251 results about "Multimodal image" patented technology

The invention discloses a diabetic foot early-stage detection method combining infrared and visible light imaging, and relates to the technical field of diabetic foot medical imaging diagnos.The diabetic foot early-stage detection method comprises the steps that multi-mode image collection and standardization processing are conducted, infrared images and visible light images are synchronously collected, and standardization processing such as size normalization is conducted; image registration and space alignment are carried out, marking points are set based on foot anatomical features, and feature extraction, mismatching point elimination and transformation matrix calculation are carried out; extracting and screening multi-dimensional features, extracting temperature and structural features, and screening by using a Relief-F algorithm; feature lesion recognition and classification are fused, and lesion probability is output through a double-branch convolutional neural network; carrying out detection result verification and feedback optimization, and comparing a clinical diagnosis optimization model; and generating a detection report and storing data, and generating a report containing the fused image. The early lesion detection precision is improved through multi-modal fusion, individual and environment differences are adjusted and adapted in a personalized mode, and reliable technical support is provided for clinic.

The invention relates to the field of medical treatment, and discloses a method for treating chronic insomnia by percutaneous magnetic stimulation of stellate ganglion block, which comprises the following steps: preprocessing: determining the initial position of stellate ganglion, tissue initial parameters and initial stimulation parameters through multi-modal image positioning and initialization to obtain reference data; dynamic sensing: collecting neck tissue displacement data, temperature field data and electromyographic signals in real time, and calculating spatial position variation of stellate ganglions and time-varying parameters of tissue electromagnetic characteristics; and modeling optimization: constructing a time-varying electromagnetic field distribution model based on the reference data, the spatial position variable quantity and the time-varying parameters. The method comprises the following steps: acquiring neck tissue displacement, a temperature field and an electromyographic signal in real time through reference data, capturing a stellate ganglion space position change and tissue electromagnetic property time-varying rule, constructing a time-varying electromagnetic field distribution model, and solving target stimulation parameters adaptive to a real-time tissue state through an optimal control algorithm.

A multimodal imaging unit for depth-surface imaging of a skin region of interest includes an ultrasound imaging transceiver, an optically transparent acoustic deflector, an optical module, and an optical camera sensor. An ultrasound beam emitted towards the acoustic deflector is deflected towards the skin region of interest, and an ultrasound beam reflected from the skin region of interest is returned to the ultrasound imaging transceiver. An optical beam from the optical module is passed through the acoustic deflector to a skin area of the skin region of interest and an optical beam reflected from the skin area is returned to an optical camera sensor through the acoustic deflector. The transceiver and the acoustic deflector are surrounded by an intermediary coupling medium and are hermetically enclosed by a cover provided with an acoustically and optically transparent access port for the ultrasound beams and the optical beams.

The invention discloses a multi-modal image fusion method and device. The method comprises the following steps: obtaining a visible light image and an infrared image, and carrying out the normalization preprocessing of the visible light image and the infrared image; global displacement and rotation parameters of the visible light image and the infrared image after normalization preprocessing are extracted based on cosine similarity, and coarse registration is achieved; dividing the image into overlapped sliding windows, and executing local thin plate spline transformation registration block by block; extracting global semantic features and local detail features of the visible light image and the infrared image after thin-plate spline transformation registration; fusing the global semantic features of the visible light image and the infrared image to obtain a global fusion feature; fusing the local detail features of the infrared image and the local detail features of the infrared image to obtain local fusion features; and fusing the global fusion feature and the local fusion feature to obtain a fused image. The detail information of the image can be captured, the accuracy and quality of the fused image are improved, and the generalization ability of the model is enhanced.

The invention discloses a visual inspection and evaluation method and system for the sealing quality of a liquid crystal display, and the method comprises the steps: generating a multi-dimensional space-time state map of a sealing region based on the time sequence multi-mode image data of a sealing technology after the time sequence multi-mode image data of the sealing technology is obtained; performing time sequence dynamic feature analysis on the multi-dimensional space-time state atlas, and extracting behavior pattern features of the sealing glue curing process; inputting the behavior mode characteristics of the sealing glue curing process into the colloid rheology-thermocuring coupling inversion model, and outputting a process parameter deviation interval and a material state distribution diagram; establishing a dynamic statistical reference behavior pattern library; and finally, carrying out matching and deviation degree calculation on the current behavior mode characteristics based on a dynamic statistical reference behavior mode library, and outputting a sealing compound quality comprehensive evaluation report and a process tuning guidance suggestion in combination with a process parameter deviation interval and a material state distribution diagram. According to the invention, deep dynamic diagnosis and root process optimization of the sealing glue curing process are realized, and the accuracy and initiative of quality control are improved.

The invention discloses an ore sorting system and method for real-time positioning and classification recognition based on deep learning, and relates to the technical field of AI vision, and the system comprises the following modules: an ore image multi-source fusion module which is used for collecting and preprocessing multi-modal ore images and fusing the multi-modal ore images to form a multi-modal image set; and the end-to-end deep learning reasoning module is used for combining an improved pyramid network structure and a local attention mechanism, extracting and fusing multi-scale feature information, performing reasoning calculation to output a recognition result, and obtaining a position coordinate, a category result and a judgment confidence coefficient of the ore. According to the method, through multi-modal image fusion and end-to-end deep learning reasoning, the position, category and contour information of the ore can be accurately recognized, the problem that the recognition rate of complex ore features, especially hard-to-process ores such as pock ores and linear ores, of a traditional method is low is effectively solved, the sorting precision is remarkably improved, and the sorting efficiency is improved. And mistaken discarding of concentrates and mistaken selection of tailings are reduced.

The invention provides an intelligent flame recognition management method, device and system based on multi-modal fusion, and relates to the technical field of computer vision, and the method comprises the steps: 1, collecting and preprocessing a visible light image and a thermal imaging image of a preset monitoring region, obtaining a processed visible light image and a processed thermal imaging image, and storing the processed visible light image and the processed thermal imaging image; inputting the processed visible light image into the trained flame feature extraction model to generate a visible light flame candidate frame; step 2, projecting a visible light flame candidate frame to a corresponding position of the processed thermal imaging image, generating an initial thermal imaging candidate frame, and performing neighbor sampling around the initial thermal imaging candidate frame to generate a plurality of thermal imaging sampling detection frames; according to the invention, through multi-modal image preprocessing, feature fusion identification, space and temperature risk positioning, key equipment temperature monitoring and hierarchical alarm storage, flame identification, effective analysis of a fire trend and intelligent risk management and control are realized, and the accuracy of flame identification management and the emergency disposal efficiency are improved.

The invention discloses an orthopedic surgery navigation positioning method and system based on three-dimensional registration, and belongs to the technical field of orthopedic surgery navigation. According to the method, through preoperative multi-modal image fusion reconstruction, intraoperative dynamic point cloud collection and an improved mixed registration algorithm, precise alignment of a preoperative model and an intraoperative actual skeleton is achieved, and by combining real-time error monitoring and dynamic compensation, precise positioning parameters of a surgical instrument are output to guide surgical operation; the system corresponds to the method and comprises an image acquisition module, a three-dimensional reconstruction module, a registration calculation module, a navigation guiding module, an error monitoring module and a control module, all the modules work cooperatively, and it is ensured that the navigation positioning precision is smaller than or equal to 0.3 mm, the registration time is smaller than or equal to 3.5 s, and the dynamic error compensation response time in the operation is smaller than or equal to 50 ms. The method does not need to depend on a physical marker, does not need a doctor to manually intervene the registration process, is high in anti-interference capability, is suitable for various orthopedic surgery scenes, effectively reduces the surgical risk, and improves the surgical efficiency and the standardization level.

This invention provides an intelligent detection method and system for PI defects in chip packaging based on multimodal images, mainly relating to the field of chip packaging defect detection technology. The method comprises the following steps: First, at least two modal images of the chip packaging site are acquired, including a color image mainly characterizing the surface morphology of the chip and a fluorescence image mainly characterizing the physical properties of the chip surface material; then, the color image and fluorescence image to be detected are input into a multimodal target detection model to perform defect detection and obtain preliminary detection results; finally, based on the preliminary detection results and combined with the grayscale feature analysis of the fluorescence image, intelligent posterior decision-making is performed to obtain the intelligent detection result of PI defects in chip packaging. This invention combines the joint detection of color and fluorescence images with intelligent posterior decision-making, solving the problems of inaccurate detection of abnormal PI thickness defects, high dependence on manual image interpretation, and difficulty in automatically distinguishing multiple types of PI defects in existing technologies.

This application relates to the field of medical image processing technology, and discloses a multimodal image recognition method and system for dynamic assessment of diabetic wound healing. The method includes acquiring infrared thermal imaging images, hyperspectral images, and visible light color images of a diabetic wound collected at the same time of visit; identifying the wound boundary between the diabetic wound and surrounding tissues in the visible light color image, and determining the central reference point of the visible light color image based on the wound boundary; establishing a virtual polar coordinate system based on the central reference point and preset anatomical reference markers; extracting tissue feature information characterizing the pathological state of the wound from the infrared thermal imaging image, hyperspectral image, and visible light color image, and mapping the spatial position of the tissue feature information in its corresponding image to the virtual polar coordinate system; normalizing the mapped tissue feature information, and calculating wound healing assessment indicators based on the normalized tissue feature information in the virtual polar coordinate system.

This disclosure is directed toward systems and methods for monitoring patients in a clinical setting by using non-contact multimodal imaging. For example, based on receiving imaging data, a computing device may identify an object and a region of focus formed by the object. The computing device may then cause a sensor to capture first sensor data at a first time and second sensor data at a second time, wherein the sensor data is associated with the region of focus. Based on determining that a difference between the first sensor data and the second sensor data is greater than or equal to a threshold difference, the computing device may generate and send, to an additional computing device, an alert indicative of the second sensor data.

The invention discloses a two-dimensional code recognition and defect repair method and device, and the method achieves the effective integration of visible light, infrared and ultraviolet image features through the collection and preprocessing of a multi-modal image, greatly improves the feature recognition degree of a two-dimensional code under a complex background, and solves a problem that a conventional method is poor in adaptability to the complex background. According to the lightweight convolutional neural network defect detection model, pixel-level defect segmentation and multi-dimensional classification are realized, a defect severity grading mechanism is combined, an accurate basis is provided for intelligent defect evaluation, and the defect of lack of intelligent evaluation in the prior art is made up; for self-adaptive repair strategies of defects of different types and levels, the advantages of multiple algorithms such as texture synthesis, error correction coding and geometric correction are fused, and the repair precision of the two-dimensional code is greatly improved; a closed-loop verification mechanism of repair and recognition realizes linkage optimization of a repair process and a recognition result by dynamically adjusting repair parameters, and the problem of low efficiency caused by separation of repair and recognition is effectively solved.

The invention discloses an urban environment dynamic monitoring system and method based on deep learning, and the method comprises the steps: collecting multi-modal image data, eliminating the modal difference through a self-adaptive normalization algorithm, generating multi-modal features, introducing an attention mechanism to carry out the weighted fusion of the multi-modal features, and obtaining the fusion feature data; an FPN multi-scale feature pyramid network is constructed to carry out pollution source identification on fused feature data, a DSAM dynamic space attention module is embedded to adaptively adjust feature channel weights, and pollution source categories and position coordinates are output; the category and position coordinates of the pollution source are predicted based on an MTL multi-task learning framework, a space-time diagram convolutional network is introduced to evaluate the environmental quality, and an environmental pollution thermodynamic diagram is output; and outputting an urban environment governance strategy through a dynamic decision tree model according to the environmental pollution thermodynamic diagram. The positioning error is reduced, and the accurate traceability requirement is met.

This invention discloses an intelligent early warning method and system for leased network quality. The method specifically includes: fusing multi-source packet loss time-series data using a federated learning framework and constructing a global micro-packet loss prediction model; fitting the current packet loss time-series data using the global micro-packet loss prediction model to generate a corresponding predicted data sequence; performing multi-time-scale comparative analysis based on the fused multi-source packet loss time-series data and the predicted data sequence to detect data offsets; generating a multimodal image set if an offset is detected; performing multimodal fusion recognition based on the multimodal image set to determine whether continuous fluctuation characteristics exist and to locate the root cause of the anomaly; and evaluating the alarm confidence level to trigger an alarm. This invention can promptly detect micro-packet loss problems in leased networks and accurately determine the root cause of anomalies, achieving intelligent early warning and dynamic monitoring strategy adjustment, improving the efficiency of leased network quality monitoring, enhancing user experience, and ensuring system security.

The invention provides a coaxial telecentric imaging tinplate can defect identification method and system, and relates to the technical field of defect identification, the system is composed of a coaxial telecentric camera assembly, a polarization multispectral light source assembly and an image processing and control unit, and can body images with constant magnification are obtained through a telecentric lens and coaxial illumination; a switchable cross-polarization visible light and near-infrared light source is used for synchronously collecting multi-modal images, and separation and difference are carried out on different polarization and wave band images to obtain a specular reflection component image, a diffuse reflection component image and a multi-spectral difference image. The image is divided into a character area, a pattern area and a bottom color area according to the format of the can body, comprehensive judgment is conducted through character comparison, a lightweight neural network and defect area connectivity analysis, automatic recognition of defects such as scratches, through printing and smudginess is achieved, and the accuracy and stability of appearance detection of the tin can on a high-speed production line can be improved.

The application relates to a method for optimizing multimodal imaging and a multimodal imaging system. Based on the multimodal imaging system, an optical coherence imaging module converts collected optical coherence imaging signals into interference photoelectric signals, an optical phase extraction module calculates a wavefront phase diagram according to the interference photoelectric signals, and an adaptive signal processing module generates a driving signal according to the wavefront phase diagram, so that a spatial light modulator in an adaptive optical module is dynamically controlled, the spatial light modulator dynamically adjusts the wavefront phase of incident laser, higher-precision wavefront distortion compensation is realized, and the imaging quality of the multimodal imaging is optimized. The method utilizes the deep penetration and layer cutting capabilities of the optical coherence imaging mode, simultaneously has full field of view and high-speed regulation and control capabilities, accurately adjusts the phase, and realizes adaptive optimization. The method provided by the application improves the overall imaging effect of the multimodal imaging, and promotes the wide application of the multimodal imaging system in the fields of disease detection and scientific research.

The invention belongs to the technical field of image fusion, and discloses a deep potential low-rank representation infrared and visible light image fusion system and method. According to the method, a preliminary salient image and a basic image are obtained through pre-decomposition; acquiring a common basic low-rank matrix and a common significant low-rank matrix of the significant image and the basic image by adopting a deep potential low-rank representation method, further acquiring the significant image and the basic image, and acquiring a first fusion image; and continuously taking the basic image and the detail image obtained by each time of depth decomposition as the input of the next layer of depth potential low-rank representation decomposition to obtain a fused image of each layer of decomposition. According to the method, a deep potential low-rank representation multi-modal image fusion framework is developed by utilizing complementary information of multi-modal sensor image data, a salient image and a basic image obtained through pre-decomposition are used as input, deep potential low-rank representation decomposition fusion is carried out, and a deep potential low-rank representation multi-modal image fusion framework is developed. And deep feature information of the image can be obtained by utilizing the rank matrix.

The invention provides an air unmanned aerial vehicle group target detection method and device based on YOLOv7-tiny, and relates to the technical field of target detection. The method comprises the steps that a data set is constructed and preprocessed; the method comprises the following steps: constructing a network based on YOLOv7-tin, introducing a multi-modal image level domain adaptive module into a backbone network, realizing cross-modal feature calibration through FiLM modulation, and aligning domain distribution in combination with an adaptive gradient inversion strategy; a multi-scale focusing modulation module is introduced between the backbone network and the neck network, and the multi-scale target sensing capability is improved through adaptive multi-scale context modeling and geometric enhancement convolution; a lightweight multi-constraint intersection-parallel ratio positioning loss function is adopted, and the positioning precision is optimized through piecewise linear enhancement, shape consistency constraint and center offset constraint in combination with dynamic weight scheduling. According to the method, the problems of feature offset, difficulty in multi-scale target detection and real-time performance and precision balance caused by environmental heterogeneity are effectively solved.

The invention discloses a railway line environment multi-modal image target extraction method, which comprises the following steps of S1, acquiring a visible light image along a railway through an unmanned aerial vehicle, and performing three-dimensional field reconstruction based on NeRF to obtain a three-dimensional scene; s2, defining a target visibility evaluation function, and finding a visual angle enabling the target visibility evaluation function to be maximum; s2, using NeRF to render the image under the visual angle in the three-dimensional scene obtained in the step S1, and re-projecting a rendering result as an optimal visual angle image under the visual angle; s3, preprocessing data of different modes from different platforms, then converting the data and the optimal view angle image obtained in the S2 into a unified coordinate system, and obtaining a multi-mode coarse registration relation through affine transformation; and S4, target area selection and target extraction are carried out based on the improved Ground DINO network. The method effectively improves the reliability and robustness of a target detection result in a complex railway line scene.

This invention discloses a multimodal image real-time registration and fusion system and method for cardiac interventional surgery, comprising: a multimodal image acquisition module, an image data transmission module, an intraoperative twin image registration and analysis platform, an algorithm calculation module, a parameter adjustment module, and an image fusion output module. After acquiring image data, the multimodal image acquisition module sends it to the intraoperative twin image registration and analysis platform via the image data transmission module. The platform incorporates a cardiac deformation adaptive optical flow algorithm, a multi-resolution hierarchical mapping algorithm, and a large-deformation cardiac image registration algorithm. The algorithm calculation module performs registration calculations, and the parameter adjustment module optimizes the fusion parameters based on the calculation results. Finally, the image fusion output module generates and outputs the fused image. This invention achieves real-time and accurate multimodal image registration and fusion, providing reliable image support for cardiac interventional surgery and improving surgical accuracy and safety.

The invention provides an image processing method and an image processing device, and relates to the field of computers. According to the method, feature extraction is carried out on multi-modal image data of functional magnetic resonance image data and structural magnetic resonance image data of a brain, feature fusion is carried out, classification is carried out based on fused features, an image recognition result is obtained, feature extraction is carried out based on a channel attention and space attention decoupling method, and the recognition accuracy is improved. According to the method, the image features can be extracted more accurately, the accuracy of the image recognition result is further improved, and diagnosis of mental diseases such as bidirectional affective disorder and Alzheimer's disease can be better assisted.

This invention discloses a battery health diagnosis method based on multimodal image fusion, relating to the field of battery health diagnosis technology. The method includes the following steps: simultaneously acquiring visible light and infrared thermal imaging images of the battery, and dividing the battery into local diagnostic units based on prior data; extracting visible light and infrared thermal imaging feature vectors from the local diagnostic units, and generating cross-modal consistency constraints by calling a pre-constructed cross-modal consistency constraint model; calculating the bimodal correlation degree of each unit by combining the feature vectors and cross-modal constraints to determine the unit failure mode; when the failure mode is a high-confidence failure, analyzing its spatial expansion trend using a spatial clustering algorithm based on graph connectivity; and generating a battery health diagnosis result by combining the unit failure mode and spatial expansion trend. This invention significantly reduces the impact of environmental interference and occasional anomalies through collaborative analysis and consistency constraints of multimodal images.

This invention discloses a multimodal image visualization method for discharge defects in transmission lines, comprising: 1. acquiring three-modal images and defining true defect labels; 2. constructing a feature extraction module and a feature purification module to obtain purified features for each modality; 3. constructing a cross-modal attention fusion module to achieve adaptive fusion of features from the three modalities and constructing a cross-modal consistency loss function; 4. constructing a multi-task loss function, comparing the model prediction results with the true labeled information, and using the SGD optimizer for backpropagation iterative training to obtain a converged defect detection model; 6. constructing a prediction module to predict the location, category, and confidence level of the defect; 7. drawing defect detection boxes on the visualized image and labeling them with type and confidence level, and overlaying ultraviolet and infrared heatmaps to achieve the visualized output of multimodal fusion. This invention can effectively suppress background interference, thereby enabling automated detection of small discharge defects caused by high-voltage arcs in transmission lines.

Aspects of the disclosure relate to the use of an optical detection scheme enabling multiple imaging sub-systems concurrently optimized for retrieving multiple characterization modalities. The feature described is an offset illumination and detection arrangement for a multimodal imaging probe. The imaging probe comprises multiple waveguides disposed within a torque-transfer coil, distally terminating at longitudinally separated positions. A first waveguide may have focusing optics for focused illumination and / or detection. Any number of other waveguides may be deployed for illumination and / or detection having no focusing optics. The disclosure enables high-fidelity multimodal imaging systems.

This invention discloses a deep learning-based intelligent AOI defect classification and detection system for PCBs, comprising a multimodal image acquisition module that simultaneously acquires optical and infrared images of the PCB board, a hierarchical decision classification module that receives image pairs and sequentially performs defect localization, multi-branch feature fusion, defect classification, and confidence output, and a feedback optimization module that filters samples based on confidence and triggers incremental learning. By introducing an infrared thermal imaging module that is triggered synchronously with electrical performance testing and fusing it with high-resolution optical images, this invention constructs a multi-physical-dimensional detection information source. This enables the system to capture defect features that are difficult to detect using traditional pure optical methods, thereby significantly improving the detection rate and classification accuracy of hidden and complex defects, and solving the problem of missed detection and misjudgment caused by a single dimension of detection information.

This invention relates to the field of activated carbon raw material screening, providing a method and system for activated carbon raw material screening based on multimodal image feature fusion. The method includes: acquiring original images of edge contours and surface textures, constructing a set of binary edge contour images and a set of grayscale surface texture images; extracting edge chain codes to generate edge curvature sequences, extracting effective cutting feature vectors based on coating damage critical thresholds, and mapping them to generate a physical wear risk index; mapping the texture region of interest to a three-dimensional grayscale topological surface, calculating fractal dimension feature values ​​and the proportion of hygroscopic texture, and coupling them to generate an environmental hygroscopic preemption index; calculating the compatibility score of color-changing beads using an environment-medium dual-constraint weighted algorithm, and performing multi-path diversion decisions based on optimization thresholds, elimination thresholds, and physicochemical test data to generate particle grading control instructions. This invention achieves intelligent screening and resource scheduling of activated carbon particle microstructure through synchronous time-division optical field imaging and multimodal feature coupling.

The invention provides a robustness perception and attention propagation fused modal deletion semantic segmentation method, which comprises the following steps: a semantic segmentation part: preprocessing a multi-modal image; extracting each modal feature by using an encoder sharing the weight, and independently projecting the modal to a semantic space; evaluating the robustness of each modal feature, and performing weighted fusion to obtain a fused semantic feature; taking each modal semantic feature as a key value pair, taking the fused semantic feature as a query, and performing attention propagation fusion and segmentation to obtain a prediction result; the network model training part is used for preprocessing a training image and extracting and projecting each modal feature; sampling and extracting modal features based on modal robustness; all modal features are subjected to weighted fusion in full-modal mixed training, the extracted modal features are directly used in fragile modal independent training, and attention propagation fusion and segmentation are carried out respectively to obtain prediction results; and combining the loss of the full-mode mixed training and the loss of the fragile-mode independent training to carry out network optimization so as to realize semantic segmentation.

The application discloses a kind of based on text guide's multimodal image fusion method and system, belong to image fusion technique, its method includes the following steps: using hierarchical encoder respectively to infrared image and visible light image are extracted, and different levels of infrared features and visible light features are obtained;Text semantic features of input task text are obtained using CLIP text encoder;Infrared features and visible light features are respectively mapped using spatial affine block, and the mapping result is modulated to text semantic features, to obtain the text guide feature under the same spatial resolution;After the most deep level infrared feature and visible light feature are decomposed and fused, input multistage semantic interaction decoder is restored into high-resolution feature of different levels, each layer high-resolution feature is modulated using channel affine block combined with text semantic features, and the modulation result is mixed and fused with text guide feature.The limitation of image fusion model under degradation condition is improved.

The invention discloses an equipment fault source positioning method and device based on multi-modal information fusion, and the method comprises the steps: carrying out the data collection of a power distribution room inspection robot which carries an infrared thermal imager, a visible light camera and a self-adaptive lifting mechanism, carrying out the preprocessing of a collected infrared image, constructing a power distribution room equipment data set, and carrying out the positioning of a power distribution room equipment fault source. The power distribution room equipment data set comprises a plurality of image pairs composed of infrared images and visible light images which are in one-to-one correspondence; performing multi-modal image registration on each image pair based on an improved whale optimization algorithm to obtain a registered image pair; fusing the registered image pair by using a deep learning fusion network containing a coordinate attention module to obtain a fused image; and carrying out fault source positioning based on the relative temperature difference and a Monte Carlo method according to the fused image. According to the invention, accurate identification and grade determination of a fault source are realized through four stages of processes of multi-modal data acquisition, accurate image registration, high-quality image fusion and automatic fault positioning.

The invention provides a multi-modal image fusion model, method and device based on high-frequency enhancement and boundary guidance and a storage medium, and belongs to the technical field of image fusion based on computer vision. A high-frequency enhancement module named as HFEB is designed, and the module comprises a set of filters focusing on enhancing input signal high-frequency information. After the HFEB is introduced into the fusion network, the perception quality and the distortion measure are obviously improved. In addition, in order to prevent loss of image details and boundary blur in the fusion process, a boundary perception guide module BAGM is introduced for auxiliary fusion. By using the boundary information, the details and the structure of the image can be better maintained, and important features can be more effectively highlighted. A large number of experiments on a reference data set prove the effectiveness of the proposed method, and show the superior performance of the method compared with the most advanced technology in the prior art.