1998 results about "High resolution image" patented technology

The invention discloses a stainless steel surface defect detection method based on machine vision, and relates to the field of material defect detection.The stainless steel surface defect detection method comprises the steps that stainless steel surface images are collected, the collected multispectral surface images are subjected to image fusion, and a multispectral composite image is generated; performing detail enhancement on the low-resolution image through a super-resolution reconstruction algorithm to obtain a high-resolution image set; positioning a defect area position from the high-resolution image set through a defect detection algorithm, and obtaining a defect position data set; extracting a historical image defect feature set and a real-time image defect feature set from the defect position data set, and constructing a defect classification model according to the historical image defect feature set; inputting the real-time image defect feature set into a defect classification model, and performing defect classification and evaluation to obtain a defect classification data set; and generating and distributing a defect detection report. By combining the multispectral imaging algorithm, the super-resolution reconstruction algorithm and the defect classification model, the automation, precision and efficiency of stainless steel surface defect detection are improved.

The invention discloses an industrial product quality detection method and system based on machine vision, and relates to the technical field of quality detection.During operation of the system, through an industrial camera and an optimized light source layout, high-resolution image acquisition is performed on electronic components in real time by adopting multi-angle shooting, a series of optimization processing is performed on the acquired images, and the quality of the electronic components is detected. The method comprises the following steps: extracting shape, texture and color features according to geometric characteristics of an electronic component to form key parameters for judging defects, identifying potential defects of the electronic component by using a deep learning model algorithm, calculating to obtain a quality evaluation index Q, and calculating the quality evaluation index Q according to calculation results of feature extraction and defect detection. And comprehensively evaluating the quality of each component, comparing the quality evaluation index Q with a first preset threshold value M and a second preset threshold value N to obtain a quality evaluation grade, feeding unqualified detection information back to a production line, and removing unqualified components.

The invention relates to the technical field of image processing, and particularly discloses a remote sensing image super-resolution reconstruction method and system based on a prior diffusion model, and the method comprises the steps: carrying out the shallow feature information extraction of a low-resolution image, and obtaining a shallow feature map; the method comprises the following steps: performing multi-scale down-sampling and convolution fusion processing on a shallow feature map and a high-resolution image, and performing high-frequency information extraction and adaptive high-frequency enhancement processing on the processed feature map to obtain condition information; according to the method, deep mining of high-frequency information is carried out on a low-resolution image input as condition information, so that the high-frequency information in the low-resolution image gradually approaches a high-resolution image, and the reconstruction precision is improved; the noise prediction network model guides step-by-step noise removal in the back diffusion process, and the detail reduction capability and precision of natural disaster remote sensing image super-resolution reconstruction are improved.

The invention relates to the technical field of image detection, and discloses an intelligent detection method for the machining process of an automobile decorative plate. The method comprises the following steps: performing high-resolution image acquisition and preprocessing on the automobile decorative plate to obtain standardized image data; performing region segmentation operation on the standardized image data to obtain a decorative plate target region mask image, and extracting to obtain a feature vector set; inputting the feature vector set into a ResNet-v1-50-based transfer learning network for training to obtain a defect classification model, and calculating candidate defect area masks through the defect classification model; and performing multi-scale analytic hierarchy process on the candidate defect area mask to obtain a defect risk heat map, and performing clamping quality analysis on the connection area of the clamping block and the clamping groove according to the defect risk heat map to obtain a clamping quality evaluation result. According to the method, accurate positioning of the defect area is realized, the defect risk heat map is constructed to quantify the risk levels of different areas, and data support is provided for production process optimization.

The invention belongs to the technical field of unmanned aerial vehicle inspection, and particularly provides a photovoltaic power station inspection method and system based on an unmanned aerial vehicle and a GIS, and the method comprises the steps: obtaining initial geographic information data of a photovoltaic power station, enabling the unmanned aerial vehicle to execute initial surveying and mapping flight, collecting high-resolution image data, and constructing a three-dimensional model of the photovoltaic power station; fusing the high-resolution image data with the initial geographic information data to generate a multi-factor initial intelligent inspection path; the unmanned aerial vehicle flies along the initial intelligent inspection path, collects multi-modal data of the photovoltaic module, and transmits the multi-modal data to a GIS for data fusion and updating to obtain fused data; generating a fault distribution thermodynamic diagram and a maintenance priority map; the inspection path is dynamically adjusted, and the three-dimensional model of the photovoltaic power station is updated. According to the method and the system, comprehensive inspection can be carried out on the photovoltaic power station, the inspection accuracy and efficiency are improved, the inspection path can be dynamically adjusted, unnecessary repeated inspection is reduced, and the inspection cost is effectively reduced.

The invention discloses a building material quality detection method and system, and relates to the technical field of quality detection.The building material quality detection method comprises the steps that a distributed sensor array is formed by deploying multifunctional intelligent nodes to collect monitoring data, and meanwhile a high-resolution image is collected in combination with an unmanned aerial vehicle; performing fusion processing on the multi-source data to generate comprehensive analysis basic data; establishing a time sequence prediction model by using a convolutional neural network and a long-short-term memory network, and predicting a material performance change trend; performing abnormal sample detection by adopting density clustering and a support vector machine algorithm; and constructing a three-dimensional digital twinborn model, visually displaying an abnormal detection result and generating a detection report. According to the invention, a complete technical system is formed, intelligentization, precision and visualization of building material quality detection are realized, and powerful support is provided for quality management decision.

The invention relates to the technical field of image processing, and discloses a lightweight image super-resolution reconstruction method based on frequency domain-spatial domain assisted Mama, which comprises the following steps: acquiring an original image data set and carrying out data preprocessing on the original image data set to acquire a training data set and a test data set; introducing a frequency domain-spatial domain Mama layer and an interaction layer based on transpose and self-attention features, and constructing a lightweight image super-resolution reconstruction model based on frequency domain-spatial domain assisted Mama; inputting the training data set into a lightweight image super-resolution reconstruction model based on frequency domain-spatial domain assisted Mama for training to obtain a trained lightweight image super-resolution reconstruction model with weight based on frequency domain-spatial domain assisted Mama for reconstructing a low-resolution image into a high-resolution image; inputting the test data set into a trained lightweight image super-resolution reconstruction model with weight and based on frequency domain-space domain assisted Mama to obtain a reconstructed high-resolution visual image; according to the method, lightweight image super-resolution is realized.

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 an image super-resolution reconstruction method based on deep learning, and belongs to the technical field of image signal processing. Comprising the following steps: S1, realizing multi-dimensional feature representation of a low-resolution input image by using a deep learning network; s2, realizing accurate alignment and lossless mapping of cross-scale features, and effectively solving the problems of feature dimension inconsistency and information distortion; s3, explicitly learning an increment mapping relation between low-resolution input and a reconstructed high-resolution image, and relieving the gradient disappearance problem of a deep learning network structure; s4, enhancing the naturalness and detail fidelity of the reconstructed image through adversarial training; s5, integrating pixel-level reconstruction loss, perception loss and structural similarity loss, and effectively balancing objective indexes and subjective visual quality of the reconstructed image; and S6, realizing joint optimization of deep learning network parameters through a unified gradient back propagation mechanism, and finally outputting a high-quality super-resolution reconstructed image.

The invention discloses a high-resolution remote sensing image change detection method based on multi-scale convolution decoding, electronic equipment and a storage medium, and belongs to the technical field of remote sensing image change detection. In order to meet the high-resolution remote sensing image change detection requirement, the method comprises the following steps: constructing a remote sensing image data set; a first stage and a second stage are formed based on a CNN module, a third stage and a fourth stage are formed based on a MambaVision module and a Transform module, and four dual-time-phase multi-scale feature maps are respectively obtained through processing of the four stages; constructing a time feature interaction module TFIM; a multi-scale convolution decoder EMCAD is constructed; and constructing a loss function, carrying out training, testing and evaluation by using the constructed remote sensing image data set, and applying the obtained trained optimal model to high-resolution remote sensing change detection. According to the invention, the detection capability of tiny changes in a high-resolution image is improved.

The invention discloses an intelligent defect detection system and a self-repairing method for a flexible base material RFID tag, and belongs to the technical field of Internet of Things electronic device manufacturing. According to the system, a three-dimensional dynamic scanning system is constructed by integrating a high-resolution image acquisition module, a multispectral sensor array and a mechanical arm motion platform, and surface and internal structure characteristics of a flexible substrate are captured in real time. A defect identification algorithm based on deep learning is combined with a multi-scale convolutional neural network and a transfer learning technology, precise classification of 12 types of defects such as microcracks, conductive layer fractures and base material deformation is realized, and the detection precision reaches 99.2%. A dual-mode self-repairing mechanism is put forward, specifically, nano-silver conductive colloid is injected through a microfluid channel for the defects of the conductive layer, and 3D structure reconstruction is achieved through a controllable temperature field; for substrate damage, a photoresponse shape memory polymer patch is adopted, and molecular-level bonding repair is achieved after ultraviolet light activation. According to the scheme, the detection efficiency is improved by more than 5 times, and the radio frequency performance of the tag is recovered to 98.7% of the initial value after self-repairing.

The invention discloses a medical image super-resolution reconstruction method based on multi-level attention guidance, and the method comprises the following steps: S10, constructing a deep learning network model based on a generative adversarial network architecture, which comprises a generator and a discriminator; the generator is based on an improved U-Net architecture, a hierarchical attention module and a dual-path feature processing module are configured in an encoder and a decoder of the generator, the hierarchical attention module adopts different attention strategies according to network levels to consider structure and texture, and the dual-path feature processing module separates and processes low-frequency and high-frequency information; the generator further comprises a multi-level feature fusion module for integrating the multi-scale features of the decoder, and an attention guide up-sampling module for final enhancement and dimension raising. The discriminator adopts a spectrum normalization U-Net architecture and uses multi-scale features for matching; s20, training the network model by adopting a composite loss function comprising pixels, adversarial, perception and total variation loss; and S30, inputting the low-resolution image into the trained model, and outputting a high-resolution image. According to the method, through deep fusion of multi-level attention and multi-scale feature processing, the image restoration quality can be remarkably improved, the texture detail definition can be enhanced, the anatomical structure accuracy can be ensured, and the noise robustness can be improved.

The invention discloses an urban building three-dimensional automatic modeling and visualization method, and belongs to the technical field of building three-dimensional modeling. The method comprises the steps that point cloud data, high-resolution images and geographic information system data of urban buildings are acquired, data cleaning, registration and alignment are carried out, and preliminary building digital representation is formed; accurately segmenting each building, and identifying the contour and main structural features of the building; based on the data integrity and the building complexity, adaptively selecting a proper reconstruction strategy to carry out three-dimensional reconstruction; in the reconstruction process, the geometric structure is analyzed and optimized in real time, and potential topological problems are repaired; automatically generating missing details based on a predefined architectural style library and a component library, and performing material inference and texture mapping; a graph structure is used for representing the relation between the buildings, and the positions and orientations of the buildings are adjusted through a global optimization algorithm; a rendering engine supporting multi-level detail switching is developed, and smooth visualization and interaction of a large-scale city scene are achieved.

The invention relates to the technical field of topographic mapping, in particular to a DEM super-resolution reconstruction method and system combined with a depth map, and the method comprises the steps: constructing a multi-modal data set which comprises an optical image, a depth map, a low-resolution DEM image and a high-resolution DEM image which are paired; designing a multi-modal fusion super-resolution network; designing a pseudo Siamese feature extraction module to extract low-level features and high-level features of the optical image, the depth map and the low-resolution DEM image; fusing the low-layer feature map generated by the pseudo Siamese feature extraction module in a shallow layer through a multi-scale feature fusion module, and fusing the deep-layer feature map in a deep layer; outputting a high-resolution DEM image through a DEM reconstruction module; and training the multi-modal fusion super-resolution network by using the multi-modal data set and the collaborative loss function. According to the method, the depth map generated by the optical image is used as priori knowledge to guide the DEM super-resolution reconstruction task based on the basic depth large model, a complex fine adjustment process is not needed, and the DEM reconstruction precision and efficiency are improved.

The invention relates to a high-fidelity cloud rendering cluster scheduling method and system, and the method comprises the steps: receiving a rendering task, generating a multi-level priority queue according to the task complexity, timeliness and resource demand classification, and automatically optimizing a scheduling strategy; monitoring the resource state of the heterogeneous computing node in real time; allocating tasks by using preemptive and round-robin scheduling strategies, and adjusting and coping with resource fluctuation in combination with a dynamic code rate; the tasks are decomposed by adopting a spatial blocking and time framing strategy, and an execution sequence is controlled according to a topological sorting algorithm; abnormal nodes are identified through heartbeat detection, and affected subtasks are migrated through incremental task updating. According to the method, efficient resource allocation, scheduling algorithm and idle key frame scheme can be realized, the GPU directly outputs the video stream, the rendering speed is remarkably improved, resource waste and operation cost are reduced through a dynamic resource allocation mechanism, a fault-tolerant mechanism is provided, the task is ensured to be normally completed under the condition of node fault, and the task efficiency is improved. And distributed rendering and synthesis of large-scale high-resolution images are supported.

The invention provides an image restoration and super-resolution reconstruction system and method based on deep learning, and belongs to the technical field of digital image processing. The invention aims to solve the problems of high calculation complexity and resource consumption, limitation of long sequence processing, high training difficulty and texture scene deficiency when a multi-scale residual network based on a Transform architecture is used for image resolution conversion. The reconstruction system comprises: an image preprocessing module performing window division and video memory optimization on an input low-resolution image; the multi-layer fusion network dynamically adjusts the characteristics of the low-resolution image, captures channel information in different scenes, performs interactive fusion, performs comparison supervision, establishes an information communication channel, dynamically adjusts and optimizes parameters through negative feedback, and obtains a super-resolution image. And the loss function module maximizes the similarity of the super-resolution image and the high-resolution image in the segmentation feature space to obtain a final super-resolution image.

The invention relates to the field of image comparison, and discloses a multi-layer PCB alignment deviation detection system and method based on image comparison, and the method comprises the steps: obtaining high-resolution image data before and after lamination of a multi-layer PCB, carrying out the unified registration preprocessing of an original image through the combination of a multi-mode image fusion algorithm and a geometric distortion correction technology, and obtaining a multi-layer PCB alignment deviation detection result; constructing a standardized image registration input set; key alignment feature extraction is carried out on the image registration input set, and a multilayer structure graph model is constructed based on a graph neural network; in combination with the alignment reference map, dynamically adjusting an image comparison window and a search region by adopting a region attention mechanism and a local adaptive matching algorithm, and constructing an alignment deviation mapping map; constructing a deviation evolution model by using a time sequence behavior recognition network according to the constructed alignment deviation mapping graph and historical process deviation data; and performing comprehensive evaluation on the image registration input set based on the deviation evolution model and the generated early warning information. The method has the advantage of improving the accurate detection level.

The invention discloses a feature integration method for interactive convolution and dynamic focusing of infrared images. The feature integration method comprises the steps that infrared image pairs with different resolutions in various real scenes are obtained through an infrared camera; performing degradation preprocessing on a part of original high-resolution images to obtain low-quality high-resolution images to form a mixed low-resolution data set, and dividing the processed data set into a training set and a test set; constructing a double-layer feature extraction module for feature modeling; training a network by using the processed training set, and optimizing a loss function; and inputting a low-resolution infrared image into the trained network, and outputting a high-resolution reconstruction result. According to the method, local and global features are fused, so that the super-resolution reconstruction quality of the infrared image in complex scenes such as low contrast and fuzzy edges is remarkably improved, and meanwhile, relatively high calculation efficiency is kept.

The invention provides an optical remote sensing image change detection method and system based on change perception and semantic guidance, a storage medium and electronic equipment, and the method comprises the following steps: firstly, carrying out the preprocessing of a dual-tense image, and carrying out the labeling of a change region, and generating a change label graph; and cutting the image and the label into small samples, and inputting the small samples as training data into the CASGNet model. Multi-scale features are extracted through an encoder, change perception is carried out through a space-time difference fusion module, cross-time change information is effectively captured, pseudo change noise is restrained, and reliable multi-scale difference features are obtained. Thirdly, the semantic guiding module decodes the multi-scale difference features, semantic masks are generated through the semantic aggregation module, fusion of the multi-scale features is guided, interference is eliminated, and edge details of a change area are complemented; and finally, in a detection stage, the preprocessed sample to be detected is input into the model, a complete and accurate change detection result is obtained, and the method is suitable for a detection task of a complex scene and a high-resolution image.

The invention discloses a face image super-resolution method and device of a diffusion model based on residual prediction, and relates to the field of image super-resolution, and the method comprises the steps: obtaining a to-be-reconstructed low-resolution image; a diffusion model based on residual prediction and a conditional noise predictor are constructed and trained, and a trained diffusion model and a trained conditional noise predictor are obtained; gaussian noise is sampled from standard Gaussian distribution to serve as residual noise images, a to-be-reconstructed low-resolution image, the residual noise image of each step and the corresponding step length are input into a trained conditional noise predictor, prediction noise of each step is obtained through prediction, and the low-resolution image is reconstructed. Iteratively executing a de-noising process of T steps through a reverse de-noising network in the trained diffusion model to obtain a restored residual image; and adding the restored residual image and the up-sampled low-resolution image to be reconstructed to obtain a restored high-resolution image. The image super-resolution method solves the problems that an existing image super-resolution method is complex in generation process and limited in detail recovery.

The invention discloses an industrial image anomaly detection method based on deep learning, and particularly relates to the technical field of industrial visual detection. The problems of high false alarm rate, fuzzy fine defect positioning, insufficient real-time response capability, difficulty in model increment updating and the like caused by data distribution drift in an industrial scene are solved. According to the method, robust features are extracted through a multi-scale feature fusion auto-encoder, and a dynamic memory bank is constructed to update a normal sample prototype online; a dual-path detection mechanism is adopted to cooperate with a pixel-level reconstruction error and attention weighted feature matching deviation; efficient edge reasoning is realized in combination with block parallel processing and model compiling optimization; and designing an elastic incremental learning framework to prevent disastrous forgetting. And finally, false alarms caused by environmental changes are reduced, accurate positioning of pixel-level defects is realized, millisecond-level detection requirements of high-resolution images are met, safe and efficient model online evolution is supported, and adaptability and reliability of an industrial quality inspection system are comprehensively improved.

The invention discloses a defect small target detection method and system for a high-resolution image of an electric power scene, and relates to the technical field of electric power vision, when a small defect detection model is trained, a progressive training mode is adopted, the detection effect of a small target in a common electric power scene is better, sampling is carried out through a scoring network, and the detection efficiency is improved. The precision of the model is ensured, and meanwhile, complicated and redundant calculation is avoided, so that the timeliness and accuracy of the whole model are ensured, and end-to-end detection of a power scene target can be realized; in the scheme of the invention, the target resolution is adopted to cut the current electric power equipment image to obtain multiple frames of current electric power image slices under the target resolution, and the current electric power image slices are detected based on the small defect detection model formed after training. And after the defect detection result is obtained, the frame coordinate of the defect detection result is mapped to the current power equipment image, so that the detection precision of the small target in the power image is high, and the intelligent inspection task requirement of a power grid can be met.

The invention relates to the technical field of image analysis and detection, in particular to a glass bottle label defect detection method and system based on image processing, and the method comprises the steps: obtaining a high-resolution image through a fixed-angle light source and near-infrared irradiation, carrying out the noise suppression processing through gradient enhancement filtering, detecting the label edge, and recognizing the label region boundary. And obtaining a label area image. According to the invention, high-resolution image acquisition is combined with gradient enhanced filtering, so that the identification precision of the label area is improved. Local detection unit division and fiber arrangement direction analysis enable direction abnormal region positioning to be more accurate. Spatial density analysis of mutation point distribution effectively distinguishes the fiber fracture degree, and misjudgment is avoided. And multi-scale calculation is combined with consistency fluctuation rate and density change rate cross validation, so that the defect detection stability is enhanced. And through gradient analysis of the fluctuation accumulation amplitude and the direction concentration deviation value, the defect classification accuracy is improved, and the defects of scratches, tearing, falling, label dislocation and the like can be effectively distinguished.

The present application relates to the field of computer vision. Provided are a visual encoding method and apparatus, and a visual encoding model training method and apparatus, which are used for using the same visual encoding model to encode images of different resolutions, and are applied to encoding scenarios for images of more sizes. The visual encoding method comprises: first, acquiring an input image, wherein the input image may be a high-resolution image and may also be a low-resolution image; and then inputting the input image into a visual encoding model, so as to output visual encoding data, wherein the visual encoding model is used for dividing the input image into a plurality of image blocks according to positional embedding, extracting features from each image block, and outputting visual encoding data on the basis of the features of each image block and corresponding positional encoding, the positional embedding is obtained by means of adjusting initial positional embedding on the basis of the difference between the input image and a preset resolution, and the positional embedding may specifically comprise a matrix corresponding to the division of the input image

Systems and methods for upsampling low-resolution content within a high-resolution image include obtaining a composite image and a mask. The composite image includes a high-resolution region and a low-resolution region. An upsampling network identifies the low-resolution region of the composite image based on the mask and generates an upsampled composite image based on the composite image and the mask. The upsampled composite image comprises higher frequency details in the low-resolution region than the composite image.

The invention discloses an image reconstruction method based on a multi-window cross feature fusion attention mechanism. According to the method, the resolution of an original image is reduced through bicubic interpolation to obtain model training data, and the original image is used as a sample label. For training data, shallow layer features are extracted through blueprint separable convolution; and improving an efficient separable distillation module to obtain a plurality of attention fusion distillation modules, and extracting deep features. A feature fusion module based on multi-window cross attention is used to carry out operations such as multiple times of segmentation, attention calculation, multi-window cross attention calculation, recombination and the like on deep features, and feature enhancement is realized. And fusing the features with shallow layer features, and outputting a reconstructed high-resolution image. Models with different reconstruction multiplying powers can be obtained according to different sampling multiplying powers under training data.

The invention relates to the technical field of metal defect detection, and discloses an aluminum alloy round aluminum rod surface defect image super-resolution method. The method comprises the following steps: acquiring a low-resolution original image sequence of surface defects of the aluminum alloy round aluminum rod, and synchronously acquiring gray value distribution at different illumination angles through a multi-channel optical sensor; constructing a dynamic degradation model according to pixel displacement of adjacent frames in the original image sequence, extracting cross-scale defect features in the original image sequence, and taking output parameters of the dynamic degradation model as spatial constraint conditions of a feature extraction network; and a high-resolution defect image is generated through the multi-stage residual error reconstruction network, the high-resolution image output by the reconstruction network is fed back to the dynamic degradation model, and the frequency domain response coefficient of the spatial fuzzy kernel function is updated to form closed-loop optimization. The identification degree of defect features is improved, and a reliable image data basis is provided for accurate detection of the surface defects of the aluminum alloy round aluminum rod.

An automated system for the detection of pests and diseases in crops using image processing based on deep learning, which includes the following: ◯ an image acquisition unit (101) configured to capture high-resolution images of crops; ◯ a preprocessing unit (102) for improving image quality by applying noise reduction, contrast enhancement and segmentation techniques; ◯ a convolutional neural network (CNN) model(103) trained on a dataset of annotated images of pests and diseases and configured to extract features and classify detected anomalies; ◯ a decision unit (104) that, based on the detected pests or diseases, generates diagnostic reports and provides treatment recommendations; ◯ an integrated mobile (105) and web-based application interface that provides real-time analytics, historical data tracking and recommendations for farmers and agricultural experts.

The invention discloses a rice disease and insect pest early warning method and system based on image recognition, and particularly relates to the technical field of insect pest recognition, different areas of a rice field are comprehensively covered through multi-source high-resolution image collection, it is ensured that the image quality reaches a preset threshold value, median filtering and histogram equalization are adopted in the image preprocessing step, and the image quality is improved. Based on a geographic information system and a Kriging interpolation method, a spatial heterogeneity model of rice field disease transmission is established, a deep convolutional neural network is used for carrying out feature extraction on the image, the type, position and severity of diseases and pests are accurately recognized, the extracted features are combined with the spatial heterogeneity model, and the accuracy of the disease and pest detection is improved. A spatial autocorrelation analysis method is adopted to deeply evaluate the distribution mode of diseases in the rice field, and a support vector machine algorithm is applied to perform classification evaluation on the disease and pest risk, so that the problems of early warning lag and missing report caused by disease spatial heterogeneity are effectively solved, and the accuracy and timeliness of early warning are improved.

The invention provides a CT (Computed Tomography) image super-resolution reconstruction method based on a generative adversarial network. The method comprises the following steps of: 1, collecting high-resolution image data and corresponding low-resolution image data; 2, establishing a CycleGAN model comprising a first generator, a second generator, a first discriminator and a second discriminator; a third step of training a CycleGAN model by using the collected high-resolution image data and the corresponding low-resolution image data so as to optimize parameters of the CycleGAN model; and a fourth step of performing image conversion on the to-be-enhanced medical image which is input into the trained CycleGAN model.