2036 results about "Image registration" patented technology

A method for aligning a sample to an array is provided. An image of sample image of a sample can be received by a data processor. The sample image having a first resolution. An array image including an overlay of an array with the sample and an array fiducial can be received by the data processor. The array image having a second resolution lower than the first resolution of the sample image. The sample image can be regisered to the array image by aligning the sample image and the array image. An aligned image can be generated based on the registering. The aligned image can can include an overlay of the sample image with the array. The aligned image can be provided by the data processor. A method for detecing fiducials associated with an array is provided. Systems and non-transitory computer readable mediums performing the method are also provided.

The invention provides a remote sensing strip mine area detection method based on a double-branch structure and a feature fusion mechanism, and the method comprises the steps: obtaining multi-source high-resolution remote sensing image data of a strip mine mining area, carrying out the preprocessing of atmospheric correction, radiation calibration, color correction, image registration, cutting operation and the like, and obtaining time sequence remote sensing image data; a deep learning algorithm is adopted to construct a strip mine area detection model based on a double-branch structure and a feature fusion mechanism, training is carried out through the time sequence remote sensing image data, a remote sensing image detection model is obtained, the double-branch structure comprises a feature extraction branch and a feature generation branch, and the feature extraction branch comprises a feature extraction branch and a feature fusion branch; the feature fusion mechanism comprises a cross attention fusion module and a feature adaptive fusion module; inputting to-be-detected remote sensing image data into the remote sensing image detection model to obtain a detection result of the strip mine mining area. According to the method, the recognition precision of small target details and mining area boundaries of low-resolution images is improved, and the calculation efficiency and the detection precision are both considered.

The invention discloses a mild traumatic brain injury image evaluation system based on deep learning, and relates to the technical field of medical imaging, and the system comprises a data source module which is used for obtaining head magnetic resonance imaging data as an input data source; the preprocessing module is used for carrying out de-noising processing and image registration on the skull magnetic resonance imaging data to realize standardized preprocessing; and the feature extraction module is used for extracting a local feature map from the multi-dimensional image data by constructing a multilayer convolutional neural network, and identifying a subcortical structure change and damage suspicious region. According to the mild traumatic brain injury image evaluation system based on deep learning provided by the invention, a standardized evaluation report automatically generated by the system not only integrates clinical information of a patient, but also provides detailed injury types, severity and follow-up visit suggestions, so that the efficiency and reliability of clinical decision making are greatly improved; and powerful support is provided for formulating an individualized diagnosis and treatment scheme.

The invention provides a large-scale satellite three-dimensional image data processing method, medium and system, and belongs to the technical field of satellite three-dimensional image data processing.The method comprises the steps that Gaussian filtering and wavelet noise reduction preprocessing is conducted on an image, and then cooperative computing of a GPU and a CPU is achieved based on a computing task distribution function. Through multi-scale feature extraction and matrix optimization, a stable feature matrix and a variable feature matrix are obtained. A quadtree spatial index is adopted to carry out data partitioning, and SIFT feature extraction and K-means clustering are combined to optimize feature representation. And establishing a stereoscopic image registration model, determining an optimal splicing sequence through a minimum spanning tree algorithm, realizing image splicing by adopting a progressive texture fusion method, and finally generating a high-precision three-dimensional earth surface model. According to the invention, the unification of processing efficiency and precision is realized, and the technical problem that the processing efficiency of large-scale stereoscopic image data is difficult to improve on the premise of ensuring the processing precision in the prior art is solved.

The invention provides an intelligent cleaning decision-making method and system for a photovoltaic power station based on machine learning, and relates to the technical field of photovoltaic power stations, and the method comprises the steps: collecting images through a visible light camera and an infrared thermal imaging camera carried by an unmanned plane, segmenting a pollution region based on color, texture and temperature features through a deep learning model after image registration and preprocessing, and obtaining a cleaning decision-making result; and calculating a pollution area proportion and establishing an evaluation index in combination with the temperature difference value so as to analyze the power generation efficiency loss and generate a pollution distribution map and an evaluation report. The pollution area can be accurately identified, the influence of the pollution degree on the power generation efficiency is quantitatively evaluated, and a decision basis is provided for power station cleaning maintenance.

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 relates to the technical field of multi-modal medical image registration optimization methods, in particular to a medical image registration optimization method and system, and the method comprises the following steps: inputting a medical image data set comprising an MRI image data set, a CT image data set and a PET image data set into a trained deformable registration network; obtaining a deformation field by using the deformable registration network; obtaining a final registration result of the two groups of medical image data based on the deformation field, obtaining an optimized final registration result through an iterative optimization algorithm, and obtaining a quality evaluation coefficient at the same time; constructing an optimized objective function by using the quality evaluation coefficient, and realizing the adjustment of the hyper-parameter of the deformable registration network through the back propagation of the optimized objective function; prediction registration is carried out by using the deformable registration network after hyper-parameter adjustment, so that an optimized registration result is obtained, the position and the shape of a convolution kernel are adaptively adjusted based on a feature extraction network of deformable convolution, and nonlinear deformation of a local anatomical structure is better captured.

The invention discloses an image registration and segmentation joint optimization method, system, device and medium, and relates to the technical field of image registration and segmentation, and the method comprises the steps: obtaining an original medical image, and employing a dynamic pairing strategy to obtain a floating image and a fixed image; the method comprises the following steps: constructing an image registration and segmentation joint optimization model, wherein the model comprises an encoder sharing weight, a segmentation decoder, a registration decoder and an uncertainty estimator; and inputting the floating image and the fixed image into an image registration and segmentation joint optimization model to obtain a registered image and a tag thereof, inputting the fixed image and the registered image into an uncertainty estimator to predict the uncertainty of registration, and minimizing a loss function to obtain an updated image registration and segmentation joint optimization model. According to the method, the problem of class imbalance of different anatomical structure labels can be relieved, and the forward action of the layered segmentation feature map in the deformation subfield generation process can be promoted.

The invention discloses a visible light-infrared dual-mode image registration method based on deep learning. The method comprises the following steps: acquiring a visible light image and an infrared image in the same scene; preprocessing the image; performing feature extraction on the preprocessed visible light image and the preprocessed infrared image, and performing feature enhancement on a feature extraction result by using a double attention mechanism; performing multi-scale feature fusion on the enhanced visible light image features and the enhanced infrared image features to generate cross-modal fusion features; inputting the cross-modal fusion features into a deformation field estimation network to generate an estimation result of a deformation field; and applying the estimation result of the deformation field to the infrared image to register the corresponding visible light image. According to the method, the problem of cross-modal registration between the visible light image and the infrared thermal imaging of the live pig body temperature non-contact prediction scene is effectively solved, high-precision pixel-level alignment is realized, and a technical basis is provided for non-contact pig body temperature monitoring.

The invention discloses an on-bridge pedestrian abnormal behavior identification and early warning method based on intelligent monitoring, and relates to the technical field of bridge engineering, and the method comprises the steps: carrying out the registration of a current visible light image and a current infrared image, and obtaining a registration dual-mode image based on a current image coordinate system; considering current bridge deformation to establish a current coordinate mapping conversion model for mapping pixel coordinate points in the current image coordinate system to physical coordinate points of a current bridge physical space coordinate system; obtaining the current three-dimensional actual coordinates of the pedestrian; recognizing abnormal behaviors of the pedestrians on the bridge based on the current three-dimensional actual coordinates of the pedestrians and acquiring current dynamic risk score values of the pedestrians; and determining the current risk level of the pedestrian according to the current dynamic risk score value of the pedestrian and a preset risk threshold value to realize multi-level linkage protection response. The method provided by the invention solves the technical problem in the prior art that the current actual coordinate of the pedestrian is difficult to accurately obtain due to environmental interference, and the abnormal recognition of the pedestrian on the bridge is inaccurate.

The invention relates to the technical field of geological exploration and three-dimensional modeling, in particular to a mining unmanned aerial vehicle high-precision three-dimensional geological modeling system which comprises a multispectral imaging acquisition module, a point cloud-image high-precision registration module, a geological feature fusion extraction module, a three-dimensional geological model construction module and a geological attribute visualization output module. Wherein the multispectral imaging acquisition module is used for acquiring registration image data and original point cloud data; the point cloud-image high-precision registration module is used for carrying out consistency matching and outputting a registration point cloud-image data set; the geological feature fusion extraction module is used for identifying geological boundaries and outputting geological feature vector data; and the three-dimensional geologic model construction module is used for generating a three-dimensional grid model with attribute topology. According to the method, through accurate three-dimensional modeling and lithology visualization, geological features and lithology information are efficiently combined, and reliable data support is provided for rock mass stability analysis.

Systems, methods, and computer-readable media for aerial localization in the absence of satellite (e.g., global navigation satellite system (GNSS)) signals while flying over heterogeneous environments. The present embodiments provide a training strategy for robust cross-seasonal image matching in complex, real-world environments. The challenge put forward is to successfully localize a UAV flying over both densely and sparsely populated areas by means of image registration. To this end, the focus of the present embodiments can be on devising loss components to a visual recognition network that adequately embeds the diverse feature types on the ground. A normalized error metric can be provided as well in order to render a conceptually different method comparable to the present approach. The present technical contributions can provide robust visual recognition algorithms in challenging natural environments.

The invention relates to the technical field of image processing, and discloses a virtual operation analogue simulation method and system based on a mixed reality technology. The method comprises the steps that three-dimensional space information is collected, feature extraction and registration are conducted on a real operation environment and video data, and mixed reality mapping and monitoring data are generated; performing medical image registration processing based on mixed reality mapping to obtain virtual scene data; processing instrument sensing data according to the mapping data and the monitoring parameters to obtain interaction data; performing surgical process simulation according to the virtual scene and the interaction data; performing operation trajectory analysis and identification based on the simulation data, and generating evaluation and early warning information; and analyzing the evaluation and early warning information, and outputting an analysis report. Through multi-source data fusion and a real-time evaluation feedback mechanism, the authenticity and accuracy of operation simulation training are effectively improved.

The invention relates to the technical field of path planning, in particular to a path planning method and system for an inspection robot, and provides the following scheme: executing an inspection task through an initial electronic map, collecting first image data and pose information, recognizing an inspection target, completing image registration, and generating second image data; inverting and calibrating the optimal observation direction based on the structural sensitivity distribution of the local visual features and the illumination variation trend, and determining the shooting position and angle to form a path node; a multi-objective optimization model is adopted, the path length, the view angle continuity and the illumination interference are comprehensively considered, and an optimal inspection path is generated; the method is suitable for outdoor inspection scenes with complex structures and non-uniform target distribution.

The invention relates to the technical field of industrial detection, and discloses a multi-channel self-adaptive glass component defect detection method which sequentially comprises the following steps: S1, multi-channel image acquisition; performing rapid switching and imaging of three illumination modes by adopting a time-sharing stroboscopic technology to obtain a multi-channel image of the glass component; s2, multi-channel image registration is carried out; s2, registering the multi-channel images obtained in the step S1 so as to align the features of the multi-channel images, and obtaining registered multi-channel images; s3, multi-channel feature extraction and fusion; performing single-channel feature extraction on the registered multi-channel image, and combining a channel attention mechanism and a space attention mechanism to obtain a multi-channel image fusion feature map; s4, segmenting a defect region; and S5, a post-processing scoring mechanism is used for preventing over-killing. Through a multi-channel image acquisition technology, multi-scale feature extraction in deep learning, a self-adaptive attention mechanism and a post-processing scoring mechanism are combined to prevent over-killing, and more efficient and more accurate plane glass component defect detection is carried out.

The invention discloses a multi-modal thermal imager intelligent monitoring method based on image processing. The method comprises the following steps: S1, performing dual-band synchronous imaging; s2, carrying out cross-modal image registration; s3, multispectral image fusion: performing smoothing processing on the infrared and visible light images through anisotropic diffusion to obtain a main structure layer image, calculating to obtain an infrared and visible light detail layer image, and linearly combining the detail layer image through PCA principal component weight, and finally, combining the detail layer image and the main structure layer image according to the self-defined weight to obtain a final fusion image. And S4, gas leakage detection is carried out, so that gas leakage detection and early warning can be realized. According to the invention, a gas enhancement algorithm is adopted to realize real-time detection of leaked gas, interference of most background noise can be eliminated, and the real-time performance and reliability of the system are improved.

The invention discloses an unsupervised deep learning image splicing method for portable fundus color photo equipment. The method comprises the following steps: constructing a multi-scale feature extraction module of an image registration network; the feature matching module is used for constructing an image registration network; the multi-scale feature extraction module and the feature matching module are sequentially connected to form the constructed image registration network; constructing a shallow feature extractor; constructing a feature reconstruction module; constructing a joint generator; sequentially connecting the shallow feature extractor, the feature reconstruction module and the seam generator to form a fusion network; constructing a fusion network; connecting the image registration network and the image fusion network to form an image splicing model; collecting data; preprocessing the data; obtaining a trained image registration network; obtaining a trained image fusion network; forming a fundus image splicing model by using the trained image registration network and the trained image fusion network; and splicing the images by using the eye fundus image splicing model. According to the method, a high-quality fundus spliced image can be efficiently generated.

The present invention belongs to the field of medical image registration, and more specifically, relates to a building method of a multi-modal three-dimensional medical image segmentation and registration model, and an application thereof. The method includes: acquiring medical images of two modalities of each target, and respectively using the images as a reference image and a floating image to acquire a training sample; and using the training sample to simultaneously optimize three network parameters, so as to acquire a segmentation and registration model formed by a reference image segmentation model, a floating image segmentation model, and a registration model. The reference image and floating image segmentation models are respectively used to perform multi-scale segmentation on corresponding images to acquire multi-scale segmentation results having the same maximum scale as the original images. The registration model are used to acquire a multi-scale deformation field on the basis of the reference image, the floating image, a maximum scale reference image segmentation result, and a maximum scale floating image segmentation result. Each of a segmentation loss and a registration loss are the sum of segmentation and registration losses in each scale, and the segmentation loss includes a first-order gradient loss and / or a level set energy function loss. The present invention can improve registration accuracy.

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

The invention discloses a two-stage infrared and visible light image registration method, system and device. The method comprises the following steps of image preprocessing, contour extraction, angular point detection, feature description and matching, affine transformation estimation, multi-scale optical flow estimation, optical flow constraint and loss function, reverse resampling and fusion and error evaluation. According to the invention, rough registration is carried out by using contour angular point features, so that a preliminary alignment result can be quickly obtained; refined alignment is carried out in combination with an unsupervised optical flow network, and sub-pixel-level registration precision is achieved. The contour angular points are based on shape information of an image target, are natural and are not influenced by spectral differences, and the matching stability is enhanced through main direction and angle features. The unsupervised depth optical flow model estimates a pixel displacement field by learning consistency characteristics of an input image, and does not need to depend on annotation data. The combination can effectively eliminate the difference between infrared light and visible light, and improves the robustness and adaptability of registration.

The invention discloses a small target recognition method and system for a multi-modal fusion image in a complex environment, and belongs to the technical field of computer vision and image recognition, and the method comprises the steps: obtaining a visible light image, an infrared image and environment sensor data; image registration is carried out on visible light and infrared images, and a multi-scale image feature pyramid is constructed. And respectively extracting visible light and infrared image features to obtain visible light and infrared imaging feature data. And performing multi-modal data fusion on the visible light and infrared imaging feature data based on a cross-modal attention mechanism, and adaptively adjusting a fusion weight based on environmental sensor data to generate fusion features. And performing space-time enhancement processing on the fusion feature to obtain an enhanced fusion feature. And performing target tracking detection on the small target, and outputting position and category information of the small target. According to the method, the small target recognition capability in a severe environment is remarkably improved, and high precision and robustness can still be kept in a foggy, low-visibility and dark scene.

The invention belongs to the field of geophysical monitoring, and discloses an earth crust deformation monitoring method and system based on satellite radar interference measurement, electronic equipment and a storage medium, and the method comprises the steps: obtaining a measurement image of a satellite radar for the earth surface, and carrying out the image registration of the measurement image, and obtaining a registered image; pre-processing the registered image to obtain a pre-processed image; performing interference processing on the preprocessed image to generate a differential interferogram containing a terrain phase and a deformation phase; phase unwrapping and terrain phase correction are carried out on the differential interferogram, a deformation phase is separated out, and earth surface deformation information is obtained; and performing time sequence analysis on the earth surface deformation information of the continuous time to obtain a change trend of earth crust deformation. According to the invention, based on satellite radar interference measurement, a large area can be covered in a short time, and effective monitoring can be carried out on wide plains, complex mountainous regions and remote regions.

The invention relates to the technical field of image processing, in particular to a tongue image feature analysis method and system. The method comprises the following steps: acquiring lingual surface multi-modal image data including a hyperspectral image, a visible light image and a dynamic tongue image; performing high-frequency component amplification processing on the visible light image and the dynamic tongue image; respectively extracting tongue regions from the hyperspectral image, the processed visible light image and the dynamic tongue image, and carrying out image registration on the extracted tongue regions; performing feature extraction on the registered tongue region in the multi-modal image data of the lingual surface by using a deep neural network to obtain a tongue spectral feature, a static tongue feature and a lingual fibrillation feature, and performing fusion to obtain fused tongue image feature data; and the deep neural network is adopted to analyze the fused tongue image feature data, and the traditional Chinese medicine analysis result of the current tongue image is predicted, so that the dependence on a complex network structure is reduced, and the fusion effect of the tongue features and the accuracy of the analysis result are improved.

The invention discloses an infrared and visible light image registration method based on an improved SIFT algorithm, and the method comprises the steps: constructing a Gaussian difference pyramid of infrared and visible light images, so as to effectively capture the multi-scale features in the images; performing local adaptive FAST key point extraction on each layer of the pyramid, and detecting significant feature points of the image on a multi-scale level; based on gradient direction distribution of images in key point neighborhoods, main direction distribution is carried out on key points, so that the key points can still keep consistent under different rotation angles; coding the key points based on the gradient information features and generating corresponding feature descriptors to be applied to subsequent registration operation; and performing initial registration by using the similarity between the descriptors, verifying and optimizing an initial registration result by using an optimized RANSAC algorithm, and eliminating mismatching points. According to the method, the number of infrared and visible light image feature point detection and matching is increased, the time complexity is reduced, and the matching precision is ensured.

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

The invention discloses an unaligned infrared and visible light image fusion method based on modal transformation and an attention mechanism, and belongs to the field of computer vision. The invention provides an image registration network model based on modal transformation for the problems of motion deformation and geometric dislocation in infrared and visible light image registration. By introducing a cross-modal style migration network, a visible light image is converted into a pseudo-infrared image, and a cross-modal registration problem is converted into a single-modal registration task, so that the influence of modal difference on registration precision is reduced. And then, a multi-scale refined registration strategy is adopted, a deformation field is gradually optimized through multi-level feature extraction and a deformation field estimation network, and the registration precision and robustness are improved. The invention further provides a lightweight image fusion network based on multi-scale feature representation and attention guidance, and the network extracts image deep features through different scales and comprehensively captures global and local information.

The invention discloses a real-time image registration and path planning system in bronchoscope surgical navigation, relates to the technical field of image registration, and is used for solving the problems that in a thoracic and abdominal minimally invasive interventional operation, organ movement and deformation caused by breathing of a patient can cause obvious influence on the registration precision of a navigation system, and the navigation precision is poor. Space registration of preoperative images and an anatomical structure of a patient in an operation often has errors due to mismatching of breathing phase changes; multi-attitude data are fused, the respiration interference degree is comprehensively evaluated through double indexes, the problem that a traditional static deformation field cannot be matched with dynamic respiration is solved through respiratory cycle driving deformation prediction, displacement errors are corrected through tidal volume observation values, and B spline control point weights are reversely optimized through registration residual errors. A deformation field can adapt to breathing amplitude and frequency differences of different patients, online distillation learning is carried out by taking a registration error output by a filter as a teacher signal, and local residual deformation and chronic changes which are difficult to describe by a traditional global model are accurately captured.

The invention discloses a lingual surface diagnostic information acquisition, diagnosis and analysis method and system, and belongs to the technical field of medical auxiliary diagnos.The method comprises the steps that an acquired lingual surface image is matched with patient information, a symptom associated lingual surface area is determined, the associated lingual surface area is subjected to priority division, and whether the acquired image meets a clear standard or not is judged; the method comprises the steps of constructing an image index evaluation model based on tongue vibration and image texture, performing tongue vibration, texture stability and water vapor fuzzy interference evaluation on an image needing to be processed, constructing a stable frame evaluation model, and importing vibration intensity, texture stability and a water vapor proportion into the stable frame evaluation model to evaluate image area stability. Image registration is carried out on the stable frame set, multi-frame registration and fusion are carried out based on an image stable region, region stability and processing information are recorded, a high-quality image for tongue picture analysis is generated, the image definition of a key diagnosis region is improved, and the accuracy and stability of tongue picture analysis are improved.

The invention provides an infrared and visible light image end-to-end registration method based on phase consistency enhancement, and belongs to the technical field of electric digital data processing. The invention discloses an infrared and visible light image end-to-end registration method based on phase consistency enhancement, which comprises the following steps of: firstly, preprocessing infrared and visible light source images in an image database, and dividing the infrared and visible light source images into a training set and a test set; and constructing a feature extractor based on a phase consistency attention mask, realizing cross-modal consistency feature extraction, and focusing an important space region by using phase information. A self-defined ResSobeNet network is adopted for parameter estimation, and Haar wavelet transform is used for replacing pooling operation, so that information loss in a traditional down-sampling method is avoided. A double-branch generator structure is designed, affine parameters and an optical flow field are generated respectively, meanwhile, constraint on rigid registration and non-rigid registration is achieved, and finally a registration result is obtained through resampling of a space conversion module. According to the method, the problems that cross-modal consistent features are difficult to extract in the infrared and visible light image registration process and explicit registration steps are complicated and difficult to generalize are solved, and the method has good universality and practicability.

The invention discloses an image registration fusion method and device, equipment and a storage medium. When the method provided by the embodiment of the invention is executed, firstly, a visible light image and an initial infrared image of a target object can be acquired; and inputting the visible light image and the initial infrared image of the target object into the image registration fusion model for registration fusion to obtain a fused image. Wherein the image registration fusion model is used for carrying out style migration on the visible light image of the target object and carrying out deformation estimation and deformation on the initial infrared image of the target object. And the image registration fusion model is also used for carrying out image fusion on the visible light image after style migration and the initial infrared image after deformation to obtain a fused image. And finally, outputting the fused image. The image registration and fusion precision is improved, the fusion effect is optimized, and the final image quality is improved.