1218 results about "Microscopic imaging" patented technology

The invention discloses a lithium ion battery health prediction system and method based on microscopic image multi-scale and region adaptive fusion. The prediction system comprises a data acquisition module, a data preprocessing module, a feature extraction module, a multi-scale feature fusion module, a health state prediction module and a data enhancement and optimization module. The method overcomes the limitation based on traditional electric signal analysis, is suitable for various microscopic imaging technologies, enables the battery health state prediction to be directly evaluated based on the change of the internal microstructure of the electrode, and improves the prediction precision, applicability and physical interpretation. The method can be widely applied to battery life prediction, battery management system optimization and intelligent monitoring systems, and provides reliable technical support for battery safety management, performance optimization and industrial intelligence.

The invention discloses a kit raw material quality detection method based on multi-modal data fusion, and relates to the technical field of automatic quality detection of chemical reagent raw materials. Comprising the following steps: S1, synchronously acquiring spectral data, microscopic image data and chemical detection data of a kit raw material through a spectral sensor, microscopic imaging equipment and a chemical sensor; s2, inputting the multi-modal data into a self-adaptive spatial-temporal feature calibration network, and analyzing local detail features and global time sequence features of the modal data through a multi-scale spatial-temporal feature extraction module; according to the kit raw material quality detection method based on multi-modal data fusion, by constructing a collaborative fusion system of an adaptive spatial-temporal characteristic calibration network and a layered information entropy screening mechanism, the core technical problems of misalignment of multi-modal data dynamic alignment and redundant interference accumulation are solved.

The invention discloses a pathological feature recognition and negative elimination method based on microscopic imaging. The method comprises the following steps: S1, collecting a pathological section image and digitally generating original microscopic image data; s2, preprocessing the original microscopic image; s3, constructing a pathological image recognition network fusing converter coding and a gating dynamic receptive field mechanism, and outputting pathological feature vectors; s4, performing context modeling through an attention guidance and category perception decoder, and outputting an image classification result; s5, constructing a discriminant boundary separation model based on positive and negative sample embedding, and performing negative exclusion judgment; s6, performing confidence coefficient weighted evaluation in combination with the uncertainty and the boundary distance, setting a dynamic threshold value, and screening out low-credibility samples; and S7, coding the classification result and the negative label into structured data, and sending the structured data to a diagnosis auxiliary system. According to the method, multi-scale modeling and a negative screening mechanism are fused, and intelligent recognition and credible diagnosis output of the pathological image are realized.

The invention discloses a system and method for identifying microbial contamination in food and cosmetics based on AI identification, and the method comprises the steps: collecting to-be-detected food or cosmetics as a detection sample, and obtaining the bacterial colony growth state of microorganisms in the detection sample; the method comprises the following steps: acquiring a microbial growth image through microscopic imaging equipment, and preprocessing the microbial growth image to obtain a target area of a microbial colony; constructing an AI recognition model by adopting a convolutional neural network, and obtaining the species and quantity of microorganisms in the detection sample; parameters of the AI recognition model are adjusted in real time according to the types and the number of the microorganisms, a preset microorganism limited standard threshold value is combined, a visual analysis report is generated, the AI recognition model is combined with a Faster R-CNN target detection method, precise classification and number statistics of the types of the microorganisms are achieved, a complete microorganism pollution analysis report is formed, and the method is suitable for popularization and application. And the accuracy and scientificity of the final identification result are improved.

The invention discloses an ultrasonic positioning microscopic imaging method and system based on joint resolution perception and vision Mangbar, and the method specifically comprises the steps: obtaining and preprocessing a plurality of modal angiography data, generating a microbubble motion sequence in combination with blood flow velocity field simulation data, and constructing a data set; inputting the data set into a mixed hierarchical feature pyramid fusion network, carrying out probability density estimation on a microbubble position based on a real-time multi-scale density field estimation algorithm, and introducing a resolution perception algorithm to enhance microbubble features to obtain a plurality of short-track images; performing dynamic feature decoupling and time-frequency analysis on the short-track image, extracting and encoding velocity component features and spatial-temporal context information of a vascular structure, and constructing a double-branch processing network based on visual Mangbar to obtain images respectively reflecting forward motion contribution and backward motion contribution, and finally generating a high-resolution blood vessel image through image fusion. The method has an important value for improving the efficiency and precision in microvascular imaging.

The invention provides a microscopic automatic focusing method and system based on image gray histogram characteristics, and the method comprises the steps: collecting an image sequence under different focal lengths, carrying out the fuzzy processing, extracting a gray histogram of each frame of image, and calculating the peak position and full width at half maximum of the histogram as the evaluation characteristics of the image definition; calculating the variance of each feature and automatically allocating a weight according to the relative response degree; and finally, comprehensively evaluating the image definition through a weighted definition scoring function, and selecting the focal length corresponding to the image with the optimal score as the optimal focusing position. The method is based on the global features of the gray histogram, is high in anti-noise capability, is adaptive to different imaging scenes through weight adaptive adjustment, is low in calculation complexity, supports real-time focusing, is especially suitable for high-noise fluorescence microscopic imaging scenes, is high in system portability, is low in operation threshold, and effectively improves the accuracy and stability of microscopic automatic focusing.

The present invention relates to an in-situ micro-nano impact indentation testing instrument, falling within the technical field of material micromechanical testing. The instrument comprises a nitrogen generation module, an environmental chamber, a high / low temperature loading module, an “optical-infrared” in-situ monitoring module, an “electromagnetic-piezoelectric” coupling impact module, etc. After the nitrogen is introduced into the environmental chamber and the test area is determined by microscopic imaging, the “electromagnetic-piezoelectric” coupling impact module can drive an indenter to indent a specimen. An acoustic emission sensor embedded in the high / low temperature loading module can monitor the surface crack propagation of the specimen. The “optical-infrared” in-situ monitoring module can perform real-time high-speed optical imaging and infrared imaging on the impact indentation process. The present invention can perform micro-nano impact indentation testing on the material at high or low temperatures.

The invention discloses a rapid polarized light acoustic imaging device which comprises an excitation source, a light beam shaping module, a rapid polarization modulation module, an energy feedback module, a rapid scanning module and an acquisition processing module. And receiving photoacoustic signals corresponding to the exciting light in four different polarization states for calculation, and extracting anisotropy information of the sample. Compared with multiple times of scanning a sample or modulating the polarization state by rotating a half-wave plate, the problems of dislocation of multiple times of polarization acquisition points and false positive calculation are avoided, meanwhile, the time interval generated by continuously modulating the laser pulses is short, and energy fluctuation among the four pulses and errors caused by fluctuation are reduced. According to the invention, the limitation of the precision and speed of polarized light acoustic microscopic imaging is overcome, the microstructure characteristics in the target sample can be rapidly, accurately and highly sensitively detected, and the method has great application prospects in biomedical imaging and high-precision material detection.

The utility model discloses a micro-fluidic chip imaging positioning device, and belongs to the technical field of micro-fluidic chip positioning. According to the technical scheme, the system comprises a microcosmic illumination module, a microcosmic imaging module, a chip moving and macroscopic illumination module, a macroscopic imaging module and an optical path multiplexing module; the microscopic illumination module comprises a fly's-eye lens assembly, the fly's-eye lens assembly comprises two fly's-eye lenses, the optical axes of the two fly's-eye lenses are parallel to each other, and the focus of each lens array element in one fly's-eye lens coincides with the center of the lens array element at the corresponding position of the other fly's-eye lens. The micro-fluidic chip clamp is applied to the aspect of micro-fluidic chip imaging positioning, overcomes the defects caused by an existing micro-fluidic chip clamp, solves the technical problems of universality, automation and high precision of micro-fluidic chip positioning, and has the characteristics of rapidness, automation, high precision and universality.

The invention provides a liquid cooling impurity monitoring method and system, and the method comprises the steps: obtaining an existing liquid cooling impurity attribute set through spectral analysis and microscopic imaging, deploying an impurity detection module in a liquid cooling channel, and obtaining a resonance spectrogram of the liquid cooling channel and environment parameters of the liquid cooling channel; performing cooperative feature extraction on the liquid-cooling cold channel resonance spectrogram and the liquid-cooling cold channel environment data, generating a liquid-cooling ecological vector, obtaining a historical liquid-cooling ecological vector corresponding to the historical data of the liquid-cooling cold channel, constructing a liquid-cooling impurity monitoring model, performing model pre-training to generate a liquid-cooling impurity library, inputting the liquid-cooling ecological vector to the liquid-cooling impurity monitoring model, and obtaining a liquid-cooling impurity monitoring result. Performing liquid cooling impurity matching on the liquid cooling ecological vector and a liquid cooling impurity library based on a cuckoo algorithm to obtain a liquid cooling impurity combination vector, obtaining a pre-trained impurity purification decision model, inputting the liquid cooling impurity combination vector into the impurity purification decision model, and generating an optimal liquid cooling impurity purification strategy, and a user can perform impurity removal on the liquid cooling system according to the optimal liquid cooling purification strategy.

The invention discloses an all-optical three-dimensional scanning confocal fluorescent microscopic imaging device and an implementation method thereof. According to the invention, through a deep learning driven adaptive regulation and control method, CNN is adopted to process spatial distribution data and dynamically regulate and control a phase hologram and the light intensity and phase compensation of a laser, so that the focal point of exciting light is subjected to aberration-free axial displacement, a bidirectional parallel optical scanning track of a two-dimensional scanning system is optimized, and all-optical three-dimensional scanning is realized; lSTM and TCN are combined to obtain a time sequence dependency relationship, a laser light source, an adjustable diaphragm, an electric focus-adjustable lens and a photoelectric detector are integrally controlled, efficient synchronization and automatic operation and high-speed axial focusing adjustment are realized, errors and time sequence mismatch are eliminated, optical characteristics of different samples and environmental interference are automatically adapted, and high-quality imaging is kept. The robustness and the applicable scene range of the system are improved; the method is used for model biological embryo real-time tracking, intracellular signal molecule dynamic visualization, cell membrane protein migration and aggregation observation and intracellular organelle interaction tracking.

The invention discloses a machine vision-based enzyme freeze-dried powder quality detection method, and belongs to the technical field of machine vision, and the method specifically comprises the following steps: in a closed optical cavity, synchronously collecting a multispectral polarization sequence of a sample through annular multispectral polarization illumination and microscopic imaging; performing short-time airflow pulse disturbance and continuous imaging on the sample, and constructing a polarization time sequence texture map by means of inter-frame polarization retention difference; according to Stokes parameters, calculating a deskewness graph and a specular reflection component graph, and carrying out joint normalization on the deskewness graph and the specular reflection component graph and the polarization time sequence texture graph to form a multi-channel feature stack; constructing a double-branch encoder, and performing comparative learning on the feature stack to generate an impurity prototype dictionary; using the dictionary to match the feature stack pixel by pixel, and tracking and inhibiting powder agglomeration false detection through a topological constraint connected domain; and recovering a height map by using an oblique incidence fringe phase shift method, performing pixel-level fusion with the candidate image, retaining targets with height abrupt change and consistent polarization characteristics, and outputting an impurity mask.

The invention discloses a multi-mode nonlinear optical microscopic imaging device and system, and the device achieves the two-photon and three-photon excitation imaging of a sample through an integrated light source module, a dispersion compensation module, a scanning light path module, a fluorescence obtaining module, a synchronous control module and a signal processing module. Fluorescence intensity imaging and fluorescence lifetime imaging can be supported at the same time; wherein the scanning light path module is driven by the random mask to realize sparse scanning, so that scanning point locations are effectively reduced, and the exciting light dosage is reduced; the synchronous control module performs time window screening and processing on the fluorescence signal based on the pulse synchronous signal and the position code, so that the signal acquisition efficiency is improved; the signal processing module adopts an FPGA (Field Programmable Gate Array) to carry out real-time image reconstruction and life calculation, so that the imaging speed and quality are ensured; and the master control PC can also perform high-fidelity reconstruction on the sparse sampling image through a deep learning model, so that high-speed, low-light-dose and high-resolution nonlinear fluorescence intensity and life imaging is realized.

The invention relates to the technical field of peanut plant growth state intelligent monitoring, in particular to a peanut plant growth state intelligent monitoring method and system and electronic equipment. The method comprises the following steps: respectively acquiring visible light and near-infrared images, laser radar point cloud data, near-infrared spectra and microscopic images of peanut plants by using a multispectral camera, a laser radar, a near-infrared camera and a microscopic imager carried by a field robot; carrying out de-atomization and color correction on the image by adopting a self-adaptive illumination compensation algorithm; a laser radar point cloud is fused to construct a plant three-dimensional point cloud model; segmenting an overlapped leaf area based on a graph neural network, and extracting morphological characteristics of a single plant; the nitrogen content and the moisture content of the leaves are analyzed through near infrared spectroscopy, and early scabs are recognized in combination with microscopic imaging; and inputting the morphological characteristics and the physiological parameters into a growth state evaluation model, and outputting growth indexes and disease and pest early warning. According to the invention, automatic and refined monitoring and growth state comprehensive evaluation of peanut plant morphology and physiological parameters are realized.

The invention discloses a time domain compression femtosecond holographic microscopy reconstruction method based on spatial domain and frequency domain joint learning. Constructing a time domain compression femtosecond holographic microscopy system for carrying out multi-moment information time domain compression coding on a measured sample and generating a single-frame snapshot measurement graph; constructing a joint spatial domain-frequency domain learning end-to-end reconstruction model comprising a spatial frequency domain interaction module; training the model by taking a dynamic micro-nano structure hologram sequence generated by simulation as a training set until the joint spatial frequency domain loss function is converged; inputting a single-frame snapshot measurement image generated by a microscopic system into the model for processing to obtain a reconstructed hologram frame sequence; and performing frequency spectrum extraction and phase unwrapping processing to generate a dynamic three-dimensional scene video so as to realize time domain compression femtosecond holographic microscopy reconstruction. According to the invention, both the hardware imaging rate and the algorithm reconstruction precision are considered, and high-speed, high-resolution and low-cost four-dimensional holographic microscopic imaging can be realized under the conditions of extremely low data bandwidth and a common CMOS camera.

A brain organoid containing optic vesicles generated based on H9 induction and an eye-brain fusion culture method thereof are provided. H9 embryonic stem cell induction is used to generate a brain organoid containing optic vesicles with primitive visual field. Based on an established optic vesicle brain organoid culture system, microscopic imaging is combined with specific marker antibodies related to early retinal development and photoreceptor cell maturation to structurally and functionally identify the brain organoid containing optic vesicles.

The invention provides a crystal quality detection method and system, and the method comprises the steps: positioning a wafer sample to a position below an objective lens through a sample conveying platform, and enabling the focus of the objective lens to be at the center of the wafer sample; when the objective lens linearly moves within the radius range relative to the wafer sample, carrying out microscopic imaging to obtain an image; when the intensity fluctuation of the image is greater than a set threshold value, performing Raman imaging to obtain a three-dimensional hyperspectrum; processing each Raman spectrum in the three-dimensional hyperspectrum to obtain a spectrum peak curve, a peak width curve and a peak position curve; when a first-order derivative tends to zero after the spectral peak curve, the peak width curve and the peak position curve are fitted, longitudinal scanning is ended; converting the spectrum peak intensity, the peak width and the peak position generated by the three-dimensional hyperspectrum into characteristic information, and calculating a wafer quality coefficient; determining the quality of the wafer sample according to the wafer quality coefficient; the measurement of different depths can be carried out on the crystal (including a wafer sample); and nondestructive, rapid and accurate quality analysis at normal temperature and normal pressure is realized.

The invention relates to the technical field of traditional Chinese medicine component detection and discloses a rapid detection method for traditional Chinese medicine compound components. The method comprises the following steps: performing Raman spectrum scanning on a traditional Chinese medicine compound sample to generate component spectrum distribution information, and performing fitting by adopting a partial least square algorithm to determine a local component anomaly risk area; meanwhile, atomic force microscopic imaging is carried out, a microstructure distribution map is generated, and a potential component mismatch feature region is marked through texture feature extraction and pattern recognition. And generating a detection optimization adjustment region based on the space coordinates of the region, analyzing the gradient change in the molecular arrangement direction of the region to evaluate the component distribution offset state, and analyzing the interface bonding state of the active component and the auxiliary material to evaluate the component interaction coupling strength. And finally, on the basis of an evaluation result, whether the real-time parameter calibration is started in the detection optimization adjustment area is judged through a dual-channel decision-making mechanism. According to the method, correlation analysis of component spectrums and microtopographies is realized, and the accuracy and pertinence of traditional Chinese medicine compound component detection are improved.

The utility model discloses a shooting device for fiber component analysis. The shooting device comprises a supporting base, an X-axis and Y-axis moving table, a Z-axis driving assembly, a carrying plate connected to the X-axis and Y-axis moving table and a microscopic imaging assembly connected to the Z-axis driving assembly and located above the carrying plate. A slide is arranged on the loading plate, and a first light source capable of irradiating to the slide is arranged in the microscopic imaging assembly. Wherein a first groove is formed in the upper surface of the carrying plate, a through light guide hole is formed in the first groove, and the slide covers the first groove; the lower surface of the carrying plate is connected with the XY-axis moving table through a connecting block, a second groove is formed in the position, corresponding to the light guide hole, of the connecting block, and a second light source is arranged in the second groove; a double-sided mirror is assembled at the bottom of the first groove and covers the light guide hole. According to the shooting device, the definition of the fiber sample image can be improved, the stereoscopic impression of the fiber sample image is also improved, and the loss of detail structures in the fiber sample image is also reduced.

The invention discloses a cell occlusion relation estimation method and system based on a pixel-level focus evaluation curve. The method comprises the following steps: acquiring a multi-focal plane cell image sequence of the same view at different focusing positions; performing cell instance segmentation on the image sequence to obtain a two-dimensional cell instance mask; calculating the definition value of each pixel in the mask on each focal plane, and constructing a pixel-level focus response curve; determining a focus depth interval of each pixel according to the peak position of the focus response curve and a preset relative threshold value; and comparing focus depth intervals of different cell pixels in the cell overlapping region, and judging the up-and-down shielding relationship between the cells. According to the method, automatic analysis from the original microscopic data to the cell upper and lower layer relation is realized through pixel-level depth modeling and interval comparison, the spatial resolution and credibility of occlusion relation judgment are improved, the compatibility with a conventional microscopic imaging process is high, and deployment and popularization are easy.

The invention relates to an aspheric wavefront detection device and method based on multi-direction orthogonal lateral shear interference. The device is composed of a laser, a linear polarizer, a light splitting element, a reflector array, a two-dimensional orthogonal grating array, a light splitting system, a polarization phase shifting element and a CMOS camera. The method comprises the following steps: firstly, an antisymmetric continuation method is included, secondly, dynamic phase recovery and distortion compensation are adopted, Zernike polynomial local fitting is introduced, and self-adaptive compensation is carried out on high-order aberration (such as coma aberration and trefoil aberration) of an aspheric surface edge region. According to the method, the multi-direction orthogonal lateral shearing interferogram can be obtained through single measurement, and a high-precision, high-efficiency and high-robustness solution is provided for precise detection of the aspheric optical element through cooperation of an algorithm and hardware. The method is suitable for high-precision phase recovery in the fields of optical detection, microscopic imaging and the like, and is suitable for high-precision dynamic detection of complex optical elements such as off-axis aspheric surfaces, free-form surfaces and the like.

A deep learning-based super-resolution fluorescence lifetime imaging microscopy (SR-FLIM) method includes the steps of: S1, performing fluorescence microscopic imaging on a sample to obtain confocal intensity images and stimulated emission depletion (STED) intensity images at a same location; S2, co-registering the acquired confocal and STED intensity images; S3, pairing the co-registered confocal and STED intensity images as input (Input) and ground truth (GT) to assemble a dataset; S4, partitioning the dataset into training and validation sets following a predefined ratio; and S5, constructing a network, and selecting hyperparameters and an optimizer. This method may achieve SR-FLIM within a conventional confocal FLIM system, surpassing spatial resolution limitations of FLIM, breaking through resolution barriers of conventional optical microscopy, while preserving normal fluorescence lifetime characteristics of fluorescent probes.

The invention provides a phytoplankton chromatography sequence identification method and a phytoplankton chromatography sequence model building method, and belongs to the technical field of image enhancement identification. The method comprises the following steps: firstly, acquiring microscopic chromatography sequence data of phytoplankton, performing view field extraction and serialization recombination, and constructing a three-dimensional data set; then, constructing a three-dimensional recognition model containing physical perception and a sequence aggregation mechanism, extracting single-frame semantic features by the model by adopting a parameter-shared twin network, and introducing a physical definition prior module to calculate a space-frequency domain quality score of a slice; secondly, designing a deep perception sequence aggregation module, and adaptively aggregating key features of a high signal-to-noise ratio by taking definition scores as gating signals and combining spatial context information between slices; and finally, training and optimizing the model based on the image-level weak supervision label to obtain an optimal model. According to the method, the problems of information truncation and out-of-focus noise interference caused by extremely shallow depth of field of high-power microscopic imaging are solved, and full-depth-of-field stereoscopic perception can be realized under the condition that frame-by-frame fine labeling is not needed.

According to the quartz fiber glass bead defect detection method and system based on microscopic imaging, since the glass beads are of a three-dimensional structure, the internal defects of the glass beads can be distributed at different heights, and the defects of the glass beads can be detected by carrying out region division on the whole disc of quartz fiber glass beads, determining different focal planes and carrying out microscopic scanning on different regions on each focal plane in sequence. Acquiring a plurality of focal plane images corresponding to different focal planes of the whole disc of quartz fiber glass beads, and splicing the same focal plane images corresponding to different areas to ensure that all potential defects of the quartz fiber glass beads are imaged; and then defects in the image are identified, positioned and classified by using deep learning, so that manual subjective errors are avoided, and the detection efficiency and accuracy are remarkably improved.

The invention discloses a depth physical parameter integrated network and method for lensless microscopic imaging, and the method comprises the steps: firstly building a physical forward model based on fractional Fourier transform, and quantizing a propagation distance into an adjustable fractional order parameter; secondly, constructing a multi-stage progressive network framework, wherein each stage comprises a physical model fidelity module and an image regularization updating module; a self-adaptive near-end network and a self-adaptive compression-excitation network are innovatively embedded in a regularization module, and real-time self-adaption of network parameters to imaging conditions is achieved; finally, end-to-end single-frame reconstruction is realized, complex amplitude information is directly recovered from a single intensity observation image, and retraining for different imaging parameters is not needed. According to the method, the reconstruction physical reliability, the noise robustness and the cross-scene generalization ability are remarkably improved, meanwhile, the lightweight real-time deployment requirement is met, and the method is suitable for lens-free microscopic imaging scenes such as biological living body observation and portable pathological diagnosis.

The invention relates to the technical field of electron microscopic imaging, in particular to a correction method and device for geometric distortion in scanning electron microscope measurement, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring a scanning electron microscope image of a standard sample; calculating an orthogonal included angle parameter and a length-width ratio parameter of the scanning electron microscope image; iteratively adjusting the angle correction parameter and the gain correction parameter under a fixed scanning rotation angle until a preset local calibration constraint condition is met; verifying the orthogonal included angle parameter and the length-width ratio parameter under each scanning rotation angle; and when the orthogonality included angle parameter and the length-width ratio parameter do not meet a preset global stability constraint condition, adjusting a relative gain coefficient, and re-executing calibration verification under each scanning rotation angle until a full-angle correction requirement is met. By adopting the method, high precision and stability of imaging correction can be kept in a full-angle range.

The invention provides a linear optical tweezer astigmatism field regulation and control method based on an optical transmission matrix. The design method comprises the following steps: establishing a 4 * 4 optical matrix for generating a linear optical tweezer astigmatism field and a transmission equation; analyzing the regulation and control rule of the parameters of the optical element on the astigmatism characteristics; collecting a fluorescence slice image of the initial astigmatic light field through an axial tomography scanning method; reconstructing the spatial distribution of the initial image light field; judging whether the light field accords with a preset astigmatism characteristic or not, if not, determining the astigmatism characteristic to be optimized, and adjusting element parameters according to a regulation and control rule; and acquiring the adjusted image of the astigmatism light field again, reconstructing the image, and continuously performing judgment iteration until the image accords with the preset astigmatism characteristic, thereby realizing regulation and control of the astigmatism light field. Matrix optics is used in optimization of a linear optical tweezer image dispersion light field optical system, the limitation of traditional trial and error design is avoided, the method can be applied to the fields of light control technology, light sheet fluorescence microscopic imaging and the like, and the performance of the optical system is improved.

The invention relates to the technical field of microscopic imaging, and discloses a microscopic imaging system with resolution exceeding light spot size limitation. The system comprises a laser unit; the measured target unit is arranged in the reference arm light path; the tested target unit comprises a tested sample and a divergence angle adjusting element; the divergence angle adjusting element is used for adjusting the divergence angle of the reference light source passing through the tested sample; the spatial radio frequency marking unit comprises a reference arm light path and a measuring arm light path and is used for performing optical heterodyne interference processing on the laser beams according to the radio frequency driving signals of various frequencies to form a reference light source and a light spot array respectively; the reference light source enters the measured target unit and then is combined with the light spot array to generate a combined light beam; the image signal receiving unit is used for collecting the combined light beam and then generating light data; and the data processing unit is used for receiving the optical data and carrying out image inversion processing on the optical data to generate imaging data of the tested sample. According to the scheme, the resolution limit of the system is improved.

The invention relates to an ultrafast electron microscopic imaging device and an imaging method thereof, in particular to a multi-mode multi-dimensional ultrafast electron microscopic imaging device and an imaging method thereof, and solves the problem that the existing electron microscopic imaging technical scheme is difficult to simultaneously meet the measurement requirements of high time-space-energy-momentum resolution and material diversification. The device comprises a sample cabin, an electron gun, a laser generation module, a secondary electron detector, an STEM detector, an in-cabin plug-in detection assembly, a plug-in camera and an energy filtering electron microscope assembly which are arranged below the sample cabin, and a vacuum system, a hollow sample table is arranged in the sample cabin, and at least two ultrafast pumping laser introduction windows are arranged on the cabin wall of the sample cabin; an ultrafast detection laser introduction window is arranged on the electronic gun shell; the secondary electron detector is arranged obliquely above the sample table; the STEM detector is arranged on the side cabin wall of the sample cabin and is positioned below the sample table; and the in-cabin plug-in detection assembly is arranged below the STEM detector.

The invention discloses a surface plasmon resonance imaging system for expanding a dynamic range through a multi-mode optical path, and relates to the technical field of microscopic imaging, a laser outputs a monochromatic and linearly polarized continuous wave beam, a first dynamic beam deflection device performs dynamic angle deflection adjustment on the continuous wave beam, a prism is used for providing a total reflection interface, and a second dynamic beam deflection device is used for performing dynamic angle deflection adjustment on the continuous wave beam. Exciting an evanescent wave; a surface plasma resonance effect is generated through interaction of the SPR detection chip and evanescent waves, and sample characteristics are detected; the flow cell provides a sample flow channel, so that a sample is in full contact with the surface of the SPR detection chip; the second dynamic light beam deflection device cooperatively adjusts the propagation direction of reflected light on the surface of the SPR detection chip, and the imaging detection device captures a high-spatial-resolution SPR image formed by the reflected light and transmits the image to the upper computer in real time for processing and analysis. According to the invention, the change of the resonance angle can be tracked in real time, and high-sensitivity and high-resolution monitoring of the dynamic process of biomolecule interaction is realized.