712 results about "Phase image" patented technology

The invention relates to the technical field of image data measurement and analysis, in particular to a method and a system for measuring slope deformation in a dangerous mountainous area based on image data. The method comprises the following steps: acquiring high-resolution image acquisition data and GNSS auxiliary data of the slope of the hard mountain area; correcting the high-resolution image acquisition data to obtain corrected slope image acquisition data; performing local feature extraction and matching of each time phase image on the corrected slope image acquisition data to obtain slope preliminary matching point set data; and performing mismatching elimination on the slope preliminary matching point set data to obtain transformation matrix data between the slope images. According to the method, high-resolution image acquisition and GNSS data are combined, through correction, registration, three-dimensional reconstruction and optical flow analysis, slope deformation of the hard mountainous area is accurately obtained and analyzed, and efficient deformation monitoring and visualization results are achieved.

The invention discloses a remote sensing image change detection method based on spatio-temporal feature interaction and feature difference enhancement, and belongs to the technical field of remote sensing image processing, and the detection method comprises the following steps: constructing a change detection data set containing a dual-temporal remote sensing image, respectively extracting multi-scale features through an encoder, and obtaining a change detection data set; inputting the data to a spatio-temporal feature interaction module to obtain interacted dual-time-phase features; fusing the interacted double-time-phase features and inputting the fused double-time-phase features into a decoder to generate four groups of same-resolution features with different semantic hierarchies; performing difference enhancement on the four groups of features through a feature differentiator, then performing channel splicing and fusion, and outputting a change detection result; and training the model by using the training set, adjusting and optimizing, and evaluating the precision. The beneficial effects of the invention are that the method can effectively capture the space-time dependency relationship between the double-time-phase images, enhances the discrimination capability of a change region, and meets the requirements of high-precision change detection in a complex scene.

The invention provides a mining area deformation monitoring and risk assessment method, which comprises the following steps of: carrying out registration, de-leveling, filtering and phase unwrapping on acquired image data through InSAR (Interferometric Synthetic Aperture Radar), generating a coherence graph, a filtering interference graph and an unwrapping phase graph, carrying out phase decomposition by using a singular value algorithm, separating a deformation phase, an atmospheric delay phase and noise, and carrying out risk assessment. The method comprises the steps of expanding discrete deformation points into a continuous deformation field through Kriging interpolation, carrying out deformation grading by taking a deformation rate, an accumulated settlement amount and a deformation gradient as core indexes, identifying a deformation high-risk area, arranging measurement robot monitoring points in the high-risk area, and constructing a risk assessment model in combination with real-time deformation data of a measurement robot and geological parameters. And evaluating the risk grade of each part of the mining area through the dynamic risk index model, and triggering graded early warning. According to the method, the InSAR and measurement robot technology is combined to solve the data fusion problem of large-range deformation monitoring and local high-precision monitoring, and the dynamic risk assessment precision under the complex terrain of the mining area is improved.

The invention discloses an intelligent change detection method for non-aligned two-time-phase images of an inspection unmanned aerial vehicle, and relates to the technical field of unmanned aerial vehicle image processing and change detection, and the method comprises the steps: obtaining two-phase images collected by a low-altitude unmanned aerial vehicle under a fixed route and same sensor parameters; a lightweight registration model is constructed and trained, feature point matching is utilized to predict matching point pairs, a homography matrix is calculated, and accurate registration of non-aligned images is achieved; and constructing and training a change detection model based on image pair interaction feature fusion, analyzing the aligned image after registration, and outputting a change information binary image. The method can effectively solve the problem of non-alignment caused by position and angle differences during two-time-phase image acquisition of an unmanned aerial vehicle, and the technical problems of low precision, poor robustness and insufficient calculation efficiency of a traditional method in change detection, effectively improves the automation level of low-altitude safety monitoring of ground highways and railways, reduces the maintenance cost, and improves the safety of the unmanned aerial vehicle. The important application value is realized.

The invention relates to a remote sensing image change detection method and equipment based on multi-scale difference feature fusion, and belongs to the technical field of remote sensing image processing. According to the invention, a multi-attention feature enhancement module is designed to carry out adaptive weight adjustment and enhancement on a dual-time-phase image and differential information thereof, meanwhile, a cross-scale residual fusion module is adopted to carry out unified fusion on features from different scales, and fused global change features are input to a classification head to carry out change detection. According to the method, the details and the overall structure of the ground object change area are accurately captured, so that the accuracy and the robustness of change detection are effectively improved, and better detection performance can be obtained under the condition that the number of training samples is small.

The invention relates to an optical phased array phase calibration method and device, an electronic device and a storage medium, and is applied to the field of optical detection.The optical phased array phase calibration method comprises the steps that a to-be-calibrated phase image is input into an optical phased array system, and a random disturbance phase image corresponding to the to-be-calibrated phase image is obtained; the random disturbance phase image is obtained by performing phase disturbance within a preset range on the to-be-calibrated phase image. Inputting the to-be-calibrated phase image and the random disturbance phase image into a target convolutional neural network model, and determining a target phase error of the to-be-calibrated phase; the target convolutional neural network model is obtained by training a preset convolutional neural network model based on the far-field training image pair; and calibrating the initial emission electric field of the to-be-calibrated phase image based on the target phase error. According to the invention, the problem that the accuracy of phase error calibration of an existing optical phased array needs to be improved is solved.

The invention discloses a remote sensing image change detection method based on a lightweight staggered structure and coarse and fine granularity fusion. The method comprises the following steps: 1, preprocessing an image in a remote sensing image change detection data set; 2, constructing a network model M2M-LINet, and initializing a hyper-parameter; the M2M-LINet comprises a coarse-grained positioning stage, a detail focusing stage and a decoding prediction stage, wherein in the coarse-grained positioning stage, coarse-grained feature mapping of an input double-time-phase image is obtained through a CALM module, and change information is preliminarily positioned; the detail focusing stage comprises an LCB module and an LTB module which are combined in a staggered manner; in the decoding prediction stage, edge features of the image are enhanced through an edge perception enhancement module EAEM, and decoding prediction is carried out through an LTB module and a convolution prediction head; 3, the M2M-LINet is trained and verified, parameters of the M2M-LINet are adjusted and optimized, and a network model M2M-LINet with the optimal parameters is obtained; and 4, predicting a detection result by using the M2M-LINet with the optimal parameter. According to the method, the change detection precision of the remote sensing image is improved with low calculation cost, and small target and boundary change can be accurately detected.

The invention discloses a method and a device for correcting laser focusing aberration in a transparent material, and belongs to the technical field of laser processing. The method comprises the steps that a machining light path containing an object plane, an aspheric reflector and a 4F device is built in Zemax, the surface type of the aspheric reflector is optimized with the minimum focus aberration as the target, and a rise table is derived; an optical path difference and a laser phase are calculated through Matlab, and a phase diagram is generated; and an actual machining light path is built, and a phase diagram is loaded to achieve low-aberration machining. Simulation optimization and phase modulation are combined, aberration caused by refractive index difference is effectively counteracted, wavefront errors are reduced by 58%, the thickness of a machining damage layer is reduced by 70%, the method is suitable for various transparent materials, the machining precision and the material utilization rate are improved, and the method is suitable for high-precision laser machining scenes.

The invention discloses a cultured seaweed identification method and system based on time sequence remote sensing and morphological constraint, and the method comprises the steps: obtaining and preprocessing an original multispectral remote sensing image, forming a multispectral remote sensing image set, and generating a water body range mask; under the constraint of a water body range mask, synthesizing each time phase image, enhancing the texture through local maximum filtering, calculating a seaweed light absorption index, and generating a feature enhanced image; performing threshold segmentation on the feature enhanced image, extracting and vectorizing a potential seaweed pattern spot raster image, and calculating morphological indexes such as area and firmness of each vector pattern spot; and screening the vector pattern spots based on a preset area and a firmness threshold, and outputting a cultured seaweed distribution result. According to the method, the self-defined index and the morphological constraint are combined, the recognition precision of regular form targets such as culture rafts is effectively improved, area time sequence change analysis can be generated, and an automatic and high-precision technical scheme is provided for dynamic monitoring of culture seaweed resources.

The invention relates to the technical field of remote sensing image processing, and particularly discloses a photovoltaic power station remote sensing identification method and system based on time constraint, and the method comprises the steps: reading image data, and obtaining projection parameters and geographical transformation parameters; processing the image data by a minimum-maximum normalization method; selecting a reference year, acquiring reference year photovoltaic power station vector data processed by a normalization method, converting image data and vector data processed by the reference year normalization method into the same coordinate system according to projection parameters and / or geographical transformation parameters, rasterizing the vector data, and generating a binary mask consistent with the resolution of the image data; and by taking the binary mask as a label, designing a deep learning network structure to train image data, and outputting a predicted binary mask. According to the method, the uncertainty of single-phase image classification is remarkably reduced, and the stability and reliability of a multi-phase classification result are ensured.

The invention relates to a method (300) for detecting damage (201) to a part (200) made of conductive material, the damage (201) being a burn or a crack, the part (200) being heated (310) by induction by injecting an energy pulse. The method comprises receiving (330) a temporal sequence of thermal images of the part after heating the part (200), and the temporal signal of the intensity of each pixel of the thermal image is used to construct (370) a phase image of the part by means of a discrete Fourier transformation of the temporal signal.

The invention provides a water chlorophyll a concentration inversion method and system based on multi-modal data and a lightweight model, and relates to the technical field of water environment remote sensing evaluation. The method comprises the following steps: firstly, acquiring a Gaofeng No.5 satellite remote sensing image, a sentinel No.3 satellite image and ground actual measurement data, and completing image preprocessing and water body pixel extraction; constructing a hyperspectral index and an aquatic vegetation index, and fusing the hyperspectral index and the aquatic vegetation index with the water body temperature, the pH environmental factors and the spectral reflectivity to form a multi-dimensional feature sample set; a core feature subset is obtained through random forest and XGBoost coupling feature selection, and a lightweight student model is trained based on knowledge distillation; and constructing a to-be-predicted feature sample for the to-be-predicted time phase image and the environment factor, inputting the to-be-predicted feature sample into the lightweight student model to obtain a chlorophyll a concentration predicted value, and generating a spatial distribution map and a quality control map layer. According to the invention, high-precision, low-redundancy and efficient deployment chlorophyll a concentration inversion is realized.

The invention relates to the technical field of minimally invasive ablation, in particular to a liver tumor microwave ablation postoperative contraction deformation prediction method, which comprises the following steps: performing unified standardization processing on different periods of liver MRI images; accurately marking a preoperative liver tumor area, a postoperative ablation area and a postoperative liver tumor ghost area of the liver MRI image to obtain masks of the corresponding areas; designing a distance perception function as an attention parameter of different areas of the liver MRI image; constructing a multi-sequence distance guide complementary network model; selecting a loss function; selecting an elastic registration method to perform pre-operation and post-operation registration; and calculating an ablation safety boundary through the ablation area mask and the adjusted tumor mask. The liver tumor microwave ablation postoperative curative effect evaluation accuracy can be effectively improved, and a foundation is laid for formulating a subsequent treatment plan of a patient.

Snapshot phase-shifting diffraction modules and associated systems and methods are described that enable high spatial and temporal resolution phase imaging with high immunity to environmental factors such as vibrations and temperature changes. One example optical diffraction phase module includes a polarization grating to produce two circularly polarized light beams with opposite polarizations, a first lens to receive the two circularly polarized beams, and a spatial filter positioned at a focal plane of the first lens. The spatial filter includes two openings, one to spatially filter one of the two circularly polarized light beams, and another opening to allow another circularly polarized light beam to pass. The module also includes a second lens to focus the received light onto an image plane and to enable a phase measurement based a plurality of interferograms. The phase module can be incorporated into a microscope system that operates a reflection or a transmission mode.

The invention relates to the technical field of optical precision interference measurement, in particular to a correction method for projection distortion detection of a primary mirror of a dynamic interferometer and a related product, and the method comprises the steps: obtaining a first coordinate and a second coordinate; determining a zero distortion point; obtaining an ideal imaging point set and an actual mapping point set after system projection through ray tracing; fitting to obtain a distortion function representing the distortion of the optical system; performing point-by-point correction on a phase diagram obtained by interference measurement, mapping pixel coordinates of the phase diagram into workpiece coordinates, obtaining interference measurement data corresponding to the actual surface of the primary mirror, and completing distortion correction; according to the method for correcting the surface shape projection distortion during interference measurement of the high-precision primary mirror of the large-aperture dynamic interferometer, the problem that in the process of assembling, repairing and developing the large-aperture Twyman type dynamic interferometer, the projection distortion effect of a detection system causes the loss of an accurate matching relation between a workpiece coordinate system and a detection coordinate system is solved.

The invention discloses a method for performing micro-hemorrhage focus recognition on brain images by using a deep learning algorithm, which comprises the following steps of: performing brain skull removal operation on magnetic sensitive weighted imaging images and phase images of a plurality of patients by using an HD-BET tool to obtain an intracerebral region map in a 2D image format; the method comprises the following steps of: firstly, firstly, carrying out brain detection on a brain region map, then, carrying out mesh division on each brain region map, dividing the brain region maps into mesh region maps with the same size, and then, training a YOLOv8 target detection model and a 3D-CNN network model by utilizing the mesh region maps, so that micro-hemorrhagic spots can be quickly predicted through the YOLOv8 target detection model, and then false positive micro-hemorrhagic spots are eliminated through the 3D-CNN network model. And a final micro-bleeding point detection result is obtained.

The invention provides a random forest shallow sea sediment classification method based on multi-temporal remote sensing image fusion, and relates to the technical field of sediment information extraction. Comprising the following steps: 1, collecting and preprocessing multi-temporal image data to obtain remote sensing reflectivity; 2, the water depth of each single-time-phase image is inverted, and the optimal water depth is obtained; 3, calculating bottom reflectivity characteristics of blue and green wave bands based on the optimal remote sensing image; 4, respectively calculating topographic features and spectral features based on the optimal water depth and the optimal remote sensing image; and 5, in combination with the bottom reflectivity features, the topographic features and the spectral features, carrying out random forest feature optimization and classification model training, and generating a substrate classification result. On the basis, the method solves the problems that an existing remote sensing image substrate classification method is insufficient in feature consideration, noise in a single-time-phase image can cause low classification precision, and therefore negative effects can be generated on accurate acquisition of substrate information.

The invention provides a self-adaptive reverse multiple exposure phase fusion technology, and relates to the field of high-dynamic-range three-dimensional reconstruction. The method includes the following steps that firstly, a saturated pixel mask matrix is obtained by judging pixels of a high-reflection area, then uniform grey-scale maps are selected step by step through a gradient adjustment method to obtain the optimal local projection intensity, and local coding and decoding are conducted on the high-reflection area through the optimal projection intensity pattern. And replacing an invalid phase value in the high-SNR image with a local phase value under the optimal projection intensity through an edge matching and phase fusion technology, and obtaining high-quality complete three-dimensional imaging information through coordinate mapping after a complete phase diagram is obtained. According to the method, the optimal projection intensity is adaptively determined through a small number of patterns, meanwhile, the high SNR of imaging patterns is guaranteed, and high-precision complete three-dimensional imaging is carried out on an HDR object. Experimental results show that in a traditional method, a large number of point clouds are lost in a saturated pixel area, the imaging integrity and the measurement precision cannot achieve the normal imaging effect, and the algorithm of the invention shows high integrity and high point cloud density in a dark area and a high-reflection area.

The invention discloses a logic chip mask defect detection method, and relates to the technical field of integrated circuit manufacturing, and the method comprises the following steps: S1, constructing a scattering response model of sub-wavelength inorganic particles on the surface of a mask, and constructing a scattering response model of sub-wavelength inorganic particles on the surface of the mask under a set multi-angle polarized light incidence condition; non-linear disturbance influence of inorganic particles with different particle sizes on the reflected light phase in each incident polarization state is simulated, and a scattering response template containing standard phase disturbance characteristics is generated; and S2, based on a scattering response template, carrying out pixel-by-pixel matching analysis on the acquired multi-angle polarization phase diagram on the surface of the mask, identifying a local area matched with the template, and marking the local area as a suspected scattering interference area. According to the method, through construction of the scattering response template and multi-angle polarization consistency analysis, accurate distinguishing of artifacts and real defects is achieved, the recognition precision and stability under complex interference are improved in combination with structure retention type phase reconstruction and dynamic threshold recognition, and mask quality control and photoetching yield improvement are facilitated.

The invention discloses a phase deflection measurement method and device based on four-step phase shift, and belongs to the technical field of computer vision and precision measurement, and the method comprises the steps: S1, classifying patterns needing to be projected according to types, and fusing the patterns into a color image; s2, sequentially projecting color images, and simultaneously collecting a pattern modulated and reflected by an object to be measured to obtain a reflection pattern; s3, carrying out channel separation processing on the reflection pattern, recovering the reflection pattern into an original grey-scale map, and respectively obtaining all phase patterns and Gray code patterns in the horizontal direction and the vertical direction; s4, performing phase calculation and Gray code auxiliary positioning according to the phase pattern and the Gray code pattern to obtain an absolute phase; and S5, mapping the absolute phase into a depth value, and recovering the three-dimensional shape of the surface of the object to be measured in combination with calibration parameters of a camera-projection system. The time of each measurement period is shortened, the system delay in the projection and acquisition process is reduced, and the real-time response capability of the whole set of detection system is improved.

The invention provides a structured light projector calibration method and system and a medium, and the method comprises the steps: projecting a multi-frequency stripe pattern to a target region based on an MEMS micromirror array module, obtaining a distorted image sequence, and carrying out the phase decoding, and obtaining a phase diagram corresponding to each frequency; resolving the phase diagram based on a phase shift method and an optical path triangulation principle to obtain a preliminary three-dimensional point coordinate; constructing a neural network model and a calibration sample set, obtaining a real three-dimensional coordinate by using a standard calibration plate or a known curved surface, and calculating the deviation between the initial three-dimensional point coordinate and the real three-dimensional coordinate to obtain a training sample set; and training the neural network model according to a training sample set based on an Euclidean distance loss function, outputting a three-dimensional offset compensation amount according to the neural network model, correcting the initial three-dimensional point coordinates, and realizing an end-to-end error self-compensation mechanism through the neural network model, so that deviation compensation is effectively performed on the three-dimensional point coordinates, and the accuracy of the three-dimensional point coordinates is improved. And the three-dimensional reconstruction precision is improved.

The invention relates to the field of image processors and biological medicines, and discloses an NK cell activity rapid detection method based on image processing. The method comprises the following steps: acquiring an unmarked time sequence phase image sequence of an NK cell and target cell co-culture system; performing cell instance segmentation to track individual cells; extracting a morphological dynamic characteristic parameter set of the target cell, wherein the morphological dynamic characteristic parameter set comprises a volume change rate, a phase gradient entropy, a cytoplasm phase fluctuation frequency and a nuclear region phase mean value; inputting the parameters into a pre-trained death state discrimination model, and outputting a death probability; and calculating a killing efficiency index based on the death probability evolution curve, and judging the activity level of the NK cells. The system comprises a phase image acquisition unit, a cell segmentation unit, a feature extraction unit, a death judgment unit and an activity judgment unit. Through unmarked imaging and deep learning fusion analysis, high-precision, real-time, quantitative and ultra-early NK cell activity evaluation is realized, and the method is suitable for clinical instant inspection and immunotherapy monitoring.

The invention relates to the technical field of flaw detection, and discloses a thermal insulation decorative plate appearance flaw detection method and system based on image recognition, and the method comprises the steps: executing optical excitation operation on the surface of a thermal insulation decorative plate through a periodic light pulse; acquiring an infrared image time sequence corresponding to the surface of the thermal insulation decorative plate according to the transient optical response sequence, and performing time sequence analysis on each pixel point in the infrared image time sequence; converting the gray time sequence change curve into a frequency domain change curve, and calculating the optical phase lag amount of each pixel point according to the frequency domain change curve; performing pixel-level comparison according to the optical phase lag and a reference phase diagram corresponding to the target thermal insulation decoration sample, generating a phase difference distribution diagram, and identifying a connected region with abnormal phase in the phase difference distribution diagram; recognizing surface flaws of the thermal insulation decorative plate according to the communication area, and determining a flaw detection result of the thermal insulation decorative plate according to the surface flaws. According to the invention, the accuracy of appearance flaw detection of the thermal insulation decorative plate can be improved.

The invention relates to the technical field of sports biomechanics, in particular to an automatic track and field action recognition and capture method based on image data, which comprises the following steps: synchronizing a visible infrared image, a polarization image and an event stream according to a mutual information criterion and generating a polarization phase diagram; inputting a polarized neural radiation field network and a skeleton graph neural network based on Lie group message passing, and alternately optimizing to obtain a continuous voxel light field and a joint rotation sequence; performing Vietoris-Rips persistent homology and third-order path signature on the sequence to extract topological path features, encoding the topological path features into a pulse sequence to drive a liquid state machine, and generating a semantic confidence flow by combining bone potential tensor and action text embedding; and constructing a saliency curve by using the voxel light field time derivative, the neuromorphic difference, the semantic complementary value and the skeleton difference, carrying out strategy gradient on-line adjustment on the weight, taking curve minimum mapping as a key frame index, and outputting an image frame. The track and field action key frames can still be accurately captured in real time under complex illumination and shielding conditions.

The invention discloses a remote sensing semantic change detection method, and the specific process is as follows: in a dual-temporal VSS-Mama encoder, executing the four-stage feature extraction of a dual-temporal image pair, and outputting a dual-temporal feature map; cascading the double-time-phase characteristic patterns according to a channel of a change decoder, and respectively placing the double-time-phase characteristic patterns into an STSS module at four stages of the change decoder to carry out learning cross-time-sequence interaction and output a binary change pattern; a parallel bilingual decoder is adopted to execute step-by-step feature up-sampling on the dual-time-phase feature maps and output bilingual change maps; repeating the training until convergence and obtaining an optimal weight file; generating a semantic change graph by using the binary change graph, the bilingual change graph and the optimal weight file; according to the remote sensing semantic change detection method, a twinning network architecture of encoder-change decoder-bilingual semantic decoder is constructed, so that global space-time modeling and local detail enhancement are complementary, and the problems that the remote sensing semantic change detection method is low in long-range dependence modeling efficiency, broken in edge continuity, easy to ignore subtle change, high in reasoning overhead and the like are solved.

A method for generating a training dataset for determining grain boundaries and minerals in a thin section of a rock sample, includes receiving the thin section of the rock sample, generating optical images of the thin section with an optical tool, generating mineral phase images of the thin section with an electron microscopy tool, computing first and second pseudo-images based on different features extracted from the optical images, generating the training dataset based on (1) the optical images, (2) the mineral phase images, and (3) the pseudo-images, and training a single deep neural network, DNN, based on the training dataset to simultaneously determine a mineral type and grain boundaries in the thin section of the rock sample.

The invention discloses a building change detection method fusing building explicit prior and multi-stage feature aggregation, and belongs to the technical field of image processing. The method comprises the steps of obtaining a double-time-phase image at a to-be-detected position, inputting the obtained double-time-phase image into a trained building change detection network to obtain a changed building prediction map of the double-time-phase image, and determining a building change result according to the changed building prediction map. According to the method, in the feature extraction stage, the explicit prior of the edge of the building is introduced, and the edge information extraction capacity of the network is enhanced. Meanwhile, in the feature fusion stage, interaction of local-global information is promoted in a multi-level feature fusion mode, and the reusability of the multi-scale feature map is enhanced. According to the method, the edge and corner information is utilized again at the output end to enhance the optimization capability of the network on the changed building edge, and the building change detection precision is jointly improved.

The invention provides a Flash wafer detection method and device based on deep learning, and relates to the technical field of semiconductor detection. The method comprises the following steps: firstly, receiving an initial electric signal response sequence which is acquired by a scanning probe and comprises reflection intensity and phase deviation; performing time-frequency domain joint transformation processing on the sequence to generate a frequency domain energy distribution map and a time domain attenuation characteristic curve; further constructing a three-dimensional space mapping model containing a frequency domain energy amplitude and a time domain attenuation time constant; a pre-trained condition generation network is called to reconstruct the model, and a reconstructed phase image set with the resolution consistent with that of a standard template is generated; and finally, performing pixel-by-pixel comparison on the reconstructed image and a standard template to generate a detection report containing defect space positioning coordinates and a contour boundary sequence. According to the method, deep mining and visual reconstruction of Flash wafer microscopic electric signal features are realized, and the accuracy and the automation degree of defect detection are remarkably improved.

The invention relates to the field of image recognition, discloses an image recognition method and an image recognition system, and systematically solves the core pain point of traditional document recognition through optical-topology fusion processing and a dynamic resource allocation mechanism. The image recognition method is composed of an acquisition module, a grid module, a phase module, a setting module and a distribution module, pixel brightness is calculated and coded based on document RGB data, and a two-dimensional coding matrix is generated; constructing a geometric correction grid, forming a nonlinear constraint field, and enhancing the anti-deformation capability of the image; generating spiral phase light waves in the constraint field by using a spatial light modulator, and generating a time-varying phase map; determining a local topology index by detecting the number of phase jump times; and extracting the closed boundary region as a character block, and generating an analysis result file. The system breaks through traditional limitation, improves image geometric correction precision, feature extraction sensitivity and boundary judgment accuracy, efficiently completes document analysis, and is suitable for scenes such as document digitization and information retrieval.

The invention provides a dual-temporal image change detection method, system and device based on multi-scale feature fusion and an attention mechanism, and a medium, and relates to the field of computer vision and remote sensing image processing, and the method comprises the steps: taking a dual-temporal remote sensing image semantic change detection data set as original remote sensing image data; preprocessing the original remote sensing image data; extracting multi-scale features of the processed remote sensing image through a double-branch feature extraction network, wherein the double-branch feature extraction network adopts a ResNet architecture as a backbone network; a cross-time-phase feature fusion module is adopted to fuse multi-scale features of different time phases, a multi-stage feature decoding module is adopted to perform up-sampling and scale fusion on the fused features, and a final remote sensing image change detection probability graph is output after decoding operation. According to the invention, by introducing technical means such as deep learning, multi-scale feature extraction and a self-attention mechanism, efficient detection of a multi-time remote sensing image change area is realized, and the detection precision is improved.