperature complementation method and system based on an asynchronous diffusion Schrodinger bridge, and the method comprises the steps: firstly, generating a weight anomal which reflects the abnormal degree of a pixel through a preprocessing step S1, so as to guide a subsequent diffusion process; s2, establishing a bidirectional diffusion path between the initial complementation image and the real image based on a diffusion Schrodinger bridge theory, and dynamically adjusting a diffusion coefficient according to anomay to generate an intermediate state xt; predicting a score function pred through a U-Net network S3, and reconstructing a current image x0 for updating a state or calculating loss; model parameters are optimized through iteration during training, multi-round denoising reconstruction is carried out during inference, and finally a complete high-quality sea surface temperature image SSTrecon is output. According to the invention, local details are fully reserved, and the accuracy of image completion is improved.

The invention discloses a titanium alloy microscopic structure prediction method and system based on a conditional generative adversarial network and a storage medium, and belongs to the following steps: firstly, constructing a process-structure mapping model, and taking the output of the model as a rule constraint condition; inputting the random noise vector and the rule constraint condition into a conditional generative adversarial network to generate a prediction image; according to the generative adversarial network, thermal dynamic constraints based on physical quantities of microscopic structures are introduced in the training process, so that the interpretability of a prediction result is improved. And carrying out quantitative comparison on the predicted image and the real image, verifying the consistency of the statistical characteristics, and if the verification is passed, outputting a prediction result. According to the method, end-to-end prediction from process parameters to microscopic structure images is realized, the limitation that only symbolization or parameterization prediction can be carried out in a traditional method is broken through, and the intuition, the interpretability and the engineering application value of the method are remarkably enhanced.

The invention discloses a face forgery detection algorithm based on style-guided multi-view fusion processing. The method comprises the following steps: firstly, carrying out standardized preprocessing on a face video sample, and extracting multi-scale image features based on an OfficientNet-B4 backbone network; by constructing a local texture map, an attention enhancement map and a style vector sequence, precise modeling and discrimination of a forged area are realized. The algorithm further utilizes a multi-branch sequence convolutional network to carry out time sequence modeling on fusion features, and outputs global style change representation for classification of forged and real images. The method comprehensively fuses the spatial texture, the semantic style and the time feature, has the advantages of high detection precision, strong generalization ability, good robustness and the like, and is suitable for complex and diverse depth forgery detection tasks.

The invention discloses a laser radar-camera fusion calibration method and system, and belongs to the field of calibration. According to the method, after a target is shot through a laser radar and a camera, multiple pairs of corresponding three-dimensional point clouds and two-dimensional images are obtained; angular point coordinates in the phase unwrapped image are obtained through the phase unwrapped image and the two-dimensional real image, three-dimensional angular point coordinates in the three-dimensional point cloud are further obtained, the two-dimensional angular point coordinates and the three-dimensional angular point coordinates are input into an external parameter calibration program of the laser radar and the camera, a rigid matrix between two sensor coordinate systems is obtained, and calibration is completed. According to the method, a fast inversion method of angular points on a phase unwrapped graph based on a Gaussian elimination method is designed, a calculation strategy of laser radar point cloud-phase unwrapped graph-angular point detection-angular point three-dimensional coordinates is provided, after three-dimensional point cloud and two-dimensional images of a target are collected, data can be imported into a calibration program for automatic calculation, and the accuracy of calibration is improved. The method has the advantages of less human intervention, high automation degree, fast operation and high efficiency.

The invention discloses a method for clearly imaging a mask under an optical objective, and relates to the technical field of optical detection and image processing, and the method comprises the following steps: S1, constructing a light intensity distribution model based on the boundary condition of the field of view of the optical objective in combination with the reflection characteristic and incident angle change rule of a metal edge material, and determining the coverage range of metal edge reflection crosstalk, generating a mask boundary interference prediction map; s2, according to the mask boundary interference prediction map, performing region division on the micro-reflectivity image, extracting brightness gradient characteristics of reflectivity lifting in an interference region, and generating a brightness gradient parameter set required by image filtering; the method is based on light intensity modeling, fusion direction filtering, pixel stripping, gradient reconstruction and credibility weighted fusion, realizes closed-loop control from interference prediction to pixel restoration, has high resolution and adaptivity, can accurately strip edge reflection artifacts and restore a real image structure, remarkably reduces misjudgment and rework rate, and is suitable for large-scale popularization and application. And the mask yield and the stability of the detection system are improved.

The invention discloses a low-illumination image enhancement method based on wavelet-Fourier transform. Relates to the technical field of computer vision, in particular to the technical field of low-illumination image enhancement based on wavelet-Fourier transform. The advantages of the two frequency domain transformations are fully exerted, the problems of insufficient illumination adjustment and poor detail retention in the existing method are solved, and the utilization efficiency of the frequency domain information is improved. The method comprises the following steps: acquiring an image data set, wherein the image data set comprises a low-illumination image and a real image; constructing an image enhancement model: inputting the low-illumination image into the image enhancement model, carrying out iterative training, and when the number of iterative training times reaches 200, obtaining a trained image enhancement model; and inputting the low-illumination image into the trained image enhancement model to obtain an enhanced image. Compared with the prior art, the method can better restore image details, adjust image saturation, reduce noise and improve visual effect.

The invention provides a method and a system for generating deviation guide information in an approaching landing section of an aircraft and a readable storage medium, and the method comprises the steps: S1, real image collection and processing: capturing an outboard environment image through an airborne visual sensor, and extracting key feature points of a target landing airport runway; s2, virtual image generation: based on the airport three-dimensional high-precision map, generating an approach glide image under a visual angle of a virtual camera; s3, image feature registration: carrying out spatial alignment on the key feature points of the runway in the real image and the key feature points of the runway in the virtual image; the pixel deviation of the runway center point in the real image and the virtual image is calculated, glide guidance is provided for a pilot or is directly input into an automatic flight system.The method can be used for the aircraft in the approaching landing section, the enhanced outboard visual display is provided for the pilot in the cockpit based on the equivalent ILS deviation guidance method, and the visual guidance effect is improved. And providing approaching glide deviation guide information.

The invention discloses a neural radiation field rendering method based on dynamic hash coding, and the method comprises the steps: employing the feature sequence data as the input, calculating the density value and color value of each sampling point through the forward propagation of a neural network, carrying out the volume rendering integral operation according to the ray tracing principle in the direction of a ray, and obtaining the feature sequence data; judging a final color output result of the current pixel point; according to an error value between the color output result and a real image, updating a network parameter weight through a back propagation algorithm, and if the error value is greater than a convergence threshold, continuing to iterate the training process to adjust a feature coding strategy to obtain an optimized neural radiation field model parameter; and after the rendering performance configuration parameters are obtained, optimizing a storage allocation strategy of feature data through a memory pool management mechanism, and if the current memory occupancy rate exceeds a safety threshold, starting a data compression algorithm to reduce the storage space requirement, and obtaining a real-time rendering output result. According to the invention, high-quality real-time rendering of the dynamic scene is realized.

The invention discloses a medical image cross-modal generation method and a medical image cross-modal generation device based on wavelet high-frequency enhancement, which have the following effects: common features and unique features of a multi-modal medical image are effectively learned by using multi-scale local-global features and high-frequency texture detail information, and an accurate and fine target modal image is obtained. The method can effectively deal with the limitation of medical conditions and reduce the cost of obtaining multi-modal images, and has great application value. A CMMB block of the global branch encoder aggregates global information and multi-scale local features, fully learns information of different anatomical structures and muscle textures, and generates fine edge textures and tissue details of a target modal image; an RSTB block of the high-frequency branch encoder extracts high-frequency features from an input mode and aggregates the high-frequency features with global branches, so that the texture fidelity of a generated image is promoted, and the image is close to a real image; the CAG mechanism of the decoder promotes feature interaction between the decoder and the encoder, redundant information is removed, and an efficient image is generated.

The invention relates to the field of cultural relic image recognition, in particular to a text image enhancement method based on multi-scale feature fusion and a residual attention mechanism, and the method comprises the steps: obtaining a historical material data set, marking a text region in the historical material data set, and constructing a real text image data set; generating an image construction synthesis data set with the same format as the historical material text region; introducing a plurality of noise types into the synthesized data set to simulate possible problems of an actual old text image; an improved U-Net network is provided for text image enhancement so as to better learn a mapping relation between a real image and a degraded text image; a multi-scale feature perception and extraction module is adopted to extract feature information in the image so as to improve the image contrast and solve the noise problem; the extracted features are further processed through a residual attention module, and important areas in the image are effectively concerned; the image features are further optimized through a feature enhancement module, and image details and contrast are enhanced; the image denoising effect of the text extraction model for the historical materials is better than that of an existing model.

The invention discloses an event stream image reconstruction method based on hierarchical uncertainty, and the method comprises the steps: S1, inputting event stream data, and carrying out the random sampling, and constructing a multi-mask event frame; s2, inputting the processed event frame into an event flow reconstruction network to obtain a plurality of reconstructed images, the reconstructed images sharing a real image, and establishing an uncertainty model between the reconstructed images and the real image; and S3, adjusting network parameters to train and test a neural network, and screening images needing to be optimized from the uncertainty scores. The accuracy and quality of event stream image reconstruction are improved, and the adaptability and robustness of the model to a dynamic scene are enhanced.

The invention relates to the technical field of deep counterfeit image detection, and discloses a deep counterfeit detection method and system, a storage medium and computer equipment. The method comprises the following steps: firstly, constructing a reference data set containing a forged image and an original real image; secondly, through an integrated model, generating antagonistic samples for the reference data set, and integrating the successfully attacked antagonistic samples into an antagonistic sample set; and finally, merging the reference data set and the adversarial sample set, and constructing a robustness enhanced data set containing four types of samples. In the model training stage, multi-classification cross entropy loss and comparative learning loss are combined, and expression of the model in a feature space is optimized through comparative learning constraint, so that the model learns discriminative features with more compact intra-class features and more dispersed inter-class features. The model trained by the method not only can effectively defend against attack and improve robustness, but also surpasses original detection performance on clean samples, and has remarkable technical advantages and application value.

The invention provides a heart MRI quantitative imaging motion correction and parameter graph reconstruction and segmentation method and system. The method comprises the following steps: constructing a multi-task model; and constructing a motion-free simulation signal data set of the selected quantity imaging task, and pre-training the single-pixel parameter estimation network and the single-pixel signal simulation network based on the data set. And constructing a real image data set of the selected amount of imaging tasks, fixing network parameters obtained by pre-training, and training a multi-task model to determine values of residual parameters in the model. And performing motion correction, quantitative parameter graph reconstruction and region-of-interest segmentation on a newly acquired image sequence of the selected amount of imaging task through the trained multi-task model, and outputting a reconstructed quantitative parameter graph and a segmentation mask of the quantitative parameter graph. The method is more accurate and quicker, and can realize full-automatic and one-stop post-processing of heart quantitative imaging.

The invention discloses a geometric spectrum coupling-based neural radiation field high-fidelity representation method and system. The method comprises the following steps of: obtaining sparse multi-view image data and calculating internal and external parameters of a corresponding camera; generating light based on internal and external parameters of the camera, and performing point sampling on the space along the direction of the light to obtain sampling points; constructing a differentiable coding layer, predicting the density of sampling points through forward propagation of a neural network, and obtaining a principal curvature, a density gradient and an included angle between a normal vector of the sampling points and an observation direction based on a density prediction result; constructing a spectrum-geometric coupling function, and calculating to obtain a spectrum coupling coding vector; the spectrum coupling coding vector is used as an input to be transmitted into a NeRF main network, and NeRF volume rendering is carried out according to a density and color prediction result; and performing difference optimization training on the rendering result and the real image to obtain a high-fidelity composite image. According to the invention, the expression ability of the neural radiation field model in the high-frequency detail area can be enhanced.

An industrial product cumulative damage image generation method based on an improved DCGAN is characterized by comprising the following steps: step 1, based on a DCGAN model framework, an industrial product cumulative damage image generation network IPD-GAN based on the improved DCGAN is constructed, and the IPD-GAN is provided with a generator and a discriminator; 2, the generator obtains random noise, processes the random noise, generates a damage image and transmits the damage image to a discriminator; step 3, the discriminator obtains a real image, carries out true and false discrimination on the real image and the damaged image, adopts a Wasserstein distance loss function with a gradient penalty term as a model to resist loss according to a discrimination result, guides training of the generator in combination with L1 loss and SSIM loss, and comprehensively optimizes system performance; and step 4, generating a damaged image by using the trained IPD-GAN. The method has the advantages that the problems of unstable training and gradient disappearance existing in the DCGAN are solved.

The invention discloses a customs remote intelligent patrol system based on a 5G + unmanned aerial vehicle technology, and relates to the technical field of unmanned aerial vehicle patrol. An unmanned aerial vehicle module is responsible for performing field data acquisition and primary processing according to application scene requirements; the wireless communication terminal provides a high-speed and low-delay data transmission channel through a 5G communication network; the customs command center is responsible for gathering, analyzing, commanding and deciding the collected application scene data. The data analysis module is matched with a high-definition camera to enable collected remote sensing data to be further close to a real image, so that customs supervision work management can be more accurate and stable, an intelligent inspection route is continuously optimized by carrying a path navigation model based on an improved ant algorithm, and the intelligent inspection efficiency is improved. And the intelligent inspection efficiency is improved.

The invention provides an optical lens, which comprises four lenses with focal power and sequentially comprises a first lens with negative focal power, a second lens with negative focal power, a third lens with positive focal power, a fourth lens with negative focal power, a fifth lens with negative focal power and a sixth lens with negative focal power from an object side to an imaging surface along an optical axis, the object side surface of the second lens is a convex surface, and the image side surface of the second lens is a concave surface; the object side surface of the third lens is a convex surface, and the image side surface of the third lens is a convex surface; the image side surface of the fourth lens is a convex surface; wherein the maximum field angle FOV of the optical lens and the real image height IH corresponding to the maximum field angle of the optical lens meet the following conditions: 35 degrees / mmlt; fOV / IHlt; 46 degrees / mm; the focal length f3 of the third lens and the focal length f4 of the fourth lens satisfy 0.5 lt; f3 / f4lt; and 0.8. According to the optical lens provided by the invention, through specific surface shape matching and reasonable focal power distribution, the lens has one or more advantages of an ultra-wide angle, a large aperture, high imaging quality, high collimation and the like.

The invention discloses a visual detection optimization control method, device and equipment based on digital twinning and a storage medium, and relates to the technical field of visual detection, and the method comprises the steps: obtaining an analog image and a real image in a digital twinning environment, and carrying out the frequency domain feature transformation of the analog image and the real image, thereby obtaining an image spectrum feature; a frequency domain alignment model is established based on multi-band spectrum envelope guide residual mapping, and the structure of virtual and real image spectrum features is kept aligned; further extracting features through multi-scale convolution and channel dependence mapping to obtain virtual-real fusion features; quantifying channel similarity and establishing a covariance regularization constraint, performing channel correction on the cross-domain features, and eliminating feature drift to obtain second virtual-real fusion features; and finally, performing visual detection and micro defect identification based on the features. The problem that a virtual sample and a real sample are different in local texture structure and channel distribution is solved.

The invention provides a vortex electromagnetic wave radar multipath virtual image suppression method and system, and relates to the technical field of radar imaging virtual image suppression, and the method comprises the steps: adjusting the circular ring radiuses corresponding to different modes in a uniform concentric circular ring array according to the prior information of a target pitch angle, and obtaining the radar emission configuration of main lobe alignment; performing time domain sampling on the multi-path vortex radar echo signal in each mode to form a two-dimensional radar echo matrix; analyzing the obtained two-dimensional radar echo matrix and the MISO system echo after phase compensation by using an azimuth dimension point spread function to obtain dual-system imaging features; and performing modal domain FFT and time domain pulse compression processing on the multipath echo features under the MISO and MIMO systems by using fast Fourier transform and pulse compression, and finally outputting a virtual image suppressed synthetic radar image. The multi-path virtual image suppression method has the beneficial effects that the suppression of the multi-path virtual image is realized by utilizing the characteristic that the real image positions of the imaging results are completely the same and the virtual image positions are different under the multi-transmitting and single-receiving systems.