4413 results about "Performed Imaging" patented technology

The invention relates to the technical field of image data processing, in particular to a cable surface defect detection method and system based on machine vision. The method comprises the following steps: acquiring a surface image of a cable, and converting the surface image into a grayscale image; determining the local complexity of each pixel point; obtaining a plurality of areas of the grayscale image, performing complexity determination, and dividing each area to obtain a plurality of windows of each area; determining a contrast limit threshold value of each window; performing image enhancement by using a CLAHE algorithm to obtain an enhanced grayscale image; and carrying out cable surface defect detection on the enhanced grayscale image by using a defect detection algorithm. According to the method, the CLAHE parameters are adaptively adjusted based on local complexity, the window size and the contrast threshold are dynamically determined in combination with gray and gradient information, discontinuity is corrected and eliminated through boundary similarity, and the quality and reliability of a cable defect detection image are improved.

Disclosed in the present invention are an edge-deployed semi-supervised anomaly detection method and system for a railway track foreign object. The method comprises the following steps: an edge device encoding and decoding a video stream captured by a camera to obtain an image frame sequence, and performing frame extraction; and using a semantic segmentation model to perform image segmentation on a certain image frame obtained by means of frame extraction, to obtain a railway track region segmentation image. The use of a single image as input may generate an expert model result having a high weight value; however, the determination based on a single image is not stable, multiple consecutive images of the task scene need to be inputted, the frequency of each expert model obtaining the highest weight is computed, and the expert model corresponding to the highest frequency is the final solution. The present invention supports scene-adaptive foreign object detection algorithm automatic selection, and a user can perform selection on the basis of prior knowledge, or selection may be performed by a scene-adaptive automatic algorithm selection method; the user only needs to provide a batch of image data of the current scene, and the optimal algorithm selection can be evaluated.

The invention discloses an SPR response region identification method based on image semantic segmentation and time sequence alignment, and the method comprises the following steps: collecting SPR image frame sequence data, and constructing an original image sequence; performing image preprocessing operation on the original image sequence, and outputting a standardized image sequence; constructing a time sequence window image set composed of multiple continuous frames; inputting the time sequence window image set into an improved SegFormer model, and generating a response region segmentation mask image corresponding to each frame; executing cross-frame time sequence alignment operation of the response area, and outputting time sequence consistency identification mapping of the response area; performing area statistics, intensity analysis and time positioning operation; and generating a structured response region recognition result. The spatial-temporal evolution process of the response area in the SPR image sequence can be effectively recognized, the accuracy and stability of response area recognition are improved, and the method is suitable for high-precision biological detection and real-time molecular analysis scenes.

The invention relates to the technical field of panoramic image construction, in particular to a panoramic image reconstruction method and system based on multi-angle imaging. The method comprises the following steps: collecting a multi-angle original image based on a distributed multi-camera array, carrying out adaptive filtering denoising and adaptive panoramic imaging adjustment, and constructing a multi-angle imaging geometric constraint network; performing multi-view semantic information deviation elimination based on a multi-angle imaging geometric constraint network, and performing global semantic feature fusion to obtain a unified semantic space representation framework; identifying illumination feature information of different visual angles, performing multi-angle illumination corresponding compensation on the multi-angle original image, performing image semantic distortion correction based on a unified semantic space representation framework, and constructing a multi-angle illumination compensation image; and performing multi-scale texture structure analysis on the multi-angle illumination compensation image to generate a high-fidelity texture fusion image. According to the invention, a natural and seamless panoramic image is provided, a panoramic scene is perfectly presented, and the immersive visual experience of a user is improved.

The invention discloses a microscopic image seamless splicing and enhanced reconstruction method based on deep learning, and the method comprises the following steps: S1, collecting a plurality of original images with overlapped regions, and recording the spatial position information and imaging parameters of the original images; s2, preprocessing the original image to generate a standardized image sequence; s3, inputting the standardized image into a structure perception feature extraction network, and extracting a feature map fusing textures and structures; s4, inputting the feature image and the original image into an image registration module; s5, inputting the registration image into the boundary attention splicing network; s6, inputting the seamless image into the residual hierarchy reconstruction network, and enhancing image details through hole convolution and multi-scale branches; s7, image quality evaluation is executed, and the structural similarity, the signal-to-noise ratio and the edge retention rate are calculated; and S8, constructing a training set and carrying out end-to-end training optimization based on a joint loss function. According to the method, a multi-module deep network is fused, and seamless splicing and high-quality enhanced reconstruction of microscopic images are realized.

The invention discloses a bearing defect detection method and system based on machine vision and ultrasonic detection, and particularly relates to the technical field of industrial automatic detection, and the method comprises the steps: S1, a synchronous collection module: carrying out pulse triggering synchronous collection, and generating a time-space reference table; s2, a feature extraction module: performing image noise reduction segmentation and ultrasonic frequency domain decomposition, and outputting a defect feature vector; s3, a fusion identification module: performing cross-modal feature alignment fusion to generate a defect classification conclusion; s4, a size measurement module: performing contour fitting to calculate inner and outer diameters, and outputting a size deviation value; and S5, a comprehensive judgment module: carrying out threshold comparison logic judgment, and generating a multi-modal detection report. According to the method, a space-time reference is established through an encoder, images are segmented in a self-adaptive mode, features are extracted through wavelet decomposition ultrasound, feature weights are re-calibrated through a parallel network and an attention mechanism, composite defects are recognized through cross-modal fusion, comprehensive judgment is conducted in combination with dimensional deviation, and a multi-dimensional quality evaluation system is achieved.

The invention relates to the technical field of red tide anomaly detection, in particular to a red tide anomaly detection method and system based on an improved multi-mode Transform. The method comprises the following steps: acquiring a remote sensing image and text data; respectively carrying out data preprocessing according to the obtained remote sensing image and text data; performing visual positioning and text selection based on the preprocessed data; performing cross-modal feature learning on the basis of a hierarchical Transform of a multi-modal capsule mechanism; guiding an attention mechanism based on a semantic path to carry out image-semantic feature alignment optimization; and carrying out multi-modal knowledge distillation on the optimized features. According to the method, an image and text preprocessing module, a visual positioning module, a keyword extraction module and other modules are combined, multi-angle accurate perception of a complex red tide scene is achieved, and the bottleneck that a red tide area is difficult to accurately recognize under the condition that data are single and information dimensions are limited in a traditional method is broken through.

The invention relates to the technical field of surface defect detection, in particular to a structure surface disease diagnosis method and system based on multi-modal edge calculation, and the method comprises the following steps: obtaining an infrared thermal image frame image and constructing a temperature difference image group, enhancing visible light texture features to generate an enhanced image group, carrying out image registration, extracting a combined feature vector, and carrying out classification and recognition. And mapping the boundary of the defect area to generate a coordinate set, counting disease information and generating a visual display layer. According to the method, thermal anomaly features in different areas can be visually expressed through temperature mapping and space division operation of the infrared thermal imaging image, accurate judgment of defect types is realized through a trained neural network model, a boundary coordinate point set of structure surface defects is extracted through an image mapping means, and the accuracy of the structure surface defects is improved. And in combination with connectivity operation and a rectangular frame construction mode, defect positions are labeled and positioned, so that the accuracy of building surface disease identification, the reliability of boundary extraction and the integrity of zoning risk presentation are effectively improved.

The invention relates to the field of machine learning, and discloses a printing quality detection method and device based on machine learning, and the method comprises the steps: obtaining the original image data of a printed matter under the conditions of multiple batches, heterogeneous illumination and multi-angle imaging, and constructing a printing image training set through combining an image region equalization strategy and a light interference shielding mechanism; performing image block-level fine-grained division on the printing image training set, performing multi-channel feature extraction on each image block by adopting a deep convolution model based on a residual attention mechanism, and generating a composite feature map through a feature coupling structure; based on the composite characteristic spectrum, constructing a defect positioning network, and identifying a potential defect response area; and in combination with the initial defect candidate set, introducing an image background disturbance simulation mechanism and a local artifact inversion model, establishing a defect error maintenance positive frame, and eliminating an interference area caused by non-printing errors. The method has the advantage of improving the accuracy of defect identification.

The invention discloses an image deblurring method based on a rotation perception multi-dimensional attention and blurring sensitivity self-adaptive distribution mechanism, aiming at the defects of the prior art in the aspects of complex blurring processing and image reality sense improvement, and belongs to the technical field of image processing, and the method comprises the following steps: preprocessing a collected picture to obtain a training set; training an image deblurring model by using the training set; and performing image deblurring by using the trained image deblurring model. According to the invention, by designing a plurality of innovative modules and combining fuzzy degree adaptive selection, kernel estimation and dynamic weight distribution driven by a physical model, a multi-dimensional rotation perception attention mechanism, spectrum reconstruction anti-noise deblurring and detail enhancement of perception loss optimization, the image deblurring effect and processing efficiency are effectively improved; the method is especially suitable for processing blurred images with high complexity and high resolution.

The embodiment of the invention discloses a medical image tumor heterogeneity detection method and device. A specific embodiment of the method comprises the following steps: acquiring a brain glioma multi-modal medical image set from medical imaging equipment; performing image preprocessing on the brain glioma multi-modal medical images in the brain glioma multi-modal medical image set to obtain a processed medical image set; performing brain glioma region segmentation on the processed medical image set to obtain a brain glioma segmentation region set; performing high-order feature extraction and subregion division on the brain glioma segmentation region set to generate a high-order statistic feature map group and a tumor subregion image group; boundary optimization and topological repair are carried out on tumor sub-region images in the tumor sub-region image group, and a processed tumor sub-region image group is generated; and generating a tumor heterogeneity assessment report by using the processed tumor subregion image group. According to the embodiment, the automation level and precision of medical image tumor heterogeneity evaluation can be improved.

The invention provides a coral reef remote sensing image multi-modal feature generation and restoration method and system, and relates to the technical field of remote sensing image restoration, and the method comprises the steps: obtaining coral reef image data based on a multi-source sensor, and carrying out the preprocessing; performing image morphological feature and spectral feature extraction on the preprocessed coral reef image data by using a convolutional neural network, and establishing a feature database; carrying out weighted fusion on the multi-source features based on a feature combination network, generating comprehensive feature expressions, and storing the comprehensive feature expressions into a feature database; the coral reef image to be restored is matched with the feature database, and guidance parameters are generated; and inputting the guidance parameters into the generative adversarial network, and performing pixel-level reconstruction on the missing region of the coral reef image to be restored. According to the method, a complete closed-loop system is constructed, multi-source information scheduling, pixel-level guide reconstruction and semantic feedback verification are covered, image information can be supplemented, ecological information can be reasonably reconstructed, and a high-quality data basis is provided for subsequent classification, monitoring and protection work.

The invention provides a high-reflection plane metal part defect detection method and system based on a composite polarized light source, and a medium. The method comprises the following steps: illuminating a high-reflection plane metal part based on the composite polarized light source, adjusting a light source polarization angle and an LED wave band combination to construct a shooting environment, and collecting an image sequence; calculating a polarization intensity distribution parameter for the image sequence based on the relationship between the polarization intensity and the Stokes parameter to obtain a polarization intensity distribution image; analyzing the polarization intensity distribution image based on a polarized light reflection model, and outputting a specular reflection component and a diffuse reflection component; fitting the specular reflection component and separating the diffuse reflection component to obtain a reflection-removed image; based on an image enhancement algorithm, edge enhancement is carried out on the reflection-removed image to obtain an enhanced image, and based on a defect identification algorithm, the enhanced image is analyzed to obtain defect information; by removing the diffuse reflection component and reconstructing the image, the mirror interference is effectively stripped, and the defect identification precision is improved.

The invention relates to the technical field of image processing, in particular to a clinical focus auxiliary segmentation system based on a nuclear magnetic resonance image. The system comprises an MRI image processing module, a lesion auxiliary segmentation module, a probability segmentation correction module and a lesion boundary smoothing module, a corresponding clinical nuclear magnetic resonance image set of a patient can be obtained, image position alignment and gray level adjustment processing can be carried out, and meanwhile a corresponding clinical image lesion segmentation model is constructed to carry out multi-scale fusion auxiliary segmentation. Generating a clinical focus region segmentation fusion image; obtaining a focus confidence probability corresponding to each pixel point in the segmentation image through the clinical focus region segmentation fusion image, and carrying out probability segmentation boundary correction on the clinical focus region segmentation fusion image to obtain a clinical focus region segmentation correction result image; and performing focus edge shape smoothing processing on the clinical focus region segmentation correction result map to generate a clinical focus edge shape segmentation optimization result. According to the invention, high-precision segmentation of the focus in the MRI image can be realized.

The invention discloses a remote sensing image segmentation method based on global feature enhancement and Fourier detail adjustment, and belongs to the technical field of remote sensing image processing. The method comprises the following steps: constructing an image segmentation model comprising a wavelet-Mama global feature enhancement module, a fast Fourier detail adjustment unit and a decoding and segmentation prediction module; performing remote sensing image segmentation training on the built image segmentation model; and performing image segmentation on the target remote sensing image by using the trained image segmentation model. According to the invention, through the wavelet-Mama global feature enhancement module and the fast Fourier detail adjustment unit, the expression ability of surface feature structures, textures and edge information in remote sensing images can be effectively improved, and high-precision segmentation of small targets and fuzzy boundaries in complex scenes is realized. The method is especially suitable for accurate recognition of buildings, roads, water bodies and other targets under high-resolution remote sensing images, and has high practical value and popularization prospects.

The invention provides an image correction method and system based on a nonlinear distortion distribution elliptical fisheye lens, and a medium. The method comprises the following steps: establishing a nonlinear mixed distortion imaging model based on a mapping relation between image coordinates and a spherical direction; defining a distortion vector, and performing fitting processing on the distortion vector based on a two-dimensional B-spline function to obtain a distortion function; the method comprises the following steps: acquiring images in a five-surface three-dimensional angle calibration space based on an elliptical fisheye lens, analyzing lens imaging parameters, and generating parameter values of a distortion function; analyzing parameter values of the distortion function based on a nonlinear mixed distortion imaging model to generate calibration parameters, and remapping pixel points in the distorted image based on the calibration parameters; performing image reconstruction on the remapped distorted image to obtain a corrected image; improving the sampling density of a target area by constructing a nonlinear mixed distortion imaging model; the system adapts to complex monitoring environments such as galleries and entrances and exits; and spatial distortion is flexibly expressed through a B spline, so that the modeling capability is improved.

The invention relates to the technical field of image processing, in particular to an intelligent welding defect positioning and detecting system based on image processing. Obtaining a suspected noise degree according to gradient features and neighborhood gray level distribution features of pixel points in the welding image; obtaining a gray confidence coefficient according to the gray features of the pixel points and the gray difference features of the pixel points and the neighborhood; obtaining a structure confidence coefficient according to the area feature of the connected domain where the pixel point is located, the contour change feature of the edge line of the connected domain and the gradient distribution feature of the edge line in the normal direction; obtaining a denoising coefficient of the pixel point according to the suspected noise degree, the gray level confidence coefficient and the structure confidence coefficient; and adjusting the standard deviation in the Gaussian filtering algorithm according to the de-noising coefficient. The method comprises the following steps: denoising a welding image according to an adaptive standard deviation, and performing image enhancement on the denoised welding image to obtain a to-be-detected image; welding flaw detection is carried out on the to-be-detected image, and the detection accuracy is improved.

The invention discloses a multi-scattering SAR (Synthetic Aperture Radar) imaging simulation method, system and device based on physical consistency modeling and a medium. The method comprises the following steps: constructing a three-dimensional electromagnetic model of a target; constructing a geometric model under the working mode of the ray tracing auxiliary transmitting-receiving co-located SAR; a high-frequency electromagnetic scattering algorithm based on ray tracing is adopted, multiple scattering paths of a first order, a second order and a higher order are considered, superposition and amplitude modulation are carried out on scattering contributions of all orders, scattering field distribution of a target under the far field condition is calculated, and a far-field scattering field is obtained; performing spatial propagation mapping on the total far-field scattering field, and mapping a far-field scattering result into a radar platform near-field echo signal; correcting the amplitude of the echo signal to obtain a two-dimensional SAR near-field echo matrix; sAR imaging processing is carried out, wherein the SAR imaging processing comprises distance compression, range migration correction (RCMC) and azimuth compression. According to the invention, the authenticity and precision of SAR imaging simulation in a complex target scene are improved.

The embodiment of the invention provides an image generation method, an image generation device, electronic equipment and a storage medium, and belongs to the technical field of image processing. The method comprises the following steps: acquiring an image generation prompt word and carrying out style understanding according to the image generation prompt word to obtain a scene style feature; performing scene expansion on the image generation prompt word according to the scene style feature to obtain a scene expansion word; performing scene arrangement according to the scene expansion word to obtain scene features; performing information fusion according to the scene style features, the scene expanding words and the scene features to obtain scene description information; performing image generation according to the scene description information to obtain a preliminary image; and performing image restoration according to the initial image and the scene description information to obtain a target image. The image generation method and device can be applied to business systems needing a large number, such as financial science and technology and medical health care, and the image generation efficiency and precision can be improved.

The invention discloses a pavement disease image adaptive enhancement and restoration processing method, and belongs to the technical field of image processing, and the method comprises the steps: obtaining a pavement disease image, and carrying out the frequency domain transformation; recognizing a pavement material type based on the frequency domain features and determining a material sensing parameter set; performing multi-scale frequency domain decomposition according to the material perception parameter set, and performing adaptive enhancement on a low-frequency illumination component and a high-frequency detail component; carrying out disease feature detection and generating a disease perception weight map, and carrying out key enhancement on a disease area; according to the method, the self-adaptive enhancement model for road surface material perception is established, and targeted image enhancement processing is realized.

The invention relates to a layout image generation method and device, computer equipment and a storage medium, and the method comprises the following steps: carrying out the element analysis of original image data, and obtaining image element information; performing feature extraction on the image element information to obtain image features and text features; performing hypergraph structure construction according to a preset design rule, the image features, the text features and the image element information to obtain hypergraph structure information; performing style parameter adjustment and coordinate adjustment on the hypergraph structure information according to the matching similarity corresponding to the element embedding vector to obtain to-be-rendered information; and performing image layout rendering according to the to-be-rendered information to obtain an effective layout image. The method can be applied to application scenes of financial science and technology and digital medical systems, and insurance product propaganda images or disease prevention propaganda images which effectively express image elements and features can be effectively, accurately and quickly generated according to original insurance product images or health education images in the system.

The invention relates to the technical field of image acquisition and testing, and discloses an image acquisition and testing method, device and equipment and a storage medium, and the method comprises the steps: collecting a standard test card image through an image acquisition card in a multi-stage standard illumination environment, and calculating sensitivity benchmark test data; executing time domain and space domain combined sampling processing to obtain super-sensitivity image data; performing brightness analysis and dynamic adjustment on the currently shot first scene image to obtain a first image acquisition parameter; acquiring a second scene image, and performing image grid division and contrast compensation on the second scene image to obtain an enhanced image after contrast compensation; according to the super-sensitivity image acquisition method and the super-sensitivity image acquisition device, the sensitivity limitation of the position depth of a traditional image acquisition card is broken through, and super-sensitivity image acquisition is realized.

The invention discloses an unmanned aerial vehicle infrared thermal imaging method and device for cold leakage detection of a low-temperature storage tank, and relates to the field of automatic inspection and detection of storage tanks, and the method comprises the steps: determining the inspection range and detection distance of an unmanned aerial vehicle based on geometric parameters, a cold leakage frequency region and environment parameters of a site; calculating the distance between the shooting points and the vertical coverage height of single-circle flight, further dividing flight elevation layering, and calculating the number of the shooting points of each circle of flight; establishing a three-dimensional model; collecting a visible light image and an infrared thermal imaging image of each shooting point; the method comprises the following steps: performing multi-dimensional correction and temperature image conversion on an acquired infrared thermal imaging image, performing image registration on a temperature image with a temperature scale and a visible light image, performing cold leakage area identification to obtain a cold leakage area, performing quantitative calculation to obtain three-dimensional positioning of a cold leakage position and a cold leakage area, and performing cold leakage area identification on the cold leakage area. And generating a visual detection result, a detection report and a maintenance suggestion. According to the invention, automatic detection and accurate analysis of the cold leakage area of the low-temperature storage tank can be realized.

The invention discloses a construction operation safety intelligent supervision system and method based on an AI identification algorithm, and relates to the technical field of operation supervision. The method comprises the following steps: acquiring a defect image sample and a non-defect image sample, performing defect classification and target feature library training, acquiring an original image, performing image pre-processing, feature library comparison and processed image output, framing a detected target by using an outer line rectangle, and obtaining a non-defect image sample; the verification terminal and a work card of an operator are networked and identity information is verified, the camera analyzes the identity information of the operator based on AI identification, and the verification terminal and the camera perform cross authentication to confirm the operator. Multi-target real-time intelligent analysis and identification of a non-digital display vernier caliper, a grounding resistance megger, a test pen, an anti-falling device and a safety cone barrel are realized, the requirements of three-dimensional supervision, operation and service in the industrial field are met, and the intelligent identification capability of the system is improved.

The present invention relates to the technical field of medical images. Disclosed are a deep learning-based CT artifact removal method and system. The method comprises: acquiring a CT image, and separately performing sine transform and wavelet transform processing on the CT image; constructing an image enhancement model, and performing image optimization on the processed image separately by means of the image enhancement model and a random inversion layer which are connected in sequence; coupling the optimized image and the original image and then inputting the coupled image into the image enhancement model for reprocessing; and performing element-wise addition on the reprocessed image and the optimized image to obtain an artifact-removed CT image. In the present invention, wavelet transform is introduced to process the CT image to extract the context and spatial information of the CT image, effectively extracting feature information in an artifact removal process and improving the performance of image enhancement; and a CT image resolution enhancement model based on a VMamba model is established, enhancing the long-term dependencies in network training, effectively recognizing and removing radioactive artifacts, and improving the network training efficiency.

The invention discloses an autoclaved aerated concrete member surface defect intelligent identification system based on image processing, and particularly relates to the field of defect identification, comprising an image acquisition module, an image preprocessing module, a defect candidate region extraction module, a defect identification and classification module, and a result output and alarm module; according to the method, a high-definition industrial camera is used for collecting a component surface image, and adaptive median filtering and a Retinex algorithm are adopted for image denoising and enhancement, so that the influence of noise and uneven illumination is eliminated; utilizing an improved multi-threshold segmentation and Canny edge detection algorithm to accurately extract a defect candidate region; the method comprises the following steps: extracting three types of feature parameters of shape, texture and gray scale, and inputting the three types of feature parameters into a deep learning model taking ResNet50 as a basic network to realize automatic identification and classification of four types of typical defects of cracks, holes, unfilled corners and surface peeling; and finally, the system divides severity levels according to the defect size, and triggers differentiated visual alarm and linkage control.

The invention discloses a method and system for recognizing the number of bacillus in a gynecological micro-ecological microscopic image, and belongs to the technical field of image recognition and micro-ecological analysis. Performing image enhancement processing on the image to improve the definition of the target area; pixel-level segmentation is carried out on the enhanced image based on the trained image segmentation model, and a suspected bacillus target area set is extracted; extracting a morphological characteristic parameter set for each target area; screening the suspected areas by combining a bacillus morphological feature discrimination model obtained by clinical labeling sample training, and removing false targets; counting the number of the effective areas, estimating the number of bacilli in the adhesion areas by adopting a skeleton endpoint analysis method, and finally outputting the total number of bacilli in the image; the method has the characteristics of high precision, high robustness and high automation degree, and is suitable for intelligent identification of the micro-ecological structure in the gynecological microscopic image.

The invention provides a satellite weak and small target on-orbit observation system and method based on intelligent closed-loop feedback, and the system comprises a detection module which carries out the image enhancement and time sequence consistency enhancement of an infrared image, obtains an enhancement feature, and obtains a candidate target set based on the enhancement feature; the tracking module is used for carrying out target matching in the search area; when the matching succeeds, the candidate position is used as an observation value, and the observation value is input into a Kalman filter to obtain a target state vector; when the matching fails, taking a prediction state of the Kalman filter as a target state vector, expanding a search window by taking a prediction position as a center, and executing re-identification; the control module is used for mapping the target state vector into an attitude error and generating a control instruction by adopting a single-neuron self-adaptive PID (Proportion Integration Differentiation) controller; and the closed-loop scheduling module is used for dynamically adjusting operation parameters of at least one module according to the execution error and the detection confidence coefficient. According to the invention, the continuous tracking precision and attitude control stability of the weak and small target are improved.

The invention discloses a farmland soil moisture content real-time surveying system and method based on unmanned aerial vehicle multispectral imaging. The method comprises the steps that multispectral sensor parameters are configured, and an unmanned aerial vehicle flight scanning path is planned; a space coordinate system and time synchronization mechanism is established, and high-precision positioning and time synchronization are achieved; collecting and calibrating visible light, near-infrared and short-wave infrared original image data to form a spectral signal set; calculating an image signal-to-noise ratio, screening a low-quality image, and executing noise suppression; extracting timestamp information from the suppressed image, performing spatial positioning, and establishing a spatial corresponding relation database; calculating time synchronization precision and performing image correction; evaluating the spatial resolution according to the distortion coefficient; calculating a comprehensive quality parameter, comparing the comprehensive quality parameter with a threshold value, and obtaining a soil moisture content distribution parameter when a condition is met; a farmland moisture state map is generated, partition marking is carried out according to a multi-level standard, a monitoring result is output, and high-precision and real-time monitoring of the farmland soil moisture content is achieved.

The invention provides a pulmonary nodule display method and device, electronic equipment and a storage medium, and the method comprises the steps: segmenting a three-dimensional reconstruction image of the chest of a target patient to obtain a preoperative segmentation image, the three-dimensional reconstruction image being obtained based on preoperative CT data, and the preoperative segmentation image comprising the position information of a pulmonary nodule; segmenting the video image of the intraoperative lung tissue of the target patient collected by the thoracoscope in real time to obtain an intraoperative segmented image; feature matching is conducted on the preoperative segmented image and the intra-operative segmented image, the lens pose of the thoracoscope is determined, the preoperative segmented image is mapped into a two-dimensional reference image based on the lens pose, and the two-dimensional reference image comprises the position information of the pulmonary nodule; and carrying out image registration on the two-dimensional reference image and the intra-operative segmented image, and carrying out pulmonary nodule marking on the intra-operative segmented image to obtain a video image for displaying pulmonary nodules in real time. The position of the pulmonary nodule in the operation is accurately displayed in real time in a non-invasive mode, and the operation efficiency is improved.