91 results about "Motion modeling" patented technology

The invention discloses a multi-target tracking method combining camera motion compensation and pseudo depth estimation, belongs to the field of computer vision, and is suitable for a complex automatic driving road environment. The method comprises the following steps: constructing a training set and a test set; detecting the image by using a deep learning detector and extracting features; a Kalman filter is adopted to correct a motion modeling state vector, and the target position and size prediction precision is improved; solving a homography matrix through feature point matching, performing global camera motion compensation, and reducing camera jitter and displacement interference; target pseudo depth information is calculated, hierarchical cascade matching is carried out, and association performance in dense and shielding scenes is optimized; a three-level cascade strategy is adopted to complete high confidence degree, low confidence degree and residual target matching in sequence; and finally, outputting a tracking result with a detection frame and identity information to obtain a trained model. According to the invention, accurate detection and stable tracking of multi-category targets can be realized in a complex environment, and identity switching is effectively reduced.

The invention provides a zebra fish multi-target tracking method and system based on a space science experiment video. The method comprises the following steps: firstly, obtaining a zebra fish space science experiment video; then, motion information is extracted based on the zebra fish space science experiment video, and the motion information is used for representing a motion state represented by pixel points in the zebra fish space science experiment video; then, multi-modal feature fusion is carried out based on the motion information and the appearance information, and multi-modal fusion features are determined; and finally, target detection and tracking are carried out according to the multi-modal fusion features, and target movement track information corresponding to the target zebra fishes is determined. According to the method, motion feature information representing dynamic changes is extracted through motion modeling based on a self-adaptive threshold value, then the interaction and fusion relation between appearance and motion features is modeled step by step through a heterogeneous graph, and the robustness of target detection, data association and trajectory prediction is enhanced. Therefore, the stability and the accuracy of multi-target tracking of the zebra fish can be effectively improved.

The invention discloses a reference video object segmentation method and system based on motion modeling and multi-modal interaction, and the method comprises the steps: taking a video sequence and natural language description as input, and generating a preliminary segmentation mask through a text coding and mask decoder; a Kalman filtering motion modeling module is introduced to predict the motion trail of the target object, and time sequence consistency optimization is carried out on the preliminary segmentation mask; fusing the historical track of the object and the action semantics in the semantic features on the basis of a key action semantic coding module to realize action semantic alignment and mask dynamic correction; the segmentation quality of the current frame is subjected to multi-dimensional scoring based on a representative frame screening mechanism, the representative frame is screened out to update a memory bank, and the long-term tracking stability is improved. According to the method, the problems of target drift, insufficient semantic alignment and memory pollution in a complex dynamic scene in the prior art are effectively solved, and the segmentation precision, robustness and semantic consistency are remarkably improved while the light weight of the model is kept.

The invention discloses a scene three-dimensional reconstruction and vector information extraction method and device based on vehicle return data, equipment and a storage medium, and the method comprises the steps: carrying out the preprocessing of a time sequence image returned by a vehicle and corresponding pose data, and obtaining a semantic mask and a camera pose of each frame of image in the time sequence image; based on the semantic mask and the camera pose, performing three-dimensional geometric reconstruction on the static scene area in the time sequence image to obtain a static three-dimensional scene model; a dynamic target is separated from the time sequence image according to the semantic mask, three-dimensional motion modeling and independent three-dimensional reconstruction are carried out on the dynamic target, and a dynamic target three-dimensional model is generated; and generating a dynamic three-dimensional scene model and vector labeling information based on the static three-dimensional scene model and the dynamic target three-dimensional model. According to the method, the limitation of automatic driving shadow mode data is overcome, the three-dimensional reconstruction of the scene and the automatic generation of the true value information are realized, the data acquisition cost is further reduced, and the efficiency of automatic driving research and development is improved.

The invention relates to an underwater single target tracking method based on a wavelet token and a space-time Transform. The method comprises the following steps: firstly, constructing a reference frame sequence, a search frame and a previous frame historical token into a space-time input sequence, and extracting cross-frame features through a Transform encoder; then, Haar wavelet decomposition is carried out on the historical token, and a low-frequency component representing a target structure and a high-frequency component capturing motion details are separated out; then, adaptively fusing the global features and the historical components of the current search frame by using a gating mechanism, and generating a wavelet token; and finally, inputting the wavelet token and the global feature into a prediction head, and outputting a target classification confidence map and a bounding box regression map to determine the position and the scale of the target. According to the technical scheme of the invention, the interference of underwater low-illumination noise can be effectively suppressed through the wavelet token, and the space-time continuity of target motion modeling is maintained in combination with a gating strategy, so that the tracking robustness of an underwater complex scene is effectively improved.

The invention discloses a field personnel behavior identification method based on human skeleton key points. The method is suitable for behavior monitoring of operators in complex production environments such as hydraulic power plants. The method comprises the following steps: performing feature extraction and modeling on skeleton data acquired by camera equipment, and performing multi-scale space-time modeling by adopting a time sequence guide space topology modeling module and a space-time motion modeling network; according to the method, the capturing capability of the dynamic relation between human skeleton nodes is enhanced, the behavior recognition precision of long time span and complex postures is improved, and the robustness and generalization capability of the model in a complex environment are improved; on the whole, the risk behavior of the operating personnel can be effectively identified, the production safety is guaranteed, and the risk caused by human errors is reduced.

The invention relates to the technical field of computer vision, in particular to a multi-target tracking method combining a time convolution network and a self-attention mechanism, which comprises a motion model based on a TCN, dynamic ReID feature updating based on track confidence, and a rematching strategy used for correcting wrong association caused by target interaction shielding. A TCN network is combined with an attention mechanism to be used for motion modeling, and nonlinear motion prediction of a track is achieved. In addition, a track confidence coefficient is provided to measure the robustness of the track, and the ReID features are dynamically updated based on the track confidence coefficient. Meanwhile, in order to make up for wrong association caused by target interaction shielding, a rematching module is designed, and the effectiveness of the rematching module is proved through experiments. In addition, each module of the system has a modular characteristic and a relatively strong generalization ability, so that the system is suitable for a wider real scene, and future research work is possibly stimulated.

The invention discloses a multi-sensor cooperative robot target positioning system, and relates to the technical field of robot positioning, in particular to the multi-sensor cooperative robot target positioning system. The objective of the invention is to solve the problems of reduced positioning precision and insufficient robustness caused by sensor data failure, insufficient cooperation mechanism and motion interference in an extreme environment. The system comprises a multi-source sensing module, a collaborative fusion module, a motion modeling module and a closed-loop control module, a robot pose adjustment instruction is generated through multi-sensor data acquisition, space-time alignment and confidence weighted fusion, dynamic motion modeling and error compensation, and closed-loop collaboration of positioning and motion is achieved. According to the invention, the positioning precision, robustness and continuous operation capability of the system in an extreme environment are effectively improved.

The invention relates to the technical field of ocean engineering operation motion modeling and control, and particularly discloses a method and a system for calculating influence of dynamic response of a separation supporting device and a buffer coupling device on a ship pose in a floating support installation process. The method comprises the following steps: acquiring initial position information of a separation support device and a buffer coupling device at a previous moment; acquiring vertical position and attitude information of the dynamic positioning ship and the upper module; calculating the supporting force of the separation supporting device and the buffer coupling device at the current moment; calculating the vertical position and attitude information of the upper module and the dynamic positioning ship at the current moment based on the supporting force and the external ballast water load; outputting relevant parameters and judging whether the installation is completed or not. According to the method, a multi-degree-of-freedom coupling dynamic model is constructed, high-precision real-time simulation of device dynamic response and ship motion attitude in the continuous unsteady-state load transfer process is realized, key parameter monitoring and risk early warning are provided for floating mounting operation, and the operation safety and efficiency are improved.

The invention discloses a three-dimensional target tracking method, system and device based on space-time enhancement and a medium, and relates to the technical field of automatic driving. The method comprises the following steps: inputting three-dimensional point cloud sequence data into a trained SMTrack network for processing to obtain a final bounding box prediction result. Wherein the SMTrack network comprises a target specific encoder, an STFM module and an STT module which are connected in sequence; wherein the target specific encoder is used for extracting target specific features from a point cloud sequence; the STFM module is used for modeling appearance information and motion information in stages according to the specific features of the target, and generating a preliminary bounding box prediction result; the STT module is used for optimizing the preliminary bounding box prediction result; according to the method, a twin structure is adopted, features are extracted from a historical frame sequence and a current frame sequence, motion modeling of part sensing and a coarse-to-fine bounding box regression mechanism are introduced, and the target positioning precision can be greatly improved.

The present disclosure provides systems, apparatuses and methods that provide probe-cavity location and motion data based on probe location and respiration data.

The invention provides an intelligent ship digital twinning motion modeling and parallel anti-interference path tracking controller. The intelligent ship digital twinning motion modeling and parallel anti-interference path tracking controller comprises a guidance signal module, a speed optimization module, a parallel path tracking controller module, a data-driven learning predictor module and an actual controller module. The data-driven learning predictor module receives position information, course angle information and speed information from the actual intelligent ship system module, and the data-driven learning predictor module receives parallel path tracking control force and torque from the parallel path tracking controller module; the data driving learning predictor module sends estimation information of the position and course angle of a virtual intelligent ship and estimation information of the speed of the virtual intelligent ship to the guidance signal module and the parallel path tracking controller module. According to the method, the self-adaptive capacity of the ship under the complex sea condition is enhanced, the collision risk is reduced fundamentally, and an effective solution is provided for the navigation scene with the high safety requirement.

The application discloses a reference video object segmentation method and system based on motion modeling and multi-modal interaction, which comprises the following steps: taking a video sequence and a natural language description as input, generating a preliminary segmentation mask through a text encoding and mask decoding module; introducing a Kalman filter motion modeling module to predict the motion trajectory of the target object and optimize the time sequence consistency of the preliminary segmentation mask; based on a key action semantic coding module, fusing the action semantics in the historical trajectory and semantic features of the object, realizing action semantic alignment and dynamic mask correction; based on a representative frame screening mechanism, multi-dimensional scoring is performed on the segmentation quality of the current frame, and representative frames are screened out to update the memory bank, thereby improving the long-term tracking stability. The application effectively solves the problems of target drift, insufficient semantic alignment and memory pollution in the prior art in a complex dynamic scene, while keeping the model lightweight, significantly improves the segmentation accuracy, robustness and semantic consistency.

According to the multi-sensor fusion evaluation algorithm and device based on the continuous time sequence, modeling is carried out based on continuous time, physical reality is better met, motion modeling with better precision is achieved, intra-frame motion can be accurately described, smoother and more accurate track estimation can be provided, and the method and the device are suitable for being applied to multi-sensor fusion evaluation. Especially, the advantages are obvious in high-speed and high-frequency vibration scenes; meanwhile, according to the technical scheme disclosed by the invention, when LiDAR / Camera matching fails, the constraints of the IMU and the GNSS are seamlessly and continuously applied to the whole track through a continuous time model instead of only acting on a discrete frame, so that error accumulation is greatly inhibited, system collapse is avoided, the robustness of a degraded scene is enhanced, application in specific fields such as surveying and mapping is greatly facilitated, and the method is suitable for popularization and application. And the method has the capability of flexibly adding constraints.

The invention discloses a human body surface dynamic reconstruction method based on a monocular video, and the method comprises the steps: extracting a key frame of human body motion from the monocular video, and obtaining an RGB image of the key frame, a mask, parameters of an SMPL human body template, and internal and external parameters of a camera; extracting a real normal vector diagram corresponding to the key frame, and constructing a basic data set; the basic data set is used for training a multi-stage progressive human body reconstruction framework, the framework comprises human body overall motion modeling serving as a first stage, local surface dynamic deformation modeling serving as a second stage and surface appearance and illumination modeling serving as a third stage, and an optimal reconstruction model is obtained through training; and extracting a new view angle frame of human body motion from the monocular video, obtaining an RGB image of the new view angle frame, parameters of the SMPL human body template and internal and external parameters of the camera, and inputting the RGB image, the parameters of the SMPL human body template and the internal and external parameters of the camera into the optimal reconstruction model to generate a human body dynamic reconstruction result under the conditions of a new view angle, a new posture and new illumination, thereby realizing high-quality rendering output with geometric details and appearance consistency.

The present disclosure provides a monocular image-based unsupervised three-dimensional scene online reconstruction method and device. The method of the present disclosure comprises: obtaining a static background mask and a mask of each dynamic object instance of a current frame monocular image through semantic segmentation, obtaining a surround view synthesis image through a static multi-view generator, determining the motion parameters of each dynamic object instance through motion modeling, obtaining a multi-view depth map based on the surround view synthesis image through a depth estimation network obtained through self-supervised training, obtaining a local depth map of each dynamic object instance through a local depth estimation network obtained through self-supervised training, and obtaining a complete 3D scene representation through the multi-view depth map, the local depth map of each dynamic object instance, and the motion parameters thereof. The present disclosure can avoid true value dependence, effectively reduce hardware cost, and at the same time improve the reliability and robustness of monocular image three-dimensional scene reconstruction.

The present application belongs to the field of elastic motion modeling of air defense missiles, and particularly relates to an elastic motion modeling method for air defense missiles with trajectory control and direct-gas combined control, aiming at solving the problem that the traditional modeling method is insufficient in covering trajectory control force, thus being difficult to realize accurate modeling of the elastic motion of air defense missiles with trajectory control and direct-gas combined control. The present application comprises: calculating the kinetic energy T of the missile system caused by the bending deformation of the elastic missile body, the elastic potential energy U of the missile system, and the damping energy D of the missile system caused by the bending deformation of the elastic missile body; calculating the generalized force Q acting on the missile body corresponding to the virtual work through the virtual work principle i ; based on the kinetic energy T of the missile system, the elastic potential energy U of the missile system, the damping energy D of the missile system, and the generalized force Q i , constructing the dynamic equation of the elastic missile body through the Lagrange equation. The present application corrects and improves the problem of insufficient coverage of trajectory control force in the traditional modeling method, and has high accuracy and precision in the elastic motion modeling of air defense missiles with trajectory control and direct-gas combined control.

This invention belongs to the field of aerodynamics technology for rotating projectiles, specifically relating to a numerical simulation method for the multi-axis angular motion of a rotating projectile. Based on the projectile's external shape model, a spherical flow field mesh is generated, dividing the mesh into an inner and outer domain. Data is transferred via interpolation at the interface. The global computational domain is used to calculate the steady-state flow field. After flow field convergence, the steady-state flow field parameters are used as the initial flow field for multi-axis angular motion simulation. Multi-axis angular motion modeling is performed on the inner domain mesh, obtaining the coordinates of the inner domain mesh nodes at different times. Multi-axis angular motion simulation is then achieved, including unsteady flow field calculations. The flow field simulation is realized through sliding mesh technology and mesh dynamic motion technology. This invention solves the difficult problems of multi-axis angular motion modeling, complex motion mesh implementation, and complex flow simulation, and streamlines the multi-axis angular motion simulation process, providing a reference for engineering applications.

The application provides a coding method and device in AIGC generation. The decoding method in AIGC generation of the application comprises the following steps: obtaining first feature information and motion information of a current frame based on generated feature information output by an AIGC generation model, wherein the motion information is used to represent inter-frame correlation; performing motion modeling according to the first feature information and the motion information of the current frame to obtain second feature information of the current frame, wherein the motion modeling is used to compensate the first feature information of the current frame based on the motion information; and obtaining reconstructed content of the current frame according to the second feature information of the current frame. The application can improve the time domain compression efficiency and the quality of the reconstructed content in AIGC.

This invention relates to the field of traffic simulation and pedestrian flow modeling technology, and particularly to a method and system for crowd movement modeling based on a large language model. The method includes: constructing a multi-dimensional interactive feature system of pedestrians, small groups, and the environment; extracting micro-behavioral features of small pedestrian groups through micro-unit segmentation and dynamic group partitioning methods; proposing a pedestrian movement decision-making framework based on a large language model and thought chain; designing a hybrid update decision-making strategy to balance high-level semantic decision-making and local obstacle avoidance; and training and testing pedestrian evacuation at traffic hubs. Instance verification of pedestrian small group movement decision-making at traffic hubs is also conducted. The proposed model is applied to hub scenarios and its empirical dataset for training and testing. Results show that it achieves better motion realism, traffic efficiency, and group structure stability than baseline models.

The invention discloses an object-oriented periodic dynamic motion 4D Gaussian splash reconstruction method, and relates to the technical field of computer vision and graphics. The method comprises the following steps: identifying and tracking a dynamic object in a foreground, generating a 3D mask and extracting sparse point clouds, performing time sequence alignment to generate a 4D point cloud sequence, identifying periodic motion and extracting global trajectory codes; initializing a standard state 3D Gaussian, constructing a periodic deformation field for a periodic object, generating a dynamic object Gaussian in combination with a global trajectory, coding a non-periodic object only by using the global trajectory, and using the standard state Gaussian for a background; and a reconstruction image is obtained through rendering, reconstruction loss is calculated and back propagation optimization is carried out on normative state 3D Gaussian, a periodic deformation field and global trajectory coding, adaptive density control is executed at an object level in the process, and 4D Gaussian splash reconstruction is completed. According to the method, high-fidelity, editable and efficient-storage 4D dynamic scene reconstruction is realized, and the representation compactness, semantic controllability and motion modeling precision are improved.

The application discloses a method and device for modeling disturbed motion of a submersible based on water dynamic similarity guidance, and belongs to the technical field of motion control of submersibles. The method comprises the following steps: determining a similarity parameter representing the degree of water dynamic essential similarity between a source submersible and a target submersible according to the distribution correlation of a source dimensionless feature set and a target dimensionless feature set in a dimensionless feature space; inputting the target dimensionless feature set into a teacher network with fixed parameters to obtain intermediate layer features and interference parameter representations output by the teacher network; inputting the intermediate layer features, the interference parameter representations and the similarity parameter into a similarity attention mechanism module to generate a knowledge distillation signal; and inputting the target dimensionless feature set into a student network comprising a lightweight adaptive layer to train the student network so as to deploy a disturbed motion prediction model of the target submersible. The method can improve the accuracy of motion prediction of the submersible in a disturbance environment such as a strong current and a turbulent flow.

The application discloses a kind of fruit fly larva tumbling movement modeling and optimization method based on material point method.For the collaborative design challenge of larva tumbling movement model and the problem that traditional method relies on manual measurement data and training time-consuming, a neuro-mechanical model is designed combined with the biological characteristics of fruit fly larvae, and an artificial neural network based on deep learning is constructed as a model controller for training, and a hyperparameter optimization method based on TPE algorithm is designed.The above method is embedded into the differentiable programming framework, realizing the high integration and efficiency optimization of the method.The method can effectively simulate the tumbling movement behavior of fruit fly larvae and optimize it under limited computing resources.The soft body model with the biological characteristics of fruit fly larvae has the advantages of flexible movement and adaptation to complex environment.The application explores the optimal control strategy of fruit fly larvae tumbling process, simulates its interaction with complex environment, and provides an important reference for the design of fruit fly larvae-like soft robot in reality.

The invention provides a 3D multi-target tracking method and system based on monocular vision, and the method comprises the steps: obtaining a continuous video image sequence collected by a monocular camera, and carrying out the preprocessing of the continuous video image sequence, and obtaining an input image sequence; performing target detection and two-stage depth estimation and fusion on the input image sequence to obtain a target detection list with fusion depth information; constructing a multi-dimensional correlation cost matrix, and performing optimal correlation on the current detection and the existing tracking trajectory based on the target detection list and the multi-dimensional correlation cost matrix; and updating the tracking trajectory state according to the association result, and carrying out life cycle management on the tracking trajectory state. According to the monocular camera depth detection technology provided by the invention, low cost and easy integration are ensured, and the depth dimension information is injected for multi-target tracking, so that the defects of a traditional algorithm in shielding processing, similar target distinguishing and three-dimensional motion modeling can be effectively overcome, and the monocular camera depth detection technology is an optimal choice for balancing performance and cost.

The invention relates to the technical field of three-dimensional graphic processing, and discloses a lunar surface / deep space scene target reconstruction and editing method based on 4D Gaussian structuring. The method is particularly suitable for dynamic target modeling and editing in deep space exploration tasks, including detectors, robots and related equipment on the lunar surface, the Mars or other celestial bodies. The method comprises the following steps: synthesizing a plurality of camera visual angles through a diffusion model based on a monocular video of an object, and reconstructing static three-dimensional Gaussian point representation in a standard space; extracting a skeleton structure of the object based on the surface grid, wherein the skeleton structure comprises a skeleton node position and a topological relation; on the basis of the skeleton structure, each Gaussian point is connected with a plurality of skeleton nodes through a linear hybrid skin mechanism, and skeleton-driven rigid deformation is achieved; non-rigid deformation is compensated through feature extraction and a regression network; and in combination with the rigid deformation and the non-rigid deformation, rendering to generate a dynamic Gaussian point model which can change along with time and can be edited. According to the method, motion modeling of an object is explicitly split into rigid motion driven by a framework and non-rigid correction, so that the definition and interpretability of motion representation are greatly improved, and particularly, higher stability and control precision are shown when challenges such as weak texture, violent illumination change and high noise are faced in a deep space environment. According to the method, object behavior modeling in a detection task is more flexible, and the method can be widely applied to scenes such as task planning, target recognition and dynamic editing.

The invention provides a slow action generation method based on motion focus area analysis. The method comprises the following steps: determining a video of a slow action to be generated; detecting a motion focus area in the video, and grading the motion focus area; and for different grades of motion focus areas, different strategies are adopted to carry out motion modeling and differential interpolation processing, and a slow motion frame sequence is generated. According to the invention, through identification and grading processing of the motion focus area, high-precision calculation of the core motion object, simplified calculation of the secondary area and simplest processing of the static background, compared with a traditional scheme of uniform and dense calculation of the whole frame, the method has the advantages of high calculation complexity and lower calculation amount, and guarantees the visual effect of the core area.

This invention proposes a motion deblurring method and system based on Taylor expansion and 3D Gaussian sputtering. The method includes: extracting the dynamic scene from a multi-view motion-blurred video sequence and parameterizing it into an optimizable time-varying 3D Gaussian sputtering representation; constructing a motion model based on Taylor expansion; obtaining an initial velocity field by differentiating and projecting the Taylor expansion motion model, and then correcting it to obtain a corrected 2D pixel-level velocity field; synthesizing a synthetic physically blurred image consistent with the input video frame by simulating the physical imaging process during camera exposure; finally, training and optimization are performed, and after optimization, a final 3D Gaussian scene representation is obtained. This final 3D Gaussian scene representation is then used to render a clear image, thus achieving motion deblurring. This invention improves the physical interpretability and accuracy of motion modeling by explicitly modeling continuous motion trajectories using Taylor expansion.

The invention relates to the technical field of inspection robots, and particularly discloses an inspection robot autonomous navigation system based on deep learning, which comprises an environment sensing module, a dynamic map construction module, a path planning module, an obstacle avoidance control module and an execution feedback module. According to the scheme, a convolutional neural network and an attention mechanism are introduced, deep feature extraction and dynamic target processing are performed on multi-modal data of an inspection environment, and high-precision global dynamic map construction is realized; equipment importance and abnormal position detection are introduced to form a path priority, an important target is preferentially accessed, and the dynamic adaptability of an inspection path is improved according to a global dynamic map and a path risk value updated in real time, so that the inspection robot can efficiently complete a task in a complex dynamic environment; real-time obstacle avoidance and local path re-planning in a dynamic environment are realized, the future collision time is predicted by combining relative motion modeling with a kinematic model, the method has strong local path adaptability, and the stability of robot inspection control is improved.

This invention belongs to the field of industrial robot motion control, specifically relating to a motion control method for a six-axis robotic arm suitable for hemispherical internal spaces. Step 1: Implement a point-to-point online motion algorithm; Step 2: Implement robot motion. The beneficial effects are: 1) Based on the special RRRPRR configuration of the six-axis robotic arm, a dual-mode spatial system motion mathematical model of Cartesian and spherical coordinates is constructed, greatly facilitating the conversion between absolute and relative coordinates and providing an intuitive method for quantitative analysis of positioning accuracy; 2) A forward and inverse kinematic model of the robot is established according to the conventional Cartesian coordinate system motion modeling method, and then a model expression under a unified spherical coordinate system is introduced using spherical coordinate transformation solutions, serving as the mathematical algorithm basis for real-time operator control; 3) After obtaining a motion control algorithm within a quarter-sphere, another motion control algorithm within a quarter-sphere can be derived through mirror projection, greatly simplifying the computational load.

This invention discloses a sequential image fusion enhancement method based on three-axis slide table displacement sensing, belonging to the field of machine vision technology. The method includes the following steps: S1, constructing discretized state and output equations to complete the three-axis slide table motion modeling, and establishing the correlation between motion parameters and image blur kernel and pixel position offset; S2, using the inverse function of the camera response function combined with Taylor linearization, performing exposure normalization processing on multiple frames of observed images to obtain scene illumination estimates with a unified radiance scale; S3, obtaining multi-dimensional fusion weights for the image by setting a fusion weight calculation mechanism that correlates the quality features of the fused image with the slide table position; S4, completing the fusion of multiple frames of images through a weighted fusion model to obtain a high-quality single-frame fused image. Using the above method, the problems of image motion blur and uneven multi-frame imaging quality caused by three-axis slide table motion are effectively solved, achieving accurate fusion and deblurring enhancement of sequential images.