239 results about "Speckle noise" patented technology

The invention relates to the technical field of optical lens defect detection, and particularly discloses an optical lens laser-induced damage on-line detection method and system, a linear polarizer and a narrow-band filter are connected in series in a detection laser light path, non-target polarized light is filtered through polarization direction matching, meanwhile, interference of the environment and scattered light is inhibited through the narrow-band filter, and the laser-induced damage on-line detection system is obtained. According to the method, external source noise such as ambient light and non-target polarized light is reduced through polarization matching and narrow-band filtering, randomness of speckle noise is offset through multi-angle collection and signal fusion in a visual detection mode, exposure is dynamically adjusted, an image is fused to improve the signal-to-noise ratio, noise and real signals are decoupled through self-supervised learning, and the real-time performance of the system is improved. Dust interference is removed in combination with morphological operation; a deep learning reasoning model enhances the damage identification capability in a noise environment, defocusing blur caused by mechanical vibration is eliminated through phase conjugate correction, and the influence of photoelectric conversion noise, dust interference and the like on the detection efficiency and accuracy is remarkably reduced through multi-link linkage.

The invention relates to the technical field of unmanned aerial vehicle control, in particular to an unmanned aerial vehicle dynamic obstacle avoidance method based on multi-sensor fusion. Comprising the steps of receiving an image sequence, a depth point cloud set, a pose state parameter and a flight speed parameter; calculating an illumination distortion gradient value and a speckle noise entropy value based on the image sequence and the depth point cloud set; performing coordinate registration and feature extraction on the image sequence, the depth point cloud set and the pose state parameter, and outputting a visual feature set and a radar feature set; generating a weighted visual feature set and a weighted radar feature set through a weight adjustment function, and generating a joint heterogeneous feature tensor through alignment splicing; and generating a body attitude and thrust control instruction by using the combined heterogeneous feature tensor through a probability prediction model and a model prediction control algorithm device. According to the method, through cross-domain multiplexing and deep coupling of the body physical parameters in the data stream, error accumulation and decision delay caused by multi-stage series calculation are avoided, and global performability of an obstacle avoidance task is facilitated.

The invention relates to the technical field of radar remote sensing image processing, in particular to a multi-scale synthetic aperture radar flood detection method and device, and the method comprises the steps: collecting a plurality of flood disaster SAR images of a flood region, and carrying out the preprocessing of the images, so as to obtain a standard flood disaster SAR image; dividing standard flood disaster SAR images, and constructing training, verification and test data sets; based on a multi-scale feature extraction network and a multi-head self-attention mechanism, constructing a multi-scale SAR flood detection network model, training the multi-scale SAR flood detection network model by using the training data set and the verification data set, and inputting the test data set into the trained multi-scale SAR flood detection network model, therefore, the influence of speckle noise is effectively suppressed, the capability of distinguishing flood from confusion-prone ground features in a complex scene is improved, and the accuracy and robustness of SAR image flood detection are improved.

The invention discloses an SAR (Synthetic Aperture Radar) target detection method based on phase-amplitude frequency domain cross modulation. The problems that due to inherent speckle noise in an SAR image, a target is difficult to distinguish, and an existing method is insufficient in frequency domain information complementarity and calculation efficiency are solved. According to the scheme, firstly, input SAR image features are converted into a frequency domain, and a phase spectrum and an amplitude spectrum of the SAR image features are obtained; the method is characterized in that through phase-amplitude frequency domain cross modulation, interaction and guidance between phases and amplitudes are promoted by using an attention mechanism, accurate adjustment, cooperative enhancement and effective noise suppression of the phases and the amplitudes are realized, and amplitude spectrums and phase spectrums after optimization modulation are generated; and finally, the optimized frequency domain features are transformed back to a spatial domain for subsequent target detection. According to the method, cross modulation is realized through a phase-amplitude feature exchange (PATE) mechanism and a frequency band division self-attention (BPSA) mechanism, so that the precision and robustness of SAR image target detection are remarkably improved, and the calculation complexity is effectively controlled.

The invention belongs to the technical field of image enhancement and recognition, and particularly relates to a radar image intelligent enhancement and recognition method and system based on multi-model fusion, and the method comprises the steps: carrying out the adaptive suppression of speckle noise of an original radar image; feature point detection is carried out, robust transformation matrix estimation and adaptive contrast enhancement are carried out, and a corrected and enhanced image is output; utilizing the generative adversarial network and multi-loss function collaborative constraint to obtain a texture reconstruction image; establishing an image-semantic double-flow network architecture, performing cross-modal attention fusion to obtain a fusion feature map, and outputting a target recognition result; performing time phase division on the texture reconstruction image, judging a change type, and outputting a change detection result; and outputting a processing report including the enhanced image, the target list and change analysis. According to the method, noise suppression, correction enhancement, texture reconstruction, target recognition and change detection are integrated, the defect of fragmentation processing in the traditional technology is overcome, and the overall processing performance and the actual application adaptability are improved.

The invention discloses an SAR image ship small target robust detection method and system based on improved YOLOv11, a storage medium and electronic equipment, and the method comprises the following steps: embedding an S2-MLPv2 module at the tail end of a backbone network, and suppressing speckle noise through spatial displacement operation and dynamic filtering; the SAFMN module is used for replacing traditional up-sampling, and the multi-scale feature fusion capability is enhanced in combination with a deformable convolution and gating mechanism; a SimAM non-parameter attention module is introduced, and ship texture and geometric features are adaptively focused based on an energy function; designing a TSIoU loss function, fusing a central point diagonal distance measurement and an end point distance measurement, and optimizing bounding box regression precision; a slice auxiliary reasoning strategy is applied, and the small target recall rate is increased through multi-scale slicing and parallel detection. The method solves the problems of strong noise interference, insufficient small target feature extraction, poor multi-scale adaptability and low convergence efficiency in the prior art.

The invention provides a construction and use method of a potential diffusion model for SAR image super-resolution. The construction and use method comprises the steps of obtaining an original SAR image and inputting the original SAR image into a real degradation model to generate a degraded SAR image; inputting the degraded SAR image into an automatic encoder to generate a structure-enhanced submerged space feature map; and inputting the structure-enhanced latent space feature map and the degraded SAR image into a potential diffusion model to generate an SAR super-resolution image. The method has the beneficial effects that a two-stage training strategy is adopted, different optimization targets are focused in stages, the training efficiency is improved, and meanwhile, the learning ability of the model to SAR image features is enhanced; sAR imaging key degradation factors are comprehensively covered, so that a generated low-resolution sample is closer to a real scene, high-quality data support is provided for model training, and model learning is prevented from being separated from an actual degradation rule; sAR specific interference such as speckle noise is effectively simulated, and the anti-noise training effect of the model is enhanced.

The invention discloses a multi-modal ultrasonic image intelligent analysis system based on deep learning, particularly relates to the field of ultrasonic image intelligent analysis, and is used for solving the problems of speckle noise and displacement artifacts caused by sound beam attenuation during deep abdomen scanning of a bedside ultrasonic person with a relatively high in-vivo quality index. Partition noise reduction is guided through pixel noise distribution associated with depth and attenuation, and deep micro-textures are reserved; the color flow velocity and the elastic displacement are anchored through the noise reduction gray scale edge, three-mode sub-pixel alignment is completed, and cross-channel deformation and drift are eliminated; the self-attention fusion network synchronously extracts texture, speed and hardness features on the unified alignment surface to realize coupling of a complete biological structure and dynamic information; the texture residual error rate and the flow rate matching degree form complementary evaluation, noise distribution and alignment grid real-time self-adjustment are driven, frame-by-frame iteration convergence is achieved, and image definition and feature consistency are continuously improved.

The invention discloses an ultrasonic image denoising method based on variational mode decomposition and local space sparse fusion, which comprises the following steps of: firstly, adaptively optimizing key parameters of variational mode decomposition by using a grey wolf optimization algorithm to realize stable and efficient decomposition of an ultrasonic image; then, classifying the modal components according to the structural features of the modal components, and implementing differentiated denoising strategies for different types of modals to separate noise and reserve useful information; after modal reconstruction, a sparse expression method based on local space information is further introduced, according to the method, accurate boundary detection is carried out through gradient vector flow and gray scale proportion analysis, self-adaptive partitioning is carried out on an image according to boundary information, and finally sparse reconstruction is carried out through double dictionaries trained for different areas. According to the method, speckle noise in the ultrasonic image can be effectively suppressed, and meanwhile, the capability of keeping the edge and detail information of a tissue structure is remarkably improved, so that the ultrasonic image with higher quality is obtained.

The invention discloses a tracking optimization method for detecting a low-speed small unmanned aerial vehicle target by a single-photon laser radar, belongs to the technical field of optical detection and target tracking, and aims to solve the problem of unstable target imaging and tracking caused by speckle noise interference in the prior art. The invention provides a speckle noise suppression method based on a vibration emission optical fiber and a space-time dynamic kernel density estimation algorithm, and the method is combined with an improved mean shift-Kalman filtering algorithm to achieve the tracking optimization of a low-speed small unmanned aerial vehicle target, and obtains an echo signal through a single-photon laser radar. Constructing a three-dimensional data matrix and reconstructing distance and intensity images through space-time filtering and kernel density estimation; and then, in combination with gray level histogram probability estimation, similarity measurement and Mean Shift iteration, target area tracking is realized, and finally, a target position is predicted and output by using Kalman filtering. The method is suitable for the fields of long-distance unmanned aerial vehicle detection and monitoring, low-altitude security defense early warning, civil airspace management and control, military anti-unmanned aerial vehicle systems and the like.

The invention belongs to the technical field of image reconstruction and classification, and particularly relates to a radar image adaptive reconstruction and classification method based on intelligent hierarchical learning, and the method comprises the steps: obtaining an original radar image and radar platform parameters, and obtaining a standardized preprocessing image; outputting a high-fidelity reconstructed image through hierarchical processing; carrying out Sobel gradient calculation to obtain a gradient image and carrying out region division, carrying out differential dynamic window local enhancement, and then carrying out global gray balance and secondary filtering to obtain an enhanced and optimized image; carrying out local / global feature extraction, carrying out weighted fusion by utilizing semantic association, and outputting a multi-layer fusion feature map; outputting a high-confidence identification result by utilizing candidate region acquisition and difficulty evaluation, dynamic network switching identification and confidence closed-loop verification; and integrating the original radar image, the high-fidelity reconstructed image and the high-confidence identification result, and outputting a structured report. According to the method, the optimal balance of radar image speckle noise suppression and detail reservation can be realized.

The invention relates to the field of intelligent image enhancement display, in particular to an AI-based ultrasonic image pathological feature intelligent extraction and image enhancement display system, which comprises a preprocessing module, a feature extraction module, an image enhancement module, an interactive display module and a database, the preprocessing module acquires an original image, performs adaptive gray normalization and speckle noise suppression, and realizes accurate processing through an adaptive gray normalization network and a feature sensing speckle denoising network; the feature extraction module fuses multi-modal features by using a cross-modal attention mechanism, calculates attention matrix operation, outputs a pathological feature probability graph and generates a pathological feature mask; the image enhancement module drives a generator to enhance the contrast of a pathological area by taking the pathological feature mask as a condition through a conditional generative adversarial network; the interactive display module compares and plays images on a display screen; according to the invention, accurate extraction and image enhancement of ultrasonic image pathological features are realized.

The invention relates to an expressway ponding recognition method based on remote sensing data and deep learning. The expressway ponding recognition method comprises the following steps: collecting single-view complex image data in a synthetic aperture radar; carrying out denoising on the single-view complex image data, removing random speckle noise, and then carrying out smooth processing, geometric correction and radiometric calibration; geocoding is carried out in combination with DEM data, and imaging geometric distortion and position offset are removed; combining the remote sensing image, labeling a data label, and constructing a data set; constructing an image segmentation model based on a U-Net network; inputting an enhanced data set into the image segmentation model to carry out model training; and inputting a radar image needing ponding identification into the image segmentation model to carry out ponding identification in the expressway road domain range. The water body classification method based on the U-Net network is constructed and verified. By making a high-quality urban water body annotation data set and introducing various data enhancement strategies, the segmentation performance of the model in a complex remote sensing image is remarkably improved.

The invention discloses a retina OCT (Optical Coherence Tomography) image denoising method based on zero sample learning. The method comprises the following steps: generating a noise independent image pair for a single noisy image by adopting a CDIS (Coherent Discrete Identifier) The method comprises the following steps of: carrying out de-noising by using an MLFSnet network (AF-MSDSConv, Aap-LSM, SA, a reconstruction module); using RMSE symmetry and consistency joint loss zero sample training; in the reasoning stage, an original image is directly input, and a same-resolution de-noising result is output. The speckle noise can be significantly suppressed and the layered structure of the retina can be retained without noise-free true values or pairwise data, so that the retina can be used while being shot.

The invention provides an SAR image denoising method based on space-frequency domain information aggregation, and the method employs a space-frequency domain information aggregation network architecture which combines the features of a space domain and a frequency domain, and extracts the space domain information and the frequency domain information of an image through a double-branch structure. And the two complementary features are fused through a double-domain aggregation module, so that speckle noise in the SAR image is effectively removed, and image details are recovered. The system specifically comprises a spatial domain information branch, a frequency domain information branch and a dual-domain aggregation module. The spatial domain branch is used for capturing local structure details, the frequency domain branch is used for extracting global context information, and the double-domain aggregation module fuses features from the spatial domain and the frequency domain to improve the speckle removing performance. The SAR image despeckling method is superior to the most advanced method in the prior art under a plurality of noise levels, and is excellent in the aspect of SAR image despeckling. According to the method, the local spatial features and the global frequency features are effectively combined, so that a better speckle removing effect is realized.

Systems and methods are provided for performing Doppler (phase-sensitive) optical coherence tomographic vibrometry measurements involving the vibrometric response of a rigid structure to an acoustic stimulus, such that the impact of speckle noise on a calculated vibrometric measure is reduced. Detected signals are processed to determine a depth range associated with the rigid structure, and to provide sampled time-dependent phase data encoded with the vibratory response of the rigid structure during application of the acoustic stimulus. The sampled time-dependent phase profile is processed to generate a vibrometric measure characterizing the rigid structure, based on non-uniformly-weighted contributions from one or more subregions within the depth region associated with the rigid structure (and optionally one or more time windows). The contribution from each subregion may be dependent on a depth-domain intensity measure associated with the subregion, where each subregion may be sufficiently small to encompass speckle-induced spatially-dependent changes.

The invention relates to the technical field of remote sensing monitoring, in particular to a method for monitoring the coastal reclamation condition through satellite remote sensing. The method comprises the following steps: acquiring SAR image data of a pair of target monitoring coast areas, and carrying out speckle noise suppression and distance Doppler terrain correction processing; according to the method, pixels in a main image and an auxiliary image in a deformation area in processed SAR image pair data are obtained, the coherence coefficient of the deformation area is calculated, and the deformation area within a set time range value is judged to be a candidate sea reclamation change suspected area according to the coherence coefficient of the deformation area and a coherence coefficient threshold value of the deformation area. According to the method, the candidate suspected area of the sea reclamation change is determined through the coherence coefficient focused on the deformation area, blind search of the whole monitoring range is avoided, the specific position of the sea reclamation change is accurately positioned, so that the possibility of misjudgment is reduced, meanwhile, the coherence coefficient focused on the deformation area can quickly exclude most deformation areas without change, and the accuracy of the sea reclamation change is improved. And the monitoring efficiency is improved.

The invention discloses a near-field electromagnetic wave imaging multi-mode noise cooperative suppression method, relates to the technical field of electromagnetic wave imaging, and aims to solve the problems that the suppression effect on speckle, stripe and Gaussian mixture noise is poor and a target structure is easy to lose in the prior art. According to the method, a complex field speckle suppression-multidirectional fringe separation-cross-channel Gaussian suppression three-stage cooperation scheme is adopted, and firstly, the amplitude and phase of a complex field image are processed through an adaptive threshold value to remove speckle noise; respectively executing horizontal / vertical ADOM filtering on the real part and the imaginary part of the complex field to eliminate stripe noise; and finally, Gaussian noise is suppressed by combining BM3D filtering and three-dimensional transform domain optimization, and robustness is improved through multi-frequency point fusion. Experiments show that the SSIM of the method is improved by 21.7% compared with that of a traditional method, the GSSIM and the PSNR are optimal, the structural features of the target can be reserved in a strong noise environment, and the method is suitable for scenes such as defect detection of near-field electromagnetic wave synthetic aperture imaging.

The invention discloses a ship target detection method based on linear non-iterative clustering. The method comprises the following steps: collecting data of a radar image ship target; performing speckle noise filtering on the data; performing superpixel segmentation on the image, performing superpixel segmentation by using an improved SNIC algorithm, and merging isolated superpixels; and carrying out CFAR detection on the superpixels to obtain a ship target, selecting background superpixels of the superpixels to be detected, assuming that the background superpixels conform to truncated GAMMA distribution and estimating parameters, and completing CFAR detection. A radar image is input into an algorithm for testing, and it is proved that the method can effectively improve the target detection accuracy of a radar image ship.

The invention discloses a physical constraint deep learning forest biomass estimation method and system. The method specifically comprises the steps of satellite-borne laser radar GEDI footprint preprocessing and quality screening; sAR radiometric calibration, terrain correction and speckle noise filtering are carried out; optical data radiation correction, atmospheric correction and geometric correction; multi-source remote sensing data space-time alignment and feature variable extraction; constructing a deep learning model fused with physical constraints; feature optimization and model reconstruction are carried out based on space-time interpretability analysis (SHAP); performing comparison and precision verification on the reconstructed final model; and generating a high-resolution annual forest biomass map based on the optimal deep learning model. The estimation result obtained by the method provided by the invention has relatively high precision and accuracy, and scientific data support and decision suggestions are provided for regional forest resource management and carbon sink capability evaluation.

The invention provides an SAR sea ice segmentation method, device and equipment based on multi-scale feature extraction fusion. The SAR sea ice segmentation method comprises the following steps: in an SAR sea ice image data imaging process, performing incident angle correction on SAR sea ice image data to obtain an SAR image product of sea ice; performing multi-scale characteristic decomposition and reconstruction on the SAR image product by adopting a deep learning network embedded with a wavelet transformation module, and performing channel enhancement in the deep learning network by adopting a residual channel attention mechanism to obtain the SAR image product after speckle noise suppression; and based on a sea ice segmentation model integrated with a lightweight adaptive extraction module and a multi-scale feature extraction module, segmenting the SAR image product after speckle noise suppression to obtain a sea ice segmentation result.

The invention discloses a diffusion-guided image cross-modal matching method. The method comprises the following steps: firstly, preprocessing an optical image and an SAR image to be matched; obtaining a coarse-to-fine multi-scale feature pair of the optical image and the SAR image and a multi-scale enhancement feature of the SAR image by using a diffusion gating feature pyramid network; on the thickest scale, an optical flow field probability distribution model is constructed through a Gaussian process matching module, and an initial optical flow field and a confidence map are output in combination with a Transform decoder; then, a multi-scale convolution refining network is adopted, displacement embedding and local correlation calculation are combined, step-by-step optimization of the optical flow field and confidence is achieved through iterative updating, and finally a fine matching result is output. According to the method, speckle noise in the SAR image is effectively suppressed and geometric distortion is compensated through feature enhancement guided by the diffusion model, Gaussian process modeling, Transform decoder prediction and a multi-scale progressive refinement strategy, and the robustness and precision of cross-modal image matching in a complex scene are remarkably improved.

The present invention provides a method and system for locating shoulder cysts based on magnetic resonance imaging (MRI) images, which relates to the technical field of medical image processing. The method includes: acquiring multiple MRI images; respectively constructing a target detection model for the humeral head region and a target detection model for cysts based on a deep learning algorithm; inputting the MRI images into the target detection model for the humeral head region for detection and outputting an image of the humeral head region; inputting the image of the humeral head region into the target detection model for cysts for detection and outputting an image of the cyst region; and determining the position of the cyst according to the center point coordinates of the cyst region image through a spatial positioning algorithm. The present invention reduces the influence of MRI images by speckle noise and echo perturbation, as well as the dependence on doctors' cognitive abilities and clinical experience, improves the image quality, makes the difference between cysts and normal tissues more obvious, and avoids confusing cysts with fluid accumulation caused by rotator cuff injuries.

The invention belongs to the technical field of B-mode ultrasound diagnosis, and particularly relates to an animal body fat detection method and an animal body fat detection model generation method and device.The detection method comprises the steps that animal body fat B-mode ultrasound images are collected, and data preprocessing is conducted; the B ultrasonic image is converted into an image of a CT / MRI style; according to the converted CT / MRI style image, scanning lines with clear boundary lines of a fat layer and a muscle layer are obtained; calculating the pixel number of the upper and lower edges of the fat layer; and the actual fat thickness is calculated according to the conversion ratio of the detection device. The generation method of the detection model comprises the steps of data preparation, model building, model training and model deployment. According to the method, the B-mode ultrasound image is converted into the CT or MRI image through the cyclic generative adversarial network technology, speckle noise and artifacts in the B-mode ultrasound image are effectively removed, the boundary definition of a fat layer and a muscle layer is remarkably improved, and the fat thickness measurement error is greatly reduced.

This application relates to a method, apparatus, device, and medium for few-sample SAR target recognition. The method includes: dividing SAR samples into support and query sets based on a meta-learning framework; converting the original SAR image into a semantic map and an attribute scattering center topology map; extracting three types of features and calculating class prototypes through three parallel feature branches; obtaining the confidence score of each branch using the Softmax function; assigning teacher and student branches according to the confidence score; minimizing KL divergence to achieve dynamic knowledge transfer; weighted fusion of predicted log odds followed by Softmax output; and backpropagation to optimize the network. This method, through multi-prototype fusion and interactive distillation, mitigates the problems of speckle noise and high intra-class variance in SAR images, maintaining high robustness and generalization ability even in extreme data-scarce scenarios.

The invention relates to a near-to-eye display optical system based on a laser light source and an LCOS modulation module, and belongs to the technical field of near-to-eye display. The invention aims to solve the technical problems that the existing near-to-eye display scheme is difficult to overcome speckle noise, chromatic dispersion, low luminous efficiency, overlarge size and the like at the same time. The system sequentially comprises a multi-mode laser light source module used for emitting low-initial speckle contrast imaging laser beams; the optical preprocessing module comprises a collimating lens group, a beam expanding prism and a phase perturber; the LCOS modulation module is used for simultaneously carrying out amplitude modulation on the light beam according to the image signal so as to load an image and carry out phase modulation so as to adjust wavefront, and reflecting and outputting an image light beam; the coupling module is used for carrying out light path coupling on the image light beam; and the near-to-eye display module is used for transmitting and projecting an image to human eyes. The system is mainly suitable for augmented reality glasses and other head-mounted display devices, and can realize high-image-quality and miniaturized near-to-eye imaging.

The invention discloses an SAR (Synthetic Aperture Radar) image change detection method combining convolution and mixed attention. The SAR image change detection method comprises the following implementation steps of: firstly, generating a difference chart for two SAR images by using a composite neighborhood intensity difference method; then, a hierarchical FCM clustering algorithm is used for carrying out pre-classification processing on the difference image, a pseudo label matrix is generated, variable and invariable high-probability sample pixels in pseudo label pixels are selected, spatial positions of the pixels are extracted, and on the pixels of the corresponding spatial positions of the two original SAR images, a pseudo label matrix is generated; pixel blocks with the pixel points as the centers are taken as a training set, and pixel blocks with all the pixel points as the centers are extracted from the two original SAR images to serve as a test set; and then training a neural network combining convolution and mixed attention by using the training sample set, and then carrying out change detection analysis on a test set by using the trained network to generate a final change detection result graph. The method has clear advantages in the aspects of SAR speckle noise suppression and change detection precision.

This invention provides a method for selecting strong scattering points in space-based ISAL systems by incorporating multi-dimensional features, relating to the field of ISAL image processing. This invention employs a multi-stage fusion processing strategy to form a complete link from image preprocessing to the final output of strong scattering points: pixel replication and extension are used to process convolutional boundary regions, significantly reducing boundary amplitude distortion caused by traditional zero-padding or periodic extension, and improving the signal-to-noise ratio of edge regions in the filtered amplitude map; the dynamic threshold generation process works synergistically with the preceding Gaussian kernel convolution preprocessing to suppress speckle noise while preserving the true amplitude characteristics of weakly scattering targets; amplitude-weighted averaging is used to calculate the centroid coordinates within the connected domain, overcoming the energy diffusion problem caused by the ISAL system's point spread function; transient interference points are eliminated based on the peak-to-mean ratio of multi-frame amplitude sequences, and the spatial distribution characteristics of the centroid position's spatial amplitude value and density constraints are combined to ensure that the output set of preferred scattering points simultaneously satisfies temporal stability, spatial saliency, and uniform distribution.

This invention belongs to the field of automatic target recognition security technology for synthetic aperture radar (SAR), and discloses a method and application for generating adversarial examples for SAR images. The method includes: generating a more stable gradient prediction look-ahead direction by combining the gradient and cumulative momentum of the current data point to reduce the influence of speckle noise; then, effectively sampling the area around the data point and adjusting its weights according to its confidence score to reduce the negative impact of semantic inconsistency of the sampled data points and further stabilize the gradient update. This invention considers the problem that SAR images are prone to excessive changes in gradient direction during gradient calculation due to coherent noise. By combining the gradient of the current data point and cumulative momentum to stabilize the gradient prediction direction, and by sampling the surrounding data points through confidence scores to achieve a more stable adversarial example update direction, it can well fit the characteristics of SAR data, thereby generating high-quality adversarial examples in real-world black-box scenarios.

The application discloses a near-field electromagnetic wave imaging multi-modal noise cooperative suppression method, relates to the technical field of electromagnetic wave imaging, and aims to solve the problems of poor suppression effect of spot noise, stripe noise and Gaussian mixed noise and easy loss of target structure in the prior art. The method adopts a three-stage cooperative scheme of "complex domain spot suppression-multi-direction stripe separation-cross-channel Gaussian suppression", first removes the spot noise by adaptively processing the amplitude and phase of the complex domain image through a threshold value; then eliminates the stripe noise by performing horizontal / vertical ADOM filtering on the real part and the imaginary part of the complex domain, respectively; finally, combines BM3D filtering and three-dimensional transform domain optimization to suppress Gaussian noise, and improves the robustness through multi-frequency point fusion. Experiments show that the SSIM of the method is improved by 21.7% compared with the traditional method, the GSSIM and PSNR are optimal, the target structure features can be reserved in a strong noise environment, and the method is suitable for defect detection and other scenes of near-field electromagnetic wave synthetic aperture imaging.