141 results about "Fusion image" patented technology

The invention discloses an intelligent detection method and device for fusing a medical image learning image, and relates to the technical field of medical image processing. The method comprises the following steps: acquiring and preprocessing a bimodal medical image, and extracting a feature map through multi-scale decomposition; constructing a cross-modal correlation model, and setting a modal attention mechanism (embedding anatomical structure prior guidance feature complementation) and a morphological attention mechanism (setting lesion morphological constraint weight); the method comprises the following steps: collecting multiple types of image samples, pairing according to a focus form and an imaging mode to construct a bimodal joint data set, and correlating and labeling to generate a training data set with modal attributes; after a multi-stage iteration training model, inputting the preprocessed image to carry out feature fusion so as to obtain a fused image; and generating a lesion probability graph according to the fused image, positioning a lesion area through multi-threshold segmentation, and outputting a detection result. The system comprises a data acquisition module, a preprocessing module and the like. The method improves the accuracy and reliability of medical image detection, and is suitable for clinical multi-modal image analysis.

The invention discloses a plateau mountain road disaster identification method and system based on multi-source remote sensing image restoration and super-resolution reconstruction. The method comprises the following steps: acquiring and preprocessing a multi-source remote sensing image of a plateau mountain region, and extracting landform measurement parameters based on a digital elevation model; a super-resolution reconstruction network fusing deformable convolution and Transform is constructed, and a low-resolution image is reconstructed by using constraint training of a composite loss function containing geomorphic measurement parameters; performing feature extraction and adaptive weighted fusion on the preprocessed image and the reconstructed high-resolution image; based on the fused image, utilizing a multi-task deep learning model to identify landslide, debris flow and roadbed subsidence disasters along the highway; and carrying out morphological optimization and boundary refinement under GIS constraint on an identification result, and outputting a disaster thematic map. According to the invention, the precision and reliability of road disaster identification in a complex terrain environment are effectively improved.

The invention relates to the technical field of medical image processing, in particular to an engineered heart tissue pulsation trajectory automatic analysis method based on an image recognition algorithm, which comprises the following steps: S1, multi-modal image fusion: performing space-time registration and feature fusion on acquired multi-modal heart images to generate a fused image sequence; s2, cardiac muscle tissue segmentation: outputting a cardiac muscle tissue segmentation result with a timestamp; s3, motion track modeling: generating three-dimensional track point cloud data in a pulsation period; s4, feature parameter extraction: performing spatial-temporal feature analysis on the track point cloud data, and extracting multi-dimensional motion parameters; and S5, heterogeneity atlas generation: generating a cardiac pulse heterogeneity atlas according to the multi-dimensional motion parameters. According to the method, automatic analysis of the cardiac pulse track and generation of the heterogeneity atlas based on the multi-modal image and space-time modeling are realized, and the precision and the intelligent level of cardiac motion anomaly recognition are remarkably improved.

The invention discloses a brain tumor multi-mode large model construction method, device and equipment and a storage medium, and is applied to the technical field of brain tumor imagines.The method comprises the steps that pixel-concept level alignment is conducted on a multi-mode MRI image and a pathological text; constructing a multi-modal feature fusion network for fusing image features and text features by adopting an attention mechanism of pathology perception and combining medical semantic information; training the multi-modal feature fusion network to generate an analysis report and a segmentation result; according to the technical scheme of multi-task cooperation, cross-modal pathological semantic accurate alignment, pathological knowledge graph injection and lightweight and continuous optimization parallelization, full-process coverage of brain tumor accurate segmentation, analysis report generation and prognosis prediction is achieved, the problems that a traditional model lacks pathological semantic support and is insufficient in clinical adaptability are solved, and the clinical adaptability of the traditional model is improved. And the deployment feasibility and the dynamic optimization capability are also considered.

The invention discloses a liver and gall lesion early screening system and method based on image fusion, and relates to the technical field of medical image processing and computer-aided diagnosis, and the method comprises the following steps: reconstructing a multi-modal image space-time coordinate system under a unified event time baseline, generating a respiratory displacement field and a magnetic sensitive pulse fingerprint, and constructing an artifact suspicion map; and performing anti-fact playback based on the artifact suspicion chart, performing frame-by-frame playback on the image acquisition sequence, quantifying artifact superposition tracks with consistent directions, and solidifying an artifact anchor point set. According to the method, space-time coordinates are constructed based on a unified event time baseline, a breathing displacement field and magnetic sensing pulse fingerprints are introduced, anti-fact playback, distortion kernel inference and residual decoupling are combined, artifact recognition and fusion intervention are achieved, and artifact closed-loop elimination is completed by judging threshold-driven fusion regulation and time reversal phase gating, so that the artifact recognition accuracy is improved. And the fused image authenticity is improved.

The invention provides a laryngeal cancer multi-mode prognosis prediction method and a laryngeal cancer multi-mode prognosis prediction system fusing a CT (Computed Tomography) image and a ViT model. Relates to the technical field of biomedical images. The method comprises the following steps: acquiring and preprocessing multi-modal data of a laryngocarcinoma patient; carrying out lightweight compression, redundant information screening and robustness training on the ViT model to obtain an optimized ViT model; extracting depth features of the CT image data based on the optimized ViT model, and performing multi-stage fusion on the depth features and clinical and genome data to construct a prognosis prediction model; and performing risk stratification on the patient according to a prognosis prediction result predicted by the prognosis prediction model, and outputting treatment guidance suggestions based on the risk stratification. Through ViT model optimization, multi-modal data fusion and clinical adaptation design, precise prediction and personalized treatment guidance of laryngocarcinoma prognosis are realized, and the problems of insufficient image degradation processing, low model deployment efficiency and the like in existing laryngocarcinoma prognosis prediction are solved.

The invention discloses a breast cancer prognosis real-time evaluation system and method fusing a multi-modal image and a co-disease network, and relates to the technical field of breast cancer prognosis evaluation. According to the system, a comprehensive feature matrix fusing images, genes and clinical features is constructed by acquiring a mammary gland medical image, extracting focus features and combining gene expression information of a focus area and co-disease data of a patient, feature weighting is carried out based on an attention mechanism, and finally a prognosis risk score is output by utilizing a multi-layer perceptron model. And risk grading and intervention suggestion generation are realized. According to the system, a co-disease network and a gene interaction network are introduced for modeling, relevance expression among multi-source information is enhanced, and pathological features and systematic health status of patients can be reflected comprehensively. The intelligent level of prognosis evaluation can be improved, and good clinical popularization value is achieved.

The invention discloses a remote sensing image space-time fusion method and device based on a selective state space model, and belongs to the technical field of remote sensing image processing and computer vision crossing. The method comprises the following steps: acquiring high-resolution and low-resolution image input, and extracting multi-scale features through a multi-layer encoder; capturing an anisotropic space structure in the remote sensing image by using a four-way two-dimensional selective scanning mechanism; designing a state space fusion module, decoupling and cooperatively processing space details and time dynamic information through a space and time sequence selective scanning fusion sub-module, and performing feature fusion by adopting adaptive gating parameters; and finally, reconstructing a high-resolution image through a symmetric decoder, and carrying out model optimization by adopting a composite loss function. On the premise of ensuring the linear calculation complexity, the spatial detail fidelity, the time continuity and the overall efficiency of the fused image are remarkably improved, and the method is suitable for large-scale remote sensing data processing.

The invention discloses a CT image analysis method and system based on a neural network, and relates to the technical field of CT image analys.The method comprises the steps that an original CT image is obtained after user authorization, a Laplace operator is adopted to strengthen a focus boundary, and a circular region of interest is intercepted to remove edge sensitive information; extracting edge and texture information in the standardized image; focus area features are focused step by step; executing characteristic distillation balance based on category sample distribution, and outputting a focus characteristic graph with local perception enhancement and sample balance characteristics; segmenting the lesion feature map into serialized units, embedding position codes, inputting the serialized units into a plurality of layers of encoders, and fusing an image structure and text indication information through a dynamic adjustment mechanism; performing linear classification on the global semantic vector to output a diagnosis result, generating a focus thermodynamic diagram, and superposing the focus thermodynamic diagram to an original image for visualization; and performing dynamic optimization based on doctor feedback. The accuracy of feature analysis is improved; the overall operation efficiency of the system is improved.

The invention specifically discloses an automatic generation method for a cervical vertebra disease rehabilitation prescription, and the method comprises the steps: constructing a cervical vertebra image feature automatic measurement system based on computer vision and deep learning, so as to monitor the training motion quality of a patient in real time, and obtaining the kinematics parameters of rehabilitation exercise training; acquiring a medical image and clinical data of a patient, and standardizing the medical image to construct a standard space of a multi-modal medical image; performing automatic segmentation and anatomical structure calibration on a vertebral body-intervertebral disc based on the standard space to obtain fusion image features; and constructing a multi-modal hierarchical decision model fusing the image features, the kinematics parameters and the clinical data to carry out quantitative analysis on pathological feature parameters, and outputting a standardized illness state assessment result conforming to international clinical guidelines. The dynamic optimization of the treatment scheme can be realized through a feedback mechanism, and the treatment effect is improved.

The invention relates to the field of remote sensing image processing and geological disaster monitoring, in particular to a goaf three-dimensional subsidence basin reconstruction system based on multi-source remote sensing image fusion, which comprises a heterogeneous data preprocessing module, a differential geometry-based data fusion module, a three-dimensional subsidence basin reconstruction module, a subsidence dynamic monitoring module and a virtual reality interaction module, the system innovatively introduces a differential geometry theory to solve the problem of heterogeneous fusion of a high-resolution optical satellite image, an SAR satellite image and airborne remote sensing Lidar point cloud data, maps multi-source data to a unified feature space through manifold learning, calculates an adaptive fusion weight through curvature analysis, constructs a multi-scale feature correlation matrix based on a geodesic line distance, and achieves the fusion of the high-resolution optical satellite image, the SAR satellite image and the airborne remote sensing Lidar point cloud data. A high-precision fusion image is generated, three-dimensional reconstruction is carried out in combination with subsidence basin geological parameters and a physical constraint method, it is ensured that a reconstruction result conforms to an actual subsidence physical rule, dynamic monitoring and virtual reality interaction of the subsidence process are achieved, and parameter adjustment and model optimization are supported.

The embodiment of the invention discloses a cervical cancer wettability risk assessment method and system based on three-dimensional ultrasound and tomographic ultrasound imaging. The method comprises the steps that three-dimensional ultrasound image data and tomographic ultrasound imaging data of a target cervical region are acquired; carrying out preprocessing and image registration on the three-dimensional ultrasonic image data and the tomography ultrasonic imaging data; fusing the spatial structure information and the chromatography tissue information by adopting a weighted fusion algorithm to generate fused image data containing multi-dimensional features; performing feature extraction on the fused image data by using a feature extraction model, and combining morphological features and histological features to generate a high-dimensional feature vector; extracting hemodynamic parameters based on the three-dimensional ultrasonic image data; inputting the high-dimensional feature vector and the hemodynamic parameters into a risk assessment model to generate a cervical cancer wettability risk score; according to the risk score, outputting a cervical cancer wettability risk assessment result; and high-precision cervical cancer wettability evaluation is realized.

The invention belongs to the technical field of remote sensing image processing, and discloses a remote sensing image space-time fusion method and system, and the method introduces a Mama module in a space domain and a frequency domain, so as to enhance the extraction and modeling capability of multi-scale features and structural information. In a spatial domain, the Mama module is used for capturing a local structure and geometric details of an image; in a frequency domain, for a high-frequency sub-band after wavelet transform, a Mama module is introduced to carry out feature extraction, so that the context dependency relationship and structural features of high-frequency information are more fully mined. Double-domain interactive fusion is realized through a channel attention and space attention mechanism, the space definition and spectrum consistency of the fused image are improved, the method has strong generalization and adaptability, space domain and frequency domain information is fully utilized, and the precision and efficiency of obtaining the remote sensing image are improved.

The invention discloses a deep learning river ice extraction method based on an optical and SAR fusion image, and belongs to the technical field of remote sensing geoscience application. The method comprises the following steps: acquiring an optical image and an SAR image which are in the same area and contain river ice distribution, preprocessing the optical image and the SAR image, marking river ice areas of the preprocessed optical image and the preprocessed SAR image, and constructing a training data set; a deep learning extraction model is constructed, and the training data set is used for training; and obtaining an optical image and an SAR image of a to-be-extracted river ice distribution region, obtaining a binary image by using the trained deep learning extraction model, converting the binary image into vector data, removing a misjudged river ice region, obtaining a river ice region in the region, and completing river ice extraction. According to the method, advantages of optics and SAR images are combined, an attention mechanism and a multi-level feature fusion strategy are constructed, and technical support is provided for refined river ice drawing in the cold and cold mountainous area.

The invention relates to the technical field of image updating, in particular to a multi-modal image real-time updating method and system for temporal bone surgery. The method comprises the following steps: acquiring a real-time multi-modal image, performing pixel-by-pixel frequency domain reconstruction optimization, and constructing a frequency spectrum enhanced fusion image; performing reverse geometric transformation compensation on the spectrum enhancement fusion image to obtain a space alignment optimization image; performing real-time visual contrast enhancement on the space alignment optimization image, and constructing a visual enhancement image; carrying out inter-frame difference calculation on the vision enhanced image, carrying out real-time increment updating optimization, and constructing an increment updating image sequence; and performing multi-level cache rendering management and parallel execution based on the incremental updating image sequence. The temporal bone surgery safety and efficiency are improved through real-time and efficient image updating.

The invention relates to the technical field of medical images, in particular to a multi-modal medical image registration and fusion analysis method, which comprises the following steps of: eliminating image noise and artifacts based on a modal adaptive filtering strategy; constructing a pyramid type feature extraction network to realize multi-scale feature extraction, calculating feature matching degrees among different modal images, and dynamically adjusting matching weights by combining feature differences among modals; a focus area attention mask is constructed, targeted enhancement of registration image features is realized, and a hierarchical fusion strategy is adopted to evaluate the quality of a fused image; and constructing a multi-task deep learning model to complete focus automatic detection, segmentation and benign and malignant preliminary judgment on the fused image. According to the multi-modal medical image registration and fusion analysis method, a three-layer feature pyramid is constructed, a multi-feature fusion matching cost function is introduced, and cross-modal feature matching is optimized through an adaptive weight iteration nearest point ICP algorithm, so that the information richness, marginal definition and focus discrimination of a fused image reach the standard.

The invention discloses a multi-time sequence SAR remote sensing image marine fixed facility identification method. The method comprises the following steps: acquiring multiple time sequence SAR remote sensing images; preprocessing the SAR remote sensing image to generate a preprocessed image; segmenting seawater and non-seawater ground features in each time sequence preprocessed image to generate a multi-time sequence binary image; filtering dynamic targets in non-seawater surface features from the multi-time-sequence binary image, retaining static targets, and synthesizing a fused image; performing mask processing on the fused image to obtain an achievement image only containing the offshore fixed facility; and judging whether the change of the marine fixed facility is identified, if so, performing grid subtraction on each fused image in the previous and later time periods to obtain a changed grid image, otherwise, converting the result image into vector data, performing area screening and geographic coordinate extraction on the vector data, and outputting the geographic position information of the marine fixed facility. According to the invention, high-precision identification of the offshore fixed facilities can be realized.

The invention provides a ship detection-oriented multi-source remote sensing image fusion method, a ship detection-oriented multi-source remote sensing image fusion medium and a ship detection-oriented multi-source remote sensing image fusion system, and belongs to the technical field of remote sensing images. Constructing an electromagnetic scattering constraint model based on physical optical approximation and a geometric diffraction theory to calculate a predicted value of the backscattering cross section of the target, introducing a fusion cost function, generating a fusion weight matrix by adopting an adaptive feature weighting model, and performing multi-scale fusion on the image through hypercomplex wavelet transform; and iteratively optimizing the fusion result by using an information diffusion heat conduction equation, and performing physical consistency correction to output an enhanced fusion image, thereby solving the technical problem of poor physical consistency of the fusion result caused by lack of physical model constraint when the synthetic aperture radar image and the multispectral image are fused.

The invention relates to the technical field of medical image processing and intelligent grading recognition of tumors, and discloses a head and neck squamous cell carcinoma early recognition method based on radiomics and deep learning, which comprises the following steps: acquiring head and neck CT (Computed Tomography), MRI (Magnetic Resonance Imaging) and PET (Positron Emission Tomography) three-mode image data; a two-stage dynamic structure alignment mechanism is adopted for registration; extracting fusion radiomics features; constructing a tumor sub-feature map; outputting a tumor level and a prediction confidence coefficient through the uncertainty prediction model; and generating a saliency interpretation heat map. In the prior art, a single-mode image or a superficial layer texture feature extraction model is depended on, and especially under the condition that a heterogeneity tumor region boundary is fuzzy and different modes have significant structure offset, high-confidence accurate discrimination of a real staging state of a tumor cannot be realized. According to the method, the accuracy of early stage identification of the head and neck squamous cell carcinoma is improved by introducing the significance guide registration mechanism and the improved graph convolutional neural network and combining Dropout reasoning.

The invention provides a building extraction method, system and device based on multi-source remote sensing data fusion. The method comprises the steps that original satellite hyperspectral image data of a to-be-detected target area is acquired and preprocessed; constructing a multi-scale collaborative observation system combining a satellite hyperspectral image and an unmanned aerial vehicle high-resolution image based on the preprocessed hyperspectral satellite image, and extracting a building candidate area; obtaining unmanned aerial vehicle image data based on the coordinate data of the building candidate area; performing data fusion and sub-pixel positioning optimization based on the unmanned aerial vehicle image data to generate a hyperspectral fusion image; and performing target positioning and edge extraction on the hyperspectral fusion image based on the boundary refinement model to obtain an extracted building area. According to the method, the problems of contradiction between the coverage range and the spatial resolution, fuzzy shielding scene edge, insufficient multi-dimensional data utilization rate and the like of hyperspectral building extraction are solved.

The invention provides a three-dimensional medical image segmentation method and system for thyroid-related eye diseases, and is applied to the technical field of data processing. Multi-modal medical image data, a coarse-grained segmentation result and a fine-grained segmentation result are obtained, the multi-modal medical image data comprise a CT image and an MRI image, the multi-modal medical image data are preprocessed, a fused image body is generated, and the fused image body is formed by registration and feature fusion of the CT image and the MRI image; processing the fused image body and the coarse-grained segmentation result to generate a fine segmentation result; processing the fine segmentation result to generate a preliminary segmentation mask; processing the preliminary segmentation mask and the multi-modal medical image data to generate a mixed loss function; and processing the preliminary segmentation mask, the mixed loss function and the extraocular muscle attachment point position based on a dynamic post-processing strategy to generate a final segmentation result.

The invention relates to the technical field of medical image processing, and discloses a PCNL fusion image puncture and lithotripsy navigation method and equipment based on optical positioning. The method comprises the following steps: acquiring a real-time ultrasonic image of a target object, and fusing the real-time ultrasonic image with a pre-acquired three-dimensional CT image of the target object to obtain a fused image; establishing a simulated skin-kidney channel according to the fused image; wherein the simulated skin-kidney channel is used for assisting in constructing a lithotripsy path for a target object; acquiring pose information of a navigation marker of the endoscope relative to the target object in real time, and displaying the pose information in the three-dimensional CT image in real time to obtain a target three-dimensional CT image displaying the pose information of the navigation marker of the endoscope in real time; wherein the target three-dimensional CT image is used for assisting in navigating the lithotripsy position of the target object.

The application discloses a remote sensing image cloud area reconstruction method based on SAR prior knowledge guidance. The method firstly inputs the obtained optical remote sensing image into a cloud detection module to obtain a preliminary cloud detection result, then constructs a SAR feature extraction module, inputs the obtained adjacent time phase SAR image into the SAR feature extraction module, constructs a polarization covariance matrix of the SAR image, extracts relevant polarization scattering features, finally constructs a cloud area image reconstruction module, and sequentially inputs the obtained optical remote sensing image, the cloud detection result and the polarization scattering features into the cloud area image reconstruction module, so that the spatial structure feature reconstruction and the global consistency feature repair of the cloud-shielded area of the optical remote sensing image are realized, and finally a high-fidelity cloud-free image is generated. The application effectively improves the feature recovery capability of the cloud-shielded area of the optical remote sensing image by fusing the SAR image texture and polarization features, and solves the problems of optical remote sensing image information blurring and partial information loss caused by serious cloud and fog interference.

The present disclosure provides a multi-temporal remote sensing image fusion method, device, equipment and storage medium. The method of the present disclosure comprises: reading and preprocessing multi-temporal remote sensing image data; performing linear normalization for each band of the multi-temporal remote sensing image data and saving global normalization parameters; initializing an adaptive Kalman filter for each pixel point in the multi-temporal remote sensing image data, the initialization of each adaptive Kalman filter comprising setting a forgetting factor of the adaptive Kalman filter; performing processing using the adaptive Kalman filter to determine the pixel value of each pixel point in the fused image data to obtain fused image data, the processing of the adaptive Kalman filter comprising a prediction operation and an update operation, the update operation comprising adjusting an observation noise covariance matrix using the forgetting factor; de-normalizing the fused image data according to the global normalization parameters; saving the fused image data and setting its geographic information. The present disclosure can improve the multi-temporal remote sensing image fusion quality.

The invention discloses an accurate positioning system based on multi-modal image fusion, and the system comprises a multi-modal image collection module which synchronously collects an MRI structure image, an fMRI function image and a DTI white matter fiber bundle image of the brain of a patient; the image preprocessing module is connected with the acquisition module and is used for de-noising, registering and standardizing the multi-modal image data to generate fused image data under a unified coordinate system; the target analysis module is connected with the preprocessing module and calculates initial coordinates of the target based on the fused data; the dynamic correction module is connected with the analysis module, monitors the head movement in real time and dynamically corrects the target position; and the target spot guiding terminal is connected with the correction module and controls the mechanical arm to accurately position according to the corrected coordinates. According to the system, the transcranial magnetic stimulation target spot positioning accuracy and the personalized adaptive capacity can be remarkably improved, the problems that a traditional method depends on experience and the single-mode image accuracy is insufficient are solved, and meanwhile the positioning stability and the effect evaluability in treatment are ensured.

The invention relates to the technical field of image fusion, in particular to a territorial space planning region boundary determination method and system based on a remote sensing image, and the method comprises the steps: obtaining a target multispectral image and a target panchromatic image which are spatially aligned under a target region; performing multi-scale and multi-direction transformation on the target panchromatic image and the target multi-spectral image to obtain a low-frequency sub-band and a plurality of high-frequency sub-bands corresponding to each image; fusing each pair of high-frequency sub-bands based on the gradient difference and the boundary significance difference between each pair of high-frequency sub-bands to obtain a plurality of fused high-frequency sub-bands; performing multi-scale and multi-direction inverse transformation based on the plurality of fused high-frequency sub-bands and the low-frequency sub-bands of the target multispectral image to obtain a target fused image; and determining a region boundary based on the target fusion image. According to the method, a target fusion image with high resolution and clearer boundary can be obtained, and a more accurate and reliable region boundary can be obtained.

The invention provides a flight area track project progress evaluation method, which can be applied to the technical field of artificial intelligence and the technical field of Internet of Things. The method comprises the following steps: acquiring unmanned aerial vehicle shooting data and satellite remote sensing data of a flight area channel; performing image fusion processing on the unmanned aerial vehicle shooting data and the satellite remote sensing data to obtain fused image data; analyzing the fused image data by using a pre-trained target detection model to obtain a detection result; based on the detection result, extracting image feature data reflecting the actual completion project amount of the flight area track project, and obtaining actual completion project amount data; and comparing the actual completion project amount data with a preset construction plan progress, and performing project progress evaluation based on a comparison result.

This invention proposes an image processing method and a display device using this method. The method includes: converting an original image to a first color space to generate a reference image corresponding to the first color space; detecting image regions with high-frequency signals in the reference image to generate a detection result; performing an operation on the detection result according to a preset gain value to generate a first operation result; evaluating the detection result and the first operation result, and selecting a reference gain value; performing an operation on the detection result according to the reference gain value to generate a second operation result; selecting at least one of the brightness values ​​of a first pixel in a first fused image and a second pixel in a second fused image according to the first operation result and the second operation result to generate the brightness values ​​of pixels in a third fused image; and converting the third fused image to a second color space to generate an output image corresponding to the second color space. The display device of this invention makes the displayed image clearer and suppresses the white-edge effect.

This invention belongs to the field of remote sensing image processing technology and discloses a method and system for spatiotemporal fusion of remote sensing images. This method introduces Mamba modules in both the spatial and frequency domains to enhance the extraction and modeling capabilities of multi-scale features and structural information. In the spatial domain, the Mamba module is used to capture the local structure and geometric details of the image; in the frequency domain, the Mamba module is introduced for feature extraction of the high-frequency subbands after wavelet transform, thereby more fully exploring the contextual dependencies and structural features of high-frequency information. Through channel attention and spatial attention mechanisms, dual-domain interactive fusion is achieved, improving the spatial clarity and spectral consistency of the fused image. It possesses strong generalization and adaptability, fully utilizing information from both the spatial and frequency domains, and improving the accuracy and efficiency of acquiring remote sensing images.

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.