51 results about "Artifact suppression" patented technology

The invention discloses a brain power supply imaging method and system based on a physical information neural network. The method comprises the following steps: establishing a three-layer boundary element method head model based on a Maxwell equation set under quasi-static approximation and calculating a lead matrix; carrying out filtering, artifact suppression and standardization processing on the multi-channel electroencephalogram signals; inputting the standardized signal into a neural network encoder to extract a time sequence feature tensor, estimating the source current density based on the feature tensor, and performing forward calculation by using the lead matrix to obtain a predicted scalp potential; constructing a mixed loss function consisting of a data fidelity item, a physical consistency item and a physical heuristic regularization item, and performing network training and optimization by taking minimization of the loss function as a target; and when the training meets a convergence condition, outputting a visualization result of the brain endogenous current density spatial distribution. According to the method, electromagnetic physical constraints are introduced, so that the physical consistency, interpretability and stability of electroencephalogram inversion are improved.

The invention provides a brain-computer interface decoding method and system based on multi-modal signal fusion, and relates to the technical field of brain-computer interfaces. The method comprises the following steps: S1, acquiring a multi-mode signal, unifying a time reference and starting a sliding window detection intention; s2, de-prompting, sequential bias checking and permutation correction, artifact suppression and band-pass filtering are carried out; s3, channel and modal quality is calculated, and a reliability weight and a rejection list are generated and marked; s4, multi-scale coding is carried out, cross-modal alignment is realized through learnable time delay compensation, and a first fusion representation is obtained; s5, performing layering and cross attention fusion under reliability gating so as to align and reconstruct constraint to keep semantic consistency, and obtaining joint semantic representation; and S6, sequence-level decoding is carried out to output semantics / states / instructions. According to the method, bias and noise are suppressed, time delay difference and cross-main-body equipment change are resisted, and decoding accuracy, robustness and practicability are remarkably improved.

The invention relates to the field of medical image processing, and discloses a multi-mode magnetic resonance aneurysm occlusion assessment method and system. The method comprises the following steps: acquiring scanning configuration and a multi-modal magnetic resonance sequence, and generating fused image data through cross-modal registration, artifact suppression and denoising processing; image segmentation, three-dimensional structure reconstruction and topology correction are carried out, and a three-dimensional anatomy and blood flow model is obtained; then, through gridding, index normalization and time sequence alignment processing, individual feature vectors are extracted; and finally, obtaining a blocking mechanism score and probability based on multi-model fusion reasoning, completing three-dimensional visual rendering, and backfilling key parameters to a configuration structure to form closed-loop optimization. According to the method, multi-modal data are effectively integrated, the evaluation accuracy is improved, and self-adaptive optimization and result traceability of the system are realized through parameter backfilling.

The invention discloses a real-time self-adaptive electroencephalogram artifact suppression and enhancement embedded system. The system aims at overcoming the limitation of an existing portable EEG device in the aspect of artifact processing, various artifacts in EEG signals are recognized and removed in a low-power-consumption, high-precision and real-time mode, and meanwhile the quality of effective EEG signals is optimized and enhanced. One key innovation of the invention lies in breaking through the limitation of single EEG signal processing, and accurate separation and correction of artifacts are carried out through the assistance of EOG and EMG. For example, the EOG signal is used for assisting eye movement artifact removal through synchronous correlation analysis and an adaptive regression filter; the EMG signal is used for assisting myoelectricity artifact correction, and suppression is carried out through spectral analysis and dynamic adjustment of filter parameters. In addition, the system also has the functions of real-time detection and repair of electrode artifacts. According to the system, high-quality EEG signals close to offline processing are provided on portable embedded equipment with limited resources, and the system has universality and high-precision application potential and can be widely applied to a plurality of frontier fields such as medical diagnosis, brain-computer interface (BCI), cognitive ability enhancement, neurological rehabilitation training, sleep disorder monitoring and human-computer interaction.

A plant three-dimensional reconstruction method for a weak texture image and scale distortion comprises the following steps: step 1, recovering an initial Gaussian point cloud and camera parameters through an SfM method by using a multi-view RGB image; 2, differential rendering is executed in training iteration, a predicted image is compared with a real image, and a loss function containing various constraints is calculated to serve as a basis for gradient solving and parameter updating; 3, calculating a pixel weighted average gradient and an artifact suppression coefficient of each Gaussian in a visible view angle; 4, updating Gaussian parameters through back propagation, and adaptively triggering Gaussian cloning, splitting or deleting operation based on the pixel gradient, the covariance matrix and transparency; and 5, dynamically adjusting the Gaussian projection matrix according to the focal length and the resolution of the camera during rendering so as to maintain scale consistency, and performing anti-aliasing rendering in combination with pixel-level super-sampling. According to the method, the reconstruction precision and the structure reduction capability of the virtual plant in the weak texture region are remarkably improved.

The invention discloses an information data processing system based on brain wave signals, and particularly relates to the field of medical signal processing and brain science application, and the system comprises a data importing and filtering module which supports the input of an original electroencephalogram file and carries out band-pass filtering on the original electroencephalogram file; the bad track detection module is used for automatically detecting and marking bad tracks based on a local outlier factor algorithm, and comparing local density differences between each channel and surrounding channels; the artifact suppression module is used for correcting non-engraving plate transient artifacts in the electroencephalogram; the data restoration and interpolation module is used for carrying out interpolation on the detected bad track and restoring the overall channel layout; the frequency band and ratio characteristic module is used for calculating energy, relative power and ratio of delta, theta, alpha, SMR, beta and betaH frequency bands; the spectrum-space-time characterization module is used for constructing the features into two-dimensional tensors; and the judgment module is used for outputting the mental state category and the attention score. The mental state can be identified based on the juvenile electroencephalogram.

The present application belongs to the field of CT tomographic reconstruction technology and artificial intelligence, and discloses a limited-angle CT reconstruction artifact suppression method based on a multi-domain feature fusion network. In view of the problem that the reconstruction result of traditional CT scanning under limited-angle conditions is prone to artifacts and structural distortion, thereby affecting the image quality and defect detection accuracy, the present application constructs a multi-domain feature fusion artifact suppression network, takes the limited-angle reconstruction result as input, and realizes artifact suppression and detail recovery through the synergistic effect of the encoder part, the decoder part, the feature enhancement part and the feature conversion part. The present application can obtain high-quality tomographic images under limited-angle conditions, effectively reduces the scanning angle and time of industrial CT detection, improves the imaging clarity and reliability without increasing the radiation dose, is suitable for industrial detection of complex structure workpieces, and has important industrial application value.

A computational pupillometry system comprises an imaging device configured to capture video frames of a subject's eye and processors executing instructions to perform advanced pupillary assessment. The system employs multi-frame integration techniques, including super-resolution algorithms that utilize sub-pixel shifts between frames, temporal averaging for noise reduction, and parallax-based artifact mitigation to enhance measurement accuracy. Artificial intelligence models, including temporal neural networks, analyze the enhanced pupillary data to determine pupillary parameters and calculate a light-invariant Pupil Reactivity (PuRe) score. The system processes ambient lighting conditions through computational models that analyze video frames before and after controlled stimulation, enabling consistent scoring across varying environmental conditions. Quality assurance mechanisms provide pre-recording and post-recording validation with real-time feedback. The system integrates with electronic medical records through standardized healthcare protocols and supports synchronized, multi-device deployment across healthcare networks.

The application relates to a mode filter, a topology optimization method and an ultrasonic guided wave damage imaging artifact suppression method, relates to the field of ultrasonic detection and imaging, and the topology optimization method comprises the following steps: acquiring initial design parameters, generating an initial population, each chromosome individual in the initial population is coded by a binary logic matrix representing material distribution and meeting design constraints; a population is optimized by using a genetic algorithm, an adaptability function in the genetic algorithm is constructed based on a mode purity index and an energy transmission index, the mode purity index is the ratio of in-plane displacement integral in a transmission area in finite element simulation with the mode filter to total displacement integral, and the energy transmission index is the ratio of relative energy at a transmission end in the finite element simulation with the mode filter to relative energy of an S0 mode; and final optimized material distribution of the mode filter is output. Compared with the prior art, the application has the advantages of effectively suppressing background artifacts, giving consideration to mode regulation performance and substrate structure integrity and the like.

The present invention relates to vibration correction. A vibration artifact suppression method 100 includes: a) step 110 of reconstructing a three-dimensional image of an object of interest based on projection data acquired by an X-ray imaging system; b) step 120 of determining whether the reconstructed three-dimensional image includes a metal object; and c) step 130 of performing a vibration correction method based on a metal object segmented in the reconstructed three-dimensional image in response to the determination that the reconstructed three-dimensional image includes a metal object; or c2) step 140 of performing a vibration correction method based on an object of interest segmented in the reconstructed three-dimensional image in response to the determination that the reconstructed three-dimensional image does not include a metal object. Using the vibration artifact suppression method, the number of iterations is significantly reduced, resulting in a significant speedup of the overall reconstruction time.

This invention relates to a method for suppressing artifacts in neurally modulated stimuli and reconstructing effective features of a brain-computer interface. The method involves simultaneously acquiring multi-channel neural electrical signals and stimulus event information; establishing a preset time window centered on the event moment; extracting transient evidence features, saturation evidence features, and recovery evidence features that occur synchronously with the stimulus from the neural electrical signals; arbitrating the credibility of the stimulus event based on the event information and evidence features to obtain the event arbitration type; generating a corrected event moment when a time offset is detected to form a credible event table; determining the artifact suppression mode and its application window according to the event arbitration type, whereby the artifact suppression mode includes strong suppression, weak suppression, or bypass processing, and performing artifact suppression or bypass processing on the corresponding neural electrical signals; extracting effective features of the brain-computer interface after suppression; and reconstructing features for the feature gaps caused by suppression according to the event arbitration type, outputting the reconstructed features and credibility markers.

The application discloses a kind of diffusion prior-based adversarial generative remote sensing image super-resolution method, belong to remote sensing image processing technical field.The application divides super-resolution reconstruction into two independent stages of degradation removal and information regeneration: first, through degradation removal network, low-resolution remote sensing image is executed to carry out degradation suppression processing such as denoising, deblurring, artifact suppression, eliminate the content-independent degradation interference caused by imaging and transmission;Again, the degraded removal image is input into the generation network to realize high-resolution information regeneration, the generation network is initialized using the pre-trained diffusion model parameters to have the prior distribution characteristics of remote sensing image, and the content consistency and detail authenticity of the generated image are ensured by combining the adversarial optimization of the discriminant network and the pixel-sensing joint fidelity constraint in the training stage;Inference stage only needs one forward mapping to output high-resolution results, while supporting visual language model semantic text condition guidance, to realize controllable adjustment of generation results.

This invention discloses a method for determining exponential distribution delay based on light intensity and event threshold control, comprising the following steps: Based on the fact that DVS image sensor events exhibit exponential distribution characteristics in the time domain when image information changes rapidly in a scene, an exponential distribution model is established that correlates the time constant with light intensity and a threshold; according to the exponential distribution model and the parameters calculated by the model, the exponential distribution delay of the DVS image sensor event to be analyzed in the time domain is determined, which is characterized by the event trigger rate, including the delay magnitude and its distribution in the spatial domain. This invention, based on the exponential distribution delay model, can quickly and conveniently process and calculate the exponential distribution delay of pixels, providing a basic explanation for the origin of motion artifacts in low-light conditions, and assisting researchers in developing artifact suppression methods based on delay mechanisms.

The present invention relates to a system and method for real-time screening of Attention Deficit Hyperactivity Disorder using electroencephalographic signals processed through a GPU-accelerated time-frequency inference architecture. The invention enables continuous acquisition of multi-channel electroencephalographic data, adaptive preprocessing for artifact suppression and signal stabilization, and parallel execution of time-frequency transformations to extract neurologically relevant features in real time. Extracted features are analyzed using an inference process configured to identify neurological patterns associated with Attention Deficit Hyperactivity Disorder, while continuous validation of signal quality and temporal consistency ensures diagnostic reliability. The system dynamically adapts analytical parameters based on evolving signal characteristics and regulates computational workload to achieve energy-efficient operation during prolonged monitoring. The invention provides a technically integrated, machine-implemented screening solution capable of delivering reliable, real-time neurological assessment suitable for clinical environments.

The invention discloses a PS-OCT polarization artifact suppression method based on a cross coupling coefficient. Comprising two parts of pre-measurement based on matrix calculation and PS-OCT data processing based on a cross coupling coefficient, according to the pre-measurement, a dual-channel interference signal is obtained by scanning a wave plate through a PS-OCT system, and then a ghosting artifact displacement set and the cross coupling coefficient are obtained; in the data processing step, a PS-OCT data processing method based on a cross coupling coefficient is used for detecting a sample and processing a dual-channel interference signal, and finally a PS-OCT image without polarization artifacts is obtained. Through the cross coupling coefficient, the cross coupling effect generated by various polarization elements can be quantified, and accurate suppression of polarization artifacts is achieved; the cross coupling coefficient is obtained in advance through the detection wave plate, in actual imaging, the cross coupling coefficient does not need to be measured any more, and the speed of suppressing polarization artifacts is increased; on the premise that the original configuration of the system is not changed, the polarization artifacts are comprehensively suppressed.

The invention relates to a mode filter, a topological optimization method and an ultrasonic guided wave damage imaging artifact suppression method, and relates to the field of ultrasonic detection and imagines.The topological optimization method comprises the steps that initial design parameters are obtained, and an initial population is generated; each chromosome individual in the initial population is formed by coding a binary logic matrix which represents material distribution and meets design constraints; a genetic algorithm is adopted for population optimization, a fitness function in the genetic algorithm is constructed based on a mode purity index and an energy transmission index, and the mode purity index is a ratio of a transmission region in-plane displacement integral to a total displacement integral under finite element simulation added with a mode filter; the energy transmission index is a ratio of relative energy of a transmission end under finite element simulation added with a mode filter to relative energy of an S0 mode; and outputting the final optimized material distribution of the mode filter. Compared with the prior art, the method has the advantages that background artifacts are effectively inhibited, and the mode regulation and control performance and the matrix structure integrity are both considered.

A physiological mechanical signal monitoring system and method utilizing motion state to suppress artifacts, belonging to the field of biomedical signal monitoring and processing technology, aims to improve the existing distributed multi-node vibration and physiological signal acquisition systems, which suffer from severe crosstalk between multiple channels, signal aliasing caused by artifacts, and feature distortion, failing to meet the requirements of high-fidelity, multimodal dynamic acquisition. Key technical points: The signal monitoring system includes a multi-point distributed acquisition module, a motion state recognition module, an artifact detection and adaptive filtering module, and a main control and signal fusion module. The main control and signal fusion module is used to fuse and reconstruct the multi-channel physiological mechanical signals after artifact suppression processing, outputting high-fidelity physiological signals. This invention significantly reduces the coupling effect between different channels caused by environmental vibration, body motion impact, and matrix resonance through independent support of multiple spatial nodes and isolation of mechanical paths, significantly improving the spatial discrimination ability and original independence of the signals. This invention achieves multi-point physical isolation at the structural level, and then combines vector superposition to achieve preliminary noise reduction, effectively reducing artifacts caused by non-target actions and environmental disturbances.

The invention provides a CBCT image tooth segmentation method based on deep learning, and relates to the technical field of oral cavity image segmentation, and the method comprises the steps: extracting a two-dimensional slice from a CBCT image as an input image; carrying out normalization, artifact suppression and data enhancement on the image to improve the preprocessing quality; constructing a segmentation network structure fusing self-calibration multi-scale attention and a mixed attention mechanism, wherein the segmentation network structure is used for extracting features and enhancing context information; training a model based on the tooth image data set, and performing parameter learning by adopting a joint loss function and an optimizer; and finally, tooth segmentation is realized through the trained model, and three-dimensional reconstruction is carried out. The method has the advantages of high segmentation precision, high processing speed, light structure and the like, and is suitable for automatic identification and diagnosis assistance of teeth in stomatology.

The invention belongs to the field of CT (Computed Tomography) tomography reconstruction technology and artificial intelligence, and discloses a finite angle CT reconstruction artifact suppression method based on a multi-domain feature fusion network. In order to solve the problem that image quality and defect detection precision are affected due to the fact that a reconstruction result of traditional CT scanning is prone to artifacts and structural distortion under the condition of a limited angle, a multi-domain feature fusion artifact suppression network is constructed, and a limited angle reconstruction result is used as input. Through the synergistic effect of the encoder part, the decoder part, the feature enhancement part and the feature conversion part, artifact suppression and detail recovery are realized. According to the method, high-quality cross-sectional images can be obtained under the limited angle condition, the scanning angle and time of industrial CT detection are effectively reduced, the imaging definition and reliability are improved on the premise that the radiation dosage is not increased, and the method is suitable for industrial detection of workpieces of complex structures and has important industrial application value.

The application discloses a multi-modal medical image intelligent fusion diagnosis process, which comprises the following core process steps: S1, multi-modal image data acquisition and preprocessing, collecting original data of at least two kinds of clinically commonly used image modalities, performing format standardization, artifact suppression, gray scale normalization and quality screening processing on the original data, and obtaining standardized image data; S2, cross-modal adaptive registration and fusion integrated processing, based on a cross-modal registration and fusion integrated network, synchronously realizing spatial alignment and preliminary feature fusion of the standardized image data, and generating an aligned feature map; and S3, layered feature extraction, adopting a differentiated feature extraction architecture adapted to different modal imaging characteristics.The application realizes the transparency and traceability of the diagnosis process through feature contribution degree visualization and diagnosis logic tracing, enables a clinician to clearly understand the diagnosis basis, meets the requirement of clinical diagnosis and treatment on explainability, significantly improves the trust degree of the clinician on the diagnosis result, and reduces the clinical application threshold.

The invention discloses an HDR image reconstruction method based on spatial convolutional coding and dynamic convolution. The method comprises the steps of obtaining an LDR image data set, constructing an HDR image reconstruction network, training the HDR image reconstruction network and testing the HDR image reconstruction network. Due to the fact that the space convolution coding module, the dynamic convolution space attention module and the linear cross frame UFO module are adopted, the problems that in the prior art, artifacts are caused by large motion of an object in a dynamic scene, and the detail and color recovery precision is insufficient under the complex illumination condition are solved. Experimental results show that the method significantly improves the HDR reconstruction quality in a dynamic scene, and has strong reconstruction capability in the aspects of color fidelity and visual perception quality. The method has the advantages of high artifact suppression capability, high detail and color recovery precision, high adaptability and the like, and is suitable for the technical fields of mobile phone photographing, photography, screen display, video monitoring, virtual reality and the like.

The invention discloses a magnetic resonance training data generation method and system based on GPU acceleration Bloch simulation and combined with modal transformation, and relates to medical imaging. The method comprises the following steps: establishing a multi-parameter virtual object library, and defining voxel-level T1, T2, PD and B0 / B1 distribution and coil sensitivity; according to an input sequence and acquisition parameters, time domain simulation is carried out by adopting GPU parallel Bloch solution, and non-ideal factors are injected as required; reconstructing to obtain k space and image domain data and generating paired labels; target comparison, organs and scenes are expanded through rule-driven or data-driven modal transformation; samples and metadata are packed to support traceability and verification. The system is composed of a parameter configuration module, a virtual object library module, a GPU simulation module, a reconstruction module, a modal transformation module and a data set management module. On the premise that physical consistency is guaranteed, data generation efficiency and diversity are remarkably improved, and the method is suitable for downstream tasks such as reconstruction, artifact suppression and quantitative imaging.

The invention discloses an electrode acquisition method based on a light-operated ion channel. The method comprises the following steps: recording a membrane current by adopting an integrated optical fiber electrode in combination with real-time artifact compensation at a single cell layer; space-time coding light stimulation and network characteristic analysis are realized by group recording through a microelectrode array; the planar microelectrode chip is integrated with optical waveguide and microfluidics, and light regulation and drug perfusion are carried out in parallel; a wireless silicon probe is used for synchronous behavior and light stimulation in an in-vivo experiment; noninvasive EEG recording is combined with deep light regulation and control, and light induction response is separated; and finally, fusing cross-scale data through tensor decomposition and machine learning, and optimizing photostimulation parameters. The method has the advantages that the light artifact inhibition rate is increased to 92% or above, the light energy utilization rate is increased by 38%, the multi-mode data processing efficiency is increased by 20 times, the problems of single-mode collection limitation, artifact interference and low regulation efficiency are effectively solved, and an innovative tool is provided for neuroscience research and clinical application.

The invention belongs to the technical field of femtosecond laser processing, and relates to a global adaptive filling and boundary splicing mark suppression method, which comprises the following steps of: 1) acquiring a breadth to be processed, and dividing the breadth to be processed into a plurality of sub-regions; the method comprises the steps of (1) obtaining a to-be-processed breadth, (2) generating a scanning filling path in each sub-region obtained in the step (1), and (3) optimizing the scanning filling paths of all the sub-regions in the to-be-processed breadth obtained in the step (2) to complete global adaptive filling and boundary splicing mark suppression. The global adaptive filling and boundary splicing mark suppression method is high in precision and efficiency.

A system and a method for noise artifact mitigation in Time-of-Flight cameras is disclosed. A receiving module receives a depth frame of an image captured by the Time-of-Flight camera. A noise artifact detection module includes an artificial intelligence module detecting one or more regions affected by noise artifacts and output corresponding to a plurality of bounding box coordinates. A noise artifact verification module verifies whether the one or more regions detected are affected by the one or more noise artifacts or represents false positives using a noise artifact identification algorithm. A depth reconstruction module removes a false depth information from the one or more regions, estimate a corrected depth information for the one or more regions and employ a statistical based depth reconstruction algorithm with a region-specific reconstruction filter to replace the false depth information to generate a final denoised depth frame.

The application relates to the field of medical image processing and discloses a multi-modal magnetic resonance aneurysm occlusion evaluation method and system. The method comprises the following steps: acquiring a scanning configuration and multi-modal magnetic resonance sequences, generating fusion image data through cross-modal registration, artifact suppression and denoising processing; then performing image segmentation, three-dimensional structure reconstruction and topology correction to obtain a three-dimensional anatomy and blood flow model; then extracting an individual feature vector through gridding, index normalization and time sequence alignment processing; finally, obtaining an occlusion mechanism score and probability based on multi-model fusion reasoning, completing three-dimensional visualization rendering, backfilling key parameters to a configuration structure, and forming a closed-loop optimization. The application effectively integrates multi-modal data, improves evaluation accuracy, and realizes adaptive optimization and result traceability of the system through parameter backfilling.

The application provides a kind of industrial CT scattering and beam hardening coupling artifact suppression method based on deep learning, belongs to the field of industrial CT image processing, for the problem that coupled artifacts cannot be accurately separated and corrected in industrial CT imaging, a beam hardening, scattering coupling artifact model based on real CT image artifact features is constructed, a simulated image and an actual image are mixed to construct a data set by a coupled model representation equation, and artifact suppression and image enhancement are completed by a long-short connection, structure doublet improved neural network, effectively removing multiple types of coupled artifacts and noise, this method is suitable for coupled artifact suppression of any complex structure actual CT image, and the practicability and generality of the method are better.

This invention provides a method for processing evoked electromyography (EMG) artifacts based on slope acceleration detection and morphological scaling. The method includes: acquiring EMG signals with the stimulus trigger as the zero point of time; extracting the terminal resting segment, calculating the first-order difference standard deviation, and constructing an adaptive threshold; calculating the local slope amplitude, sign, and historical slope reference point by point within the search interval, and determining the artifact end index based on polarity reversal, slope acceleration, or smooth exit criteria; linearly scaling the artifact segment using the end point as an anchor point to suppress artifact amplitude and eliminate boundary steps; performing a second-order bidirectional zero-phase bandpass filter on the scaled signal to filter out interference and retain waveform phase; scaling the original signal and backfilling it into the artifact segment, followed by level alignment and square root fade-in processing to reconstruct a smooth and continuous physiological signal. This invention achieves precise positioning of artifact boundaries and distortion-free signal reconstruction, possessing advantages such as thorough artifact suppression, no ringing distortion, complete preservation of physiological morphology, and strong adaptability.

The application belongs to the technical field of brain-computer interface, and particularly relates to a brain-controlled digital human interaction system based on a brain-computer interface and artificial intelligence, which comprises a multi-modal neural signal acquisition unit, a cross-modal noise suppression and complete form compensation unit and a digital human interaction control unit. The multi-modal neural signal acquisition unit adopts the same master clock trigger frequency sampling to respectively acquire three types of modal data, and injects the three types of modal data into an on-chip ring-shaped double buffer in real time. The cross-modal noise suppression and complete form compensation unit is used for performing multi-stage artifact suppression on a cross-modal data stream, and then performing complete form compensation based on time domain similarity discrimination to obtain a cross-modal neural representation sequence. The digital human interaction control unit is used for realizing digital human control according to the cross-modal neural representation sequence. The application can stably output coherent and natural digital human actions in a complex interaction scene.

The invention discloses a multi-mode fundus imaging system based on an FPGA and a real-time eye movement compensation method, and belongs to the field of medical imaging equipment. The system comprises an optical imaging and tracking module, an FPGA processing control module, a storage module and a system control and display unit. A unified frame-level clock management unit is arranged in the FPGA module and is used for synchronously scheduling scanning of the tracking light beam and the imaging light beam; eyeball displacement parameters are solved in real time through a hardware feature matching unit, and initial coordinates of cSLO and SD-OCT scanning galvanometers are corrected in a feedforward mode according to the parameters before each imaging frame or scanning block starts, so that frame-level eye movement compensation is achieved; meanwhile, preprocessing, displacement-based multi-frame alignment accumulation and image registration and fusion are performed on cSLO and SD-OCT data streams in parallel in a hardware pipeline. According to the invention, real-time synchronization, motion artifact suppression and high-precision fusion of multi-modal data are realized through full-hardware processing, and the imaging quality, the system response speed and the repeatability of follow-up scanning are remarkably improved.