342 results about "Dynamic imaging" patented technology

The invention provides an acute cerebral apoplexy early-stage intelligent screening and early warning method and system based on deep learning, and relates to the technical field of medical imaging and artificial intelligence, and the method comprises the steps: obtaining a multi-temporal angiography image of the brain of a patient through a pulse-type dynamic imaging scheme, extracting hemodynamic features through a double-flow feature extraction network, and obtaining a multi-temporal angiography image of the brain of the patient; and constructing a vasculopathy risk distribution diagram by combining a vascular structure analysis model, and marking a potential embolism risk area. Furthermore, in combination with tissue perfusion parameters obtained by multispectral imaging and brain tissue function parameters obtained by electroencephalogram signals, an ischemic penumbra region is identified by using a self-adaptive threshold segmentation model. And finally, based on the ischemic penumbra area, the tissue perfusion parameters and the brain tissue function parameters, calculating a risk level by using a hierarchical decision model with a space-time attention mechanism, and generating a corresponding clinical intervention scheme. According to the invention, the early accurate screening and risk early warning of acute cerebral apoplexy can be realized, and the clinical intervention efficiency is improved.

The invention discloses an anesthesia equipment fault diagnosis method and system based on image recognition, particularly relates to the technical field of medical equipment fault detection, and is used for solving the problem that a non-contact image detection method in existing anesthesia equipment pipeline leakage detection is poor in transparent pipeline weak leakage signal recognition capability. Through combination of multispectral dynamic imaging and polarized light scattering mode analysis, pipeline surface microscopic deformation characteristics are obtained, and a leakage path prediction result is generated. Environmental reflective noise is inhibited based on spectral distribution characteristics of the reference image set, optical response changes of the candidate area are triggered in combination with active pressure disturbance, and the inverse proportion physical relation between the permeation rate and the fluid conduction speed is verified; through dynamic threshold screening and spatial continuity verification, accurate positioning of a target area is realized, finally, spectral features, path forms and real-time pressure parameters are fused to generate leakage risk assessment, non-intrusive high-sensitivity leakage identification is realized, and reliable guarantee is provided for safe operation of clinical anesthesia equipment.

The invention relates to the technical field of magnetic resonance imaging, in particular to an intelligent sparse low-rank non-Cartesian magnetic resonance dynamic imaging method, which comprises the following steps of: acquiring magnetic resonance k-space data which is subjected to non-Cartesian continuous sampling by adopting multiple coils, continuously sampling data, rearranging the continuously sampled data along a time dimension by using a framing operator to obtain under-sampled k-space data, and then inputting the framed non-Cartesian under-sampled k-space data to be reconstructed into a trained network for image reconstruction. And reconstructing a final dynamic image through depth space-time sparsity, time low-rank learning, data consistency verification and loss function constraint. Compared with the prior art, the non-Cartesian magnetic resonance dynamic image reconstruction method has the advantages that the image is reconstructed through multiple times of network feedback iteration, the reconstruction speed of the non-Cartesian magnetic resonance dynamic image is greatly increased, and the temporal-spatial resolution is improved.

The invention discloses a multi-state wireless network signal dynamic imaging system and method based on meta universe, and relates to the technical field of wireless communication. According to the method, a spatial dimension prediction model and a time dimension prediction model are combined, and spatial distribution of wireless network signals is accurately predicted through a data fusion technology; the reliability and the accuracy of wireless network signals are improved by dynamically adjusting a reference weight optimization prediction result; according to the invention, by collecting the real-time network requirements of the user including the video traffic and the file uploading and downloading and the real-time requirements of the user on the wireless network signal quality, intelligent and dynamic network signal quality evaluation can be realized, and the network quality in different use scenes can meet the personalized requirements; through the signal intensity heat map and sound prompt, a user can timely obtain feedback related to wireless network signal quality, and the user is helped to find an area with the best signal in a complex environment, so that more optimized network experience is realized.

The present invention relates to X-ray imaging. In order to improve the throughout of an X-ray imaging system, an apparatus is provided for managing an imaging workflow of an X-ray imaging system for an upcoming imaging sequence. The apparatus comprises an input unit, a processing unit, and an output unit. The input unit is configured to receive data including (i) a current temperature profile of an X-ray anode inside a tube housing of the X-ray imaging system, (ii) information about a heat capability and a cooling rate of the X-ray anode, (iii) information about a heat capability and a cooling rate of the tube housing, (iv) information about a current operation condition of the X-ray imaging system, and (v) information about a planned operation condition of the X-ray imaging system for the upcoming imaging sequence, wherein the planned operation condition comprises a sequence of planned scans, each planned scan being associated with a respective set of planned scan parameters. The processing unit is configured to determine, based on the received data, an estimated temperature profile of the X-ray anode under the planned operation condition for the upcoming imaging sequence. The processing unit is configured to compare the estimated temperature profile of the X-ray anode with a maximum allowable hardware temperature of the X-ray anode. In response to the determination that the estimated temperature profile is greater than or equal to the maximum allowable hardware temperature of the X-ray anode, the processing unit is configured to modify the planned operation condition such that the estimated temperature profile of the X-ray anode under the modified planned operation condition is less than the maximum allowable hardware temperature of the X-ray anode. The modified planned operation condition comprises a change of the sequence of planned scans and / or a change of the set of planned scan parameters for one or more planned scans. The output unit is configured to provide the modified planned operation condition for the upcoming imaging sequence.

The invention relates to the technical field of medical imaging, in particular to a nuclear medicine imaging method and system, and the method comprises the steps: patient preparation, tracer agent distribution detection, image enhancement, multi-target dynamic imaging, image quantitative analysis and data fusion, and result output and diagnosis suggestion. A preliminary image of tracer agent distribution in the body of a patient is obtained through a multi-modal imaging device, a high-resolution image is generated through a convolutional neural network noise suppression algorithm, a contrast ratio self-adaptive enhancement algorithm and a super-resolution reconstruction algorithm based on a generative adversarial network, and accurate dynamic analysis is carried out. In combination with a random forest algorithm and a time sequence analysis model, performing quantitative analysis on the radioactive intensity, the volume and the metabolic rate of the target spot, and generating personalized diagnosis suggestions; according to the invention, the precision and diagnosis effect of nuclear medicine imaging are greatly improved, and the method is suitable for early diagnosis, curative effect evaluation and disease progress monitoring of clinical diseases.

The invention relates to the technical field of magnetic resonance imaging, and discloses a motion estimation guided magnetic resonance four-dimensional dynamic imaging method and device, and the method comprises the steps: obtaining a three-dimensional single-frame static image of magnetic resonance and an under-acquired low-resolution four-dimensional dynamic image sequence, the low-resolution four-dimensional dynamic image sequence is a multi-frame low-resolution four-dimensional dynamic image obtained by carrying out undersampling on K space acquisition data; utilizing a motion encoder in the image sequence prediction model to extract motion estimation information in the low-resolution four-dimensional dynamic image sequence; inputting the three-dimensional single-frame static image into a structure encoder in the image sequence prediction model to obtain static anatomical structure information; and fusing and decoding the motion estimation information and the anatomical structure information through a decoder to obtain a high-resolution four-dimensional dynamic image sequence. The problems that an existing magnetic resonance four-dimensional imaging technology is long in scanning time, complex organ movement is difficult to capture, and time-space consistency is poor are solved.

The present invention provides an early intelligent screening and warning method and system for acute stroke based on deep learning, which relates to the field of medical imaging and artificial intelligence technology, including obtaining multi-phase angiography images of the patient's brain through a pulsed dynamic imaging scheme, extracting hemodynamic characteristics using a dual-stream feature extraction network, and constructing a vascular lesion risk distribution map in combination with a vascular structure analysis model to mark potential embolism risk areas. Further, combining tissue perfusion parameters obtained by multispectral imaging and brain tissue function parameters obtained by electroencephalogram signals, an adaptive threshold segmentation model is used to identify the ischemic penumbra area. Finally, based on the ischemic penumbra area, tissue perfusion parameters, and brain tissue function parameters, a hierarchical decision model with a spatiotemporal attention mechanism is used to calculate the risk level and generate a corresponding clinical intervention plan. The present invention can achieve early and accurate screening and risk warning of acute stroke, and improve the efficiency of clinical intervention.

The invention discloses an HDR image reconstruction method based on progressive feature alignment and quality guide fusion, and the method comprises the steps: inputting a to-be-processed LDR image sequence into a trained progressive quality guide image reconstruction model, and obtaining an image reconstruction result; according to the model, after an input LDR image sequence is linearized, a progressive variable feature alignment module aligns non-reference frame features through a multi-scale pyramid and cascade deformable convolution, and multi-scale features are output through exposure perception modulation; the quality guide motion compensation module iteratively optimizes an optical flow field and a confidence coefficient mask through a cascade optical flow quality refining structure block; and the structure maintenance fusion module aligns the HDR sequence by using the optical flow field, performs dynamic weighted fusion in combination with a confidence mask, and finally enhances details through a Laplace attention mechanism and outputs a ghosting-free HDR image. According to the method, through progressive alignment and fusion of quality perception, the balance problem of ghosting and detail loss in dynamic HDR imaging is solved.

The invention discloses a tympanic membrane repair postoperative wound assessment method based on artificial intelligence, and relates to the technical field of medical assessment, and the method comprises the steps: obtaining multi-modal image data of a reference tympanic membrane and a target tympanic membrane through a multi-modal dynamic imaging fusion technology, determining a target biomechanical feature ratio through a biomechanical feature fusion technology, and obtaining a target tympanic membrane target biomechanical feature ratio; the method further comprises the steps of obtaining a physiological parameter set of the reference tympanic membrane and the target tympanic membrane, training an associated parameter prediction model to construct a metabolism-mechanical associated model, and achieving accurate evaluation of tympanic membrane wound healing conditions. The invention also covers related electronic equipment, a computer readable storage medium and a computer program product.

The invention provides a dynamic recognition system and method based on a light-operated nerve synapse array, and relates to the technical field of photoelectric pulses, and the system comprises a lens imaging module which is configured to collect light signals and transmit the light signals to the light-operated nerve synapse array so as to adjust the resistance value of the light-operated nerve synapse array; the displacement buffer module is configured to receive the serial input signal of the micro-control unit, convert the serial input signal into a parallel output signal, and transmit the parallel output signal to the light-operated nerve synapse array after the register is filled up; the light-operated nerve synapse array comprises a plurality of light-operated nerve synapses, and under the condition that the parallel output signal is low level, the corresponding target light-operated nerve synapses work and are connected with the analog-to-digital converter; the analog-to-digital converter is configured to collect a voltage value of the target light-operated nerve synapse and determine a resistance value of the target light-operated nerve synapse based on the voltage value; and the dynamic imaging module is configured to convert the resistance value into a gray value image.

The invention provides an exhaled gas alcohol content intelligent verification system based on image recognition, relates to the technical field of intelligent detection, and aims to solve the problems that a traditional sensor is prone to environmental interference, needs frequent calibration and is insufficient in dynamic response. Comprising a gas-sensitive developing unit, a multispectral dynamic imaging unit, a data processing unit and a closed-loop control unit, the gas-sensitive color development unit adopts a reversible gas-sensitive film and generates a color change signal through an alcohol specific color development reaction; the multispectral dynamic imaging unit is combined with an ambient light suppression and dynamic region positioning technology to capture spectral reflectivity time sequence data in a color development process; the data processing unit maps the chromaticity characteristic into an alcohol concentration value based on a Gaussian process regression model, and triggers near-infrared auxiliary verification through confidence evaluation; and the closed-loop control unit maintains a constant detection environment through the temperature control and airflow stabilization module. According to the scheme, non-intrusive high-precision detection of the alcohol concentration in a complex environment can be realized.

The present invention discloses a phantom for testing the dynamic imaging performance of a CT device, and relates to the technical field of medical imaging and radiotherapy. The present invention includes a phantom, which is composed of a fixed phantom, a moving phantom and a driving part, wherein the driving part drives the moving phantom to perform linear motion in the fixed phantom; the moving phantom is composed of a hollow tube, a target, a head, and a connecting plate; the driving part is a connecting rod mechanism, which is driven by a motor controller to control a motor, and the turntable and the connecting plate are connected by a connecting rod for rotation, and a plurality of holes are distributed along the radial direction on the turntable, and the connection between the connecting rod and the turntable is realized by inserting a first pin shaft into different holes, and the turning radius of the first pin shaft is quickly changed, thereby changing the motion range and crank length of the moving phantom, and realizing the change of the motion stroke of the moving phantom. The present invention has the advantages of simple structure, and the motion speed, motion stroke and motion curve can be arbitrarily set.

The invention discloses a focused ultrasound transcranial nerve regulation and control platform and method based on photoacoustic imaging. The system comprises a synchronous pulse generator, a pulse ultrasound transmitting system, a photoacoustic signal collecting system and a fixing device. According to the method, the limitation of other imaging monitoring modes is overcome, monitoring is carried out by utilizing the characteristics of non-invasiveness, high resolution and high contrast of photoacoustic imaging, the TUS progress can be monitored for providing real-time dynamic imaging, and functional information can be provided; photoacoustic imaging is applied to focused ultrasound transcranial nerve regulation for the first time. And meanwhile, by utilizing high sound resolution and high optical contrast of photoacoustic imaging, the strongest photoacoustic signal can be provided in a near-infrared region for hemoglobin of a seat endogenous contrast agent, so that the PAT becomes an ideal imaging tool for detecting cerebral hemodynamics. In addition, key pathophysiological parameters are evaluated through functional imaging provided by the PAT, and the method can be used for evaluating brain changes in the low-intensity focused ultrasound stimulation process.

The invention discloses a thoracic cavity heart sound signal non-contact extraction system and method based on structured light. The system comprises a structured light projection module, an image acquisition module, an image processing and displacement extraction module and a signal processing and trigger point identification module, structured light is projected on the surface of the chest of a human body to form a regular pattern, and a high-speed image acquisition means is utilized to record micro displacement change generated by light spots under cardiac drive. Thoracic cavity vibration information is extracted through an image processing technology, periodic feature points corresponding to a cardiac rhythm are recognized in combination with a time sequence analysis strategy, a thoracic cavity heart sound signal is extracted, rhythm analysis is conducted, and a cardiac trigger point is output to serve as a trigger signal for synchronous control of medical equipment. The technical problems of signal distortion, equipment interference, potential safety hazards, inconvenience in use and the like in a strong electromagnetic environment in a traditional contact mode are solved, and a high-precision synchronous trigger signal is provided for dynamic imaging.

The invention relates to the technical field of material performance testing, in particular to a neat paste flow testing method and system based on multi-mode dynamic imaging. The test method comprises the following steps: collecting a continuous infrared image sequence of a neat paste free flowing process; synchronously triggering visible light supplement in a flowing initial stage, and obtaining at least one frame of visible light image; performing registration fusion on the visible light image and the infrared image at the corresponding moment to generate an enhanced image frame; extracting a neat paste contour in real time based on the enhanced image frame, and calculating dynamic parameters; according to the change trend of the dynamic parameters, the flowing stability opportunity is adaptively judged; and triggering final contour extraction at a stable opportunity, and calculating static morphological parameters and dynamic process parameters. Through the multi-modal dynamic imaging and intelligent image fusion technology, complex edge contours are accurately extracted, impurity interference is eliminated, and the measurement precision is improved.

The invention discloses an automatic instrument reading method and system based on embedded machine vision. The method comprises the following steps: deploying an embedded image acquisition unit with wide dynamic imaging capability; performing multi-mode preprocessing of white balance correction, contrast enhancement and noise suppression on the image; self-adaptively identifying the instrument type and positioning a key area by using a lightweight convolutional neural network; hough transform and sub-pixel edge detection are adopted for a pointer type instrument to achieve high-precision angle analysis, and optical character recognition is completed for a digital instrument through a bidirectional long-short-term memory network; and finally, fusing multi-frame results through Kalman filtering, evaluating credibility, and outputting stable readings with timestamps. According to the technical scheme, high-robustness, high-precision and low-delay automatic reading can be realized at the embedded end, and the operation stability and the long-term adaptability of the system in a weak network environment are remarkably improved.

The invention discloses a variable view field Linnik interference microscopic system and a measurement method thereof, and belongs to the field of laser measurement instruments, and a variable diaphragm is introduced into a reference arm of an interference system to realize matching of objective lenses of different test arms, so that light field spatial correlation under speckle illumination is maintained; meanwhile, a light source system with a delay line structure is adopted to accurately compensate the optical path difference introduced by the asymmetric objective lens configuration, so that the acquisition of a high-contrast interference pattern is realized. The method can effectively improve the quality of an interference image, has a depth selection function, and is suitable for dynamic phase imaging in different view field ranges. The technical scheme of the invention shows good imaging effect and adaptability in the aspects of dynamic imaging and depth layering.

The invention discloses an artificial intelligence traffic abnormal event identification method based on a high-altitude visual angle of an unmanned aerial vehicle, particularly relates to the field of traffic management, solves the problem of abnormal event identification caused by parallax distortion in a high-speed cruise scene, and optimizes parallax distortion through cooperation of flight control and zooming driven by a continuous frame motion intensity baseline. Stabilizing a target contour by using a dynamic imaging window, constructing a sparse confidence kernel to analyze multi-dimensional motion, adaptively filtering low-quality candidates and finely trimming abnormal coordinates; the flight attitude and view finding parameters are dynamically adjusted according to the weight of the abnormally dense area, and high-altitude wide-angle coverage and single-vehicle fine-grained monitoring coexist; carrying out confidence analysis to remove parallax artifacts, generating coordinates after abnormal candidate rechecking, and driving flight control fine tuning; each processing link compresses the sensing bandwidth cooperatively, the alarm confidence is improved, the decision chain time delay of discovery, positioning and following is shortened, the coordinate precision and the pushing time efficiency under the conditions of complex illumination and severe parallax are ensured, and the reliable intelligent sensing capability is provided for traffic management.

A system for increasing a frame rate of a dynamic image includes an input for receiving a set of consecutive image frames of a first dynamic image having a first plurality of image frames and a first frame rate. The system further includes a deformation encoding neural network coupled to the input and configured to derive at least one parameter characterizing dynamics of the set of consecutive image frames and to generate an interpolated image frame based on the at least one parameter, and a post-processing module coupled to the deformation encoding neural network and configured to receive one or more interpolated image frames from the deformation encoding neural network, to create a second plurality of image frames comprising the one or more interpolated image frames and the first plurality of image frames, and to generate a second dynamic image using the second plurality of image frames, the second dynamic image having a second frame rate higher than the first frame rate.

A Multi-Angle Dynamic Image system provides multi-angle observation capabilities comprising lamps strategically positioned to obtain high quality images of the security documents or items under analysis, to obtain images with variations in the observation angle of the security document or item under study. The system includes a box platform for placing the security document or item thereupon, the box platform changes its orientation, allowing the system to capture the variations in color and structure as the orientation and the angles of the observation and illumination change. The system also includes a high-resolution linear camera to capture the intricate details while avoiding distortion and parallax issues. The system also includes a computer configured to run algorithms to process image acquisition data for a complete set of observation angles, to pre-process image acquisition data including crop and image standardization, to calculate differences between adjacent angle images, eliminate noise, to calculate a total optical flow using the images at different angles and their differences to generate the Dynamic Images vectorial field, one per orientation, and the Dynamic Map, a heat map generated using the magnitude from the Dynamic Image vectorial field, and calculate from the Dynamic Image the different parameters associated with dynamic properties of the OVD.

The invention relates to the technical field of magnetic resonance imaging, and discloses a magnetic resonance four-dimensional dynamic imaging method and device driven by a physical model, and the method comprises the steps: obtaining the magnetic resonance K space data of a to-be-imaged part in a complete motion process; extracting a preset number of three-dimensional static images in the magnetic resonance K space data; analyzing the three-dimensional static image by using the target training model to obtain a dynamic change mode of the three-dimensional static image, and predicting a static image of a future time frame based on the dynamic change mode to obtain a four-dimensional image sequence; and applying physical constraint to the four-dimensional image sequence and correcting data in the K space domain by using a preset physical driving module, and carrying out multiple iterations to obtain a target four-dimensional dynamic imaging sequence. According to the method, high-resolution imaging of the prediction model is utilized, a small number of static images can generate the magnetic resonance four-dimensional dynamic sequence, and the problems that magnetic resonance imaging is limited in providing organ tissue dynamic motion information and complex dynamic motion joint functions and lesion conditions are difficult to comprehensively evaluate are solved.

The invention discloses a microfluidic-EICP combined dynamic visual monitoring system and method for a heavy metal polluted soil remediation process, and relates to the technical field of soil monitoring. The microfluidic-EICP combined heavy metal polluted soil remediation process dynamic visual monitoring system comprises a microfluidic reaction module, a multi-mode sensing module, a dynamic imaging module and a data analysis module, and the microfluidic reaction module comprises a heavy metal pollution simulation unit and an in-situ reaction control unit. The multi-mode sensing module comprises a substrate layer, a chemical sensing layer and a data interface layer, the dynamic imaging module comprises an optical imaging unit and a spectral analysis unit, and the data analysis module is electrically connected with the communication module. According to the technical scheme provided by the invention, multi-physical field cooperative monitoring of a chemical field, a mechanical field and a permeation field in the prior art is realized, and the cross-scale correlation analysis capability of a micro crystal growth mechanism and a macroscopic soil body modification effect is improved.

The invention discloses a self-adaptive high-dynamic-range image generation method in a chemical monitoring scene, and belongs to the technical field of high-dynamic imaging and computational imaging. Performing registration preprocessing on the acquired reference image based on a brightness consistency criterion; constructing a high dynamic range image generation network model, inputting the reference image into the model, performing feature extraction and fusion through a multi-scale network, and generating a high dynamic range image based on the fused features; performing tone mapping on the generated high dynamic range image based on a brightness consistency criterion; and comparing the generated high dynamic range image with a reference image, calculating a pixel-level loss function and multi-dimensional perception loss, and optimizing an image generation process through a back propagation training network model. According to the method, imaging details of different scales are captured through a multi-scale feature processing method, so that the generated image is better in brightness range and details, and the expressive force of the image under a complex illumination condition is ensured.

The invention belongs to the technical field of nuclear equipment monitoring, and aims to solve the problems of spatial resolution reduction caused by a multi-source acoustic signal aliasing effect in a complex industrial environment and feature extraction difficulty caused by industrial noise and fault sound source spectrum overlapping. According to the method, spatial decoupling is carried out, sparse Bayesian learning and minimum variance undistorted response beam forming joint optimization is carried out, a sound source orientation is preliminarily estimated, synchronous extrusion wavelet transform and NMF joint time-frequency analysis is carried out, and a spatial anomaly acoustic diagnosis result is obtained. Sound source transient characteristics and energy evolution laws are extracted through time-frequency conjoint analysis, a multi-dimensional characteristic space is constructed, the number of fault types is determined for fault type classification, and three-dimensional coordinate mapping and error feedback are displayed; the system comprises an airspace decoupling module, a time-frequency decomposition module, a feature fusion module and a dynamic imaging module. According to the invention, the sound source positioning precision can be realized, and the resolution limitation of traditional beam forming is broken through.

The invention relates to the technical field of deep learning, and provides a mammary gland blood oxygen dynamic imaging method and system based on multispectral fusion, and the method comprises the steps: irradiating mammary gland tissues, and collecting mammary gland tissue reflected light image sequences under different wavelengths to obtain multispectral time sequence data; constructing an adaptive weight prediction network model, and dynamically predicting the fusion weight of each spectral wavelength at different moments in the future according to the blood oxygen change intensity and historical time sequence information at the current moment; performing multi-resolution time sequence modeling on the multi-spectral time sequence data, respectively extracting short-term, medium-term and long-term time sequence features, and performing weighted fusion on the features of different spectral wavelengths through a fusion weight to obtain fusion feature data; and performing inversion processing on the fused feature data, and calculating oxyhemoglobin saturation distribution of a continuous time sequence in real time to obtain a dynamic blood oxygen image sequence. According to the invention, the blood oxygen detection precision and the depth resolution are improved, and high-precision dynamic imaging of the blood oxygen saturation of the breast tissue is realized.

The invention relates to an eye biomechanical characteristic evaluation method based on a dynamic Scheimpflug imaging technology. The method comprises the following steps: capturing a dynamic deformation video of a cornea through a Scheimpflug camera; performing eye biomechanical characteristic evaluation on the dynamic deformation video through the evaluation model; wherein a training sample of the training evaluation model comprises sample cornea data; the sample cornea data comprises sample video data and sample measurement data which are simultaneously measured in the process of pressing the cornea of the user; sample video data are captured through a high-speed Scheimpflug camera, and sample measurement data are obtained through a cornea ultrasonic biomechanical system. The evaluation model adopted in the method is obtained through training based on sample video data captured by a Scheimpflug camera and sample measurement data obtained through a cornea ultrasonic biomechanical system, and the eye biomechanical characteristics can be evaluated more accurately and reliably.

The invention discloses a method for punching a Bessel laser electrode material for a high-performance battery, and belongs to the field of laser processing of electrode materials. The implementation method comprises the following steps: an ultrasonic spraying instrument is adopted to spray ion conductive and electron non-conductive slurry on the surface of an electrode material, and the slurry can conduct lithium ions when being applied to a battery due to high ion conductivity, so that the excellent performance of the battery is kept; due to low electron conductivity, the material can be coated on an electrode as a protective layer to absorb energy at a Bessel side lobe so as to inhibit side lobe damage of an electrode material, and the electrode material at the side lobe is reserved, so that the capacity of the electrode is not influenced. An adopted Bessel laser processing system comprises a femtosecond laser, a diaphragm, an ultrafast laser reflection group, a mechanical switch, a computer, a dichroscope, a Bessel cutting head, an electrode material, a three-dimensional precision displacement table, a beam splitter, white light and a CCD dynamic imaging unit. According to the invention, the sidelobe damage caused when the Bessel laser punches a hole in the high-absorption electrode material can be obviously inhibited, and the power density of the battery is improved.

The invention provides a lens-free dynamic imaging system and a lens-free dynamic imaging method with distinguishable motion. The system comprises a laser light source, a binary optical mask, an image sensor and image processing equipment, coherent light emitted by the laser light source is coded and modulated by the binary optical mask and then propagated to the image sensor, the image sensor collects multiple frames of diffraction images of a moving sample and transmits the collected images to the image processing device, and the image processing device reconstructs the images through a neural network method. According to the image reconstruction method, physical priori knowledge of a lensless diffraction propagation model is fused into implicit neural representation, and a space-time priori loss and bidirectional memory training method is designed for network training, so that artifacts and blur generated by moving target imaging can be effectively reduced, and a high-resolution and high-quality imaging result can be obtained.

The invention discloses a dynamic image motion correction method based on a graph neural network, and the method comprises the following steps: a), constructing a target region trajectory tracking model based on the graph neural network, and achieving the high-precision motion trajectory modeling; b) analyzing an image displacement mode in real time through a dynamic discrimination algorithm; and c) realizing adaptive image motion correction based on the target area track features. According to the method, the problem of complex motion trail modeling limitation caused by dependence on fixed template matching in a traditional method is innovatively solved, and the problem of spatial-temporal characteristic aliasing caused by unsteady state deformation of nervous tissues is effectively solved. According to the technical scheme, the dependence on a hardware synchronization signal acquisition module is eliminated, the resource configuration requirement of the edge computing equipment is remarkably reduced, and meanwhile, the multi-scale time sequence integration efficiency is improved. According to the method, dynamic imaging reconstruction of subcellular neural activities can be realized, and dynamic change details in a target neuron issuing process can be accurately restored.