342 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 discloses a synthetic aperture quantitative phase imaging method based on mixed digital holography and Fourier lamination, which comprises the following steps of: installing a programmable LED (light-emitting diode) array which is annularly arranged on an off-axis digital holography system for providing inclined illumination of angle transformation, obtaining accurate low-frequency information of an object which contains noise through digital holography, and taking the information as initial estimation; and then, object high frequency is reconstructed through a Fourier lamination algorithm based on intensity measurement, so that synthetic aperture quantitative phase imaging is realized. According to the invention, the accurate low frequency provided by holography is used as initial estimation, so that the accuracy of phase recovery and wavefront aberration reconstruction is ensured, a matched illumination condition does not need to be strictly met, and meanwhile, the number of intensity graphs required by Fourier lamination is reduced; the non-interferometric phase recovery method based on Fourier lamination improves the imaging resolution to an incoherent diffraction limit, and effectively solves the inherent speckle noise problem in digital holography.

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 relates to the technical field of radar remote sensing image processing, in particular to a single-polarization synthetic aperture radar change detection method and device.The method comprises the steps that initial single-polarization SAR images of front and back time phases are preprocessed to obtain a final image, a multi-scale feature difference chart is calculated, pseudo tags are generated through fuzzy C-means clustering, and the final image is obtained; a UNet coding and decoding structure is used as a basic framework, a double-branch convolutional neural network structure is designed, an input layer, a convolutional layer, a pooling layer, a full connection layer and an output layer are adopted to form a twin convolutional neural network, residual connection is established between an input tensor and an output tensor after network processing, a change detection model is generated, and a change detection result is obtained. And outputting a detection result of the single-polarization synthetic aperture radar by using the change detection model. Therefore, the problems of low accuracy of a change monitoring result and the like caused by the defects that the processing capacity of speckle noise in an SAR image monitoring method is limited and a static threshold segmentation method is difficult to adapt to the global situation in the prior art are solved.

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 an image processing system and method, electronic equipment and a computer storage medium, and the method comprises the steps: receiving a support target image and a query target image through a feature embedding network, and carrying out the feature extraction of the support target image and the query target image, so as to obtain an output feature; on the basis of the output features, obtaining feature representations of different weights of the support target image and the query target image at different positions of respective images; initial features of the metric classification network are obtained through the output features and the feature representations of different weights; and obtaining the similarity between the support target image and the query target image based on the initial features of the metric classification network, and classifying the query target image. According to the method, the WAAPNet model is designed, and the WAA module is introduced, so that more detailed and efficient features can be extracted while speckle noise of the SAR image is suppressed, and the complex features of the SAR image can be extracted more sufficiently and effectively by the network; the algorithm complexity of the model can be reduced, and the network operation rate is improved.

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 belongs to the technical field of synthetic aperture radar automatic target identification security, and discloses an SAR image adversarial sample generation method and application, and the method comprises the steps: generating a more stable gradient prediction look-ahead direction through combining the gradient and accumulated momentum of a current data point, so as to reduce the influence of speckle noise; then, performing effective sampling on the periphery of the data point, and adjusting the weight of the data point according to the confidence score of the data point so as to reduce the negative influence of semantic inconsistency of the sampled data point and further stabilize gradient updating; according to the method, the problem that the gradient direction of an SAR image is easy to change too much in the gradient calculation process due to the existence of coherent noise is considered, the gradient prediction direction is stabilized by combining the current data point gradient and the accumulated momentum, and a more stable adversarial sample updating direction is achieved by sampling surrounding data points through confidence; and SAR data characteristics can be well fit, so that an adversarial sample with relatively high quality can be generated in a real black box scene.

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 belongs to the technical field of image processing and target detection, and relates to a CNN and GNN fusion-based network architecture and an image detection method. The network architecture comprises a convolutional neural network and a graph neural network encoder. The convolutional neural network receives an input image, and outputs N local feature maps after at least two rounds of convolution and local information extraction. The graph neural network encoder receives the N local feature maps, M multi-scale feature maps are output after primary convolution, n1 global information capture, secondary convolution and n2 global information capture, the M multi-scale feature maps pass through the PAFPN feature fusion network and the detection head, and a final result is output. The graph neural network encoder is configured with a convolution branch and a graph attention branch, the convolution branch is used for retaining original information in the local feature graph, and the graph attention branch is used for capturing global information in the feature graph. The global information of the feature map can be enhanced, and the purpose of suppressing interference factors such as speckle noise and complex geographical environment is achieved.

The present invention provides an optical waveguide device that ensures a large area of light available for image display, enables easy correction of deviations of light spots and accompanying cost reduction, and can display images with reduced speckle noise. The present invention further provides a light source module including a light source and the optical waveguide device. The optical waveguide device (1) allows a plurality of light beams of different wavelengths to be input to and propagate through corresponding waveguides (4b, 4g, 4r) and then be output. Input ports (3b, 3g, 3r) of the waveguides are spaced apart from each other so as not to receive light beams of improper wavelengths; at least one of the waveguides is curved; and output ports (5b, 5g, 5r) of the waveguides are located close to each other. Thus, the optical waveguide device enables cost reduction and can display images with reduced speckle noise.

The invention provides a low-noise speckle interference system and method based on a polarization camera, and relates to the technical field of speckle interference. According to the low-noise speckle interference system, a polarization camera and a speckle interference system are combined, so that a single speckle interference pattern comprises four different polarization state channels, in the image processing process, the complex amplitude difference before and after deformation is averaged, information fusion of the four channels is achieved, and the speckle interference pattern is obtained. The standard deviation of the speckle interference noise can be reduced to the original # imgabs0 #, so that the suppression effect of the speckle interference noise is greatly improved; in the aspect of data processing, only one-step averaging operation is added compared with a traditional speckle interference technology, the influence on the overall measurement speed is small, and the flexibility is excellent; in addition, by reducing the speckle noise standard deviation, the requirement of the speckle interference technology for a filtering algorithm is reduced, and the practicability of the speckle interference technology is improved.

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 the technical field of image enhancement, in particular to an optical remote sensing cloud and cloud shadow removal method, system and device and a storage medium. In order to solve the technical problem of poor image cloud removal effect in the prior art, the method comprises the following steps: firstly, respectively preprocessing an optical image and an SAR image, solving the problem of speckle noise existing in the SAR image, and strengthening high-frequency information in the optical image; then, fusing the penetrating power of the SAR enhanced image and the spectral information of the optical enhanced image, and carrying out channel and space coupling attention mechanism processing on a fusion result to obtain a semantic complementary feature image; and finally, performing structure reconstruction processing on the semantic complementary feature image for several times, retaining high-frequency details and low-frequency semantics, improving image reconstruction quality, and performing convolution processing on the obtained structure reconstruction feature image to obtain a cloud noise removed reconstruction image. The method is applied to the field of image cloud and cloud shadow removal, the removal effect is good, and the image recovery quality is high.

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.

Provided are an illumination device and a projector, in which speckle noise of a projected image is reduced in a projector provided with an illumination device. An illumination device is provided with: a first light source that emits first light having a first wavelength; a second light source that emits second light having a second wavelength different from the first wavelength; a light combining member that combines the first light and the second light and emits combined light; a first diffusion device having: a first substrate having a first diffusion surface that diffuses and emits incident synthesized light; and a first drive device that rotates the first substrate. A light-condensing element that is disposed between the light-synthesizing member and the first diffusion device and that condenses the synthesized light on a first diffusion surface of the first diffusion device; a collimating element that parallelizes the combined light emitted from the first diffusion device; and a second diffusion device that is disposed between the light collecting element and the first diffusion device, and that changes the state of the spot of the synthesized light on the first diffusion surface over time.

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 discloses a synthetic aperture radar image target detection method and device, equipment and a storage medium. The method comprises the following steps: acquiring a to-be-detected SAR image; performing denoising processing on the SAR image by using a denoising network; establishing an integrated target detection model which comprises a denoising network and a subsequent open vocabulary target detection model, and training the integrated model by using an improved bounding box regression loss function constructed based on angular point distance and side edge overlapping information; and inputting the denoised SAR image and text prompt into the trained open vocabulary target detection model for feature extraction and target detection, and outputting a detection result. According to the method, the denoising technology and open vocabulary target detection are combined, the problem that speckle noise is not fully processed in the current SAR target detection field is solved, meanwhile, the limitation of traditional target detection on fixed categories is overcome, the target detection precision is improved, and the applicability and robustness of the model are improved.

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 discloses a diffractive optical element design method based on automatic differentiation and a free-form surface technology, and belongs to the field of optical design. The implementation method comprises the following steps: expressing phase distribution corresponding to a diffractive optical element as superposition of multiple layers of local free-form surfaces; calculating the intensity distribution of the input light field which is modulated by the diffractive optical element and then propagated to a target plane through a scalar diffraction theory; and in combination with automatic differential and multi-scale optimization strategies, phase distribution is optimized by adopting a gradient descent method. The phase distribution of the diffractive optical element is represented by a continuous and smooth free-form surface, so that the phase surface is always continuous and smooth in the optimization process, the curvature distribution of the curved surface is further restrained in the optimization process, and the processing difficulty of the diffractive optical element is reduced; in the optimization process, random phases and phase singular points are avoided, and speckle noise is suppressed; the superposition representation phase distribution of multiple layers of local free-form surfaces is adopted, and a multi-scale optimization strategy is matched, so that the dependence on an initial phase can be eliminated, and the convergence of iteration is ensured.

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.