1943 results about "Microscopic imaging" patented technology

The invention provides a fine-grained phytoplankton microscopic image enhancement classification method and a model building method thereof, and belongs to the technical field of underwater image enhancement classification, and the method comprises the following steps: firstly, collecting phytoplankton samples from a plurality of sea areas, and obtaining image data through a microscopic imaging technology; screening, preprocessing and data enhancement are carried out on the images, and a high-quality training set and a high-quality test set are constructed; according to the method, a double-branch deep learning model HySwinFormer is developed, the double-branch deep learning model HySwinFormer comprises a multi-scale context capture branch based on hierarchical vision TransFormer and a local detail enhancement branch designed by adopting a parallel convolution kernel, and joint expression of features is realized through a designed weighted fusion strategy; and training and optimizing the model to obtain a final model. Experimental results show that the classification performance of the HySwinFormer model on multiple data sets is superior to that of an existing mainstream deep learning model, and the classification effect is remarkably improved.

The invention discloses an immune cell state analysis method and system, and relates to the technical field of image processing. The method comprises the following steps: acquiring a motion image sequence of immune cells by using microscopic imaging equipment; performing edge detection on the motion image sequence, and marking contour pixel data labels of immune cells; performing trajectory tracking on the immune cells under a time sequence, and outputting motion trajectory pixel data; inputting the motion trail pixel data into a dynamic change rate recognition model, obtaining a first-order change rate, a second-order change rate and a change rate mean value, and generating a dynamic change feature embedding vector; inputting into an immune cell state prediction large model for state prediction to obtain an active state prediction index, and performing active labeling on the immune cells according to the active state prediction index. The technical problems of inaccurate analysis and incomplete prediction of the immune cell activity state in the prior art are solved, and the technical effect of improving the accuracy and comprehensiveness of immune cell activity state prediction is achieved.

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 method for analyzing mineral trace elements by utilizing a micro-Raman spectrum, which comprises the following steps of: fixing a mineral sample on a rotary sample table of a micro-Raman spectrometer, selecting an area to be detected by combining a micro-imaging technology, collecting a Raman background signal under the pre-illumination of low-power laser, and analyzing the trace elements in the area to be detected according to the Raman background signal. The laser focal length, the spot size and the incident angle are adjusted accordingly, the laser is focused on a target area, under the calibrated laser condition, through a multi-angle excitation micro-area, after an algorithm is applied to optimize signals, reduce impurity interference and integrate multi-angle Raman signals, the characteristic spectrum information of the microelements can be extracted, and the microelements can be accurately detected. High-resolution analysis of sample components is realized, and a three-dimensional distribution model of microelements in a microcell is constructed in combination with microscopic imaging data. The method has the characteristics of non-destructiveness, high resolution and rapid detection, and is suitable for mineral component analysis in the fields of geology, material science and the like.

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 an out-of-focus identification and restoration method for a microscopic system. The method comprises the following steps: 1, preparing a microscopic out-of-focus image data set and carrying out degradation modeling; 2, constructing an out-of-focus parameter prediction network based on multi-label parameter reasoning and microscopic system priori knowledge and an image restoration network based on a generative adversarial network, performing independent training, and then performing merging training through a circulation system formed by mutual connection based on predicted point spread function convolution and deconvolution operation; 3, model fine tuning based on a specific system and a new data set; and 4, performing model testing, performing large-view image sliding window detection and collage fusion, and explicitly outputting defocus parameters of the system during image shooting. The method can be applied to out-of-focus fuzzy recognition and restoration of microscopic imaging of various systems, and is beneficial to the accuracy of functions such as particle counting and particle size statistics of the systems, thereby further promoting the application of the full-depth-of-view microscopic system in biomedical detection.

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 a two-photon imaging system and method based on vector light field regulation and control, and belongs to the technical field of microscopic imaging. The two-photon imaging system comprises a laser beam expanding module, a vector light field modulation module, a light beam scanning module and an imaging module which are arranged in sequence; the laser beam expanding module comprises a laser, a first half-wave plate, a first polarization beam splitter, a first lens and a second lens which are arranged in sequence; a laser beam generated by the laser passes through the first half-wave plate and the first polarization beam splitter for laser power adjustment, and then passes through the first lens and the second lens for beam diameter adjustment. According to the two-photon imaging system based on vector light field regulation and control, the focal field is regulated and controlled by regulating and controlling the amplitude, the phase and the polarization of the vector light field, and the imaging resolution or the volume imaging speed of the system can be improved.

The invention provides a deep frequency shift super-resolution microscopic imaging chip and a deep frequency shift super-resolution microscopic imaging system for omni-directional ultra-high wave vector illumination, and belongs to the technical field of optical super-resolution microscopy. The chip comprises a substrate plated with an adhesion layer and multiple layers of films on the adhesion layer, and sub-wavelength-scale circular ring gratings are engraved on the substrate and the adhesion layer; exciting light enters the circular ring grating through the substrate, and evanescent waves of ultrahigh wave vectors in all directions are generated through the circular ring grating. The evanescent waves are coupled to the upper surface through the multi-layer thin film to excite surface plasmons with ultrahigh transverse wave vectors in the corresponding direction, and the surface plasmons are used for carrying out all-directional ultrahigh wave vector illumination and deep frequency shift super-resolution microscopic imaging on a sample placed on the upper surface of the multi-layer thin film. The system comprises an excitation module, a deep frequency shift super-resolution microscopic imaging chip for omni-directional ultra-high wave vector illumination, an imaging module and an excitation control and image acquisition module. According to the invention, omni-directional ultra-high resolution imaging of a complex fine structure sample can be realized, so that application requirements in related fields can be met.

The invention provides a microscope objective optimization method and electronic equipment, and relates to the technical field of microscopic imaging. The method comprises the following steps: acquiring initial design parameters of a microscope objective; respectively calculating the imaging quality of the microscope objective and the total ghost image intensity of the microscope objective based on the Kohler illumination system according to the initial design parameters of the microscope objective; according to the imaging quality and the total ghost image intensity of the microscope objective, a preset lens evaluation function is constructed, and the target design parameters of each target lens when the imaging quality meets the first preset requirement and the total ghost image intensity meets the second preset requirement are iteratively calculated, so that compared with an existing mode of determining through multiple tests, the optimization efficiency can be improved; and through introduction of the preset lens evaluation function, comprehensive evaluation can be realized, and the optimization effect is ensured.

The invention discloses an enhanced photoacoustic microscopic imaging method based on physical degradation learning, which comprises the following steps of: on the basis of a photoacoustic imaging principle, degrading an image of a real photoacoustic microscopic image to the ground into a low-quality photoacoustic microscopic image through physical model degradation learning with variability. A high-quality photoacoustic microscopic imaging image is used as a target of supervised training, deep learning is used as a basis, the high-quality photoacoustic microscopic imaging image is input into a self-designed convolutional neural network with an attention mechanism for network training, the quality of a reconstructed image is evaluated until the reconstruction quality reaches the standard, and otherwise, the above steps are executed again. And inputting a real low-quality photoacoustic image into the trained neural network to obtain a reconstructed high-resolution photoacoustic image. According to the method, the resolution ratio and the contrast ratio of the deep photoacoustic microscopic image can be effectively improved, meanwhile, the photoacoustic microscopic image can be denoised, the dependence on the data set can be effectively reduced due to the fact that the data set is obtained through the physical model, and the reconstruction effect under different environments can be improved.

The invention discloses a super-resolution laser direct writing and imaging method and device based on a two-photon and stimulated radiation loss effect, and the method comprises the steps: adding a photoinitiator into photoresist, and achieving two-photon STED high-precision micro-nano structure laser direct writing through the two-photon absorption effect and the stimulated radiation loss effect of the photoinitiator; and meanwhile, the two-photon STED super-resolution microscopic imaging is carried out on the inscribed structure by utilizing the fluorescence luminescence characteristic, the two-photon absorption characteristic and the stimulated depletion (STED) characteristic of the two-photon STED super-resolution microscopic imaging of the two-photon STED super-resolution microscopic imaging of the inscribed structure. According to the invention, the two-photon and STED effects of the initiator are utilized, so that super-resolution inscribing and imaging can be realized at the same time, and the components of the photoresist are simplified. Besides, the same exciting light and loss light can be respectively utilized during inscribing and imaging, so that high-precision inscribing and super-resolution imaging can be simultaneously realized in one optical system, and the complexity of the system can be effectively simplified by multiplexing the optical paths of the exciting light and the loss light for inscribing and imaging.

The invention discloses a differential polarization confocal microscopic imaging system and method of a multi-focus channel, and relates to the technical field of optical microscopy. Comprising a light source emission module, a dichroscope (6), a two-dimensional galvanometer module, a scanning module, a sample acquisition module, a detection module and an imaging terminal (17). According to the invention, differential polarization imaging and a multi-focus channel confocal technology are combined, so that the imaging quality of the confocal microscopic system is further improved on the premise of ensuring the confocal imaging speed; meanwhile, differential imaging is introduced, so that the imaging contrast ratio can be greatly improved, and the confocal microscopic system can obtain a better imaging effect in the application field of thick tissue microscopic imaging.

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 floating algae flow type microimaging sheath fluid focusing micro-fluidic chip device and system, and belongs to the technical field of micro-fluidic control. According to the device, the three-channel sheath fluid flow chip design is adopted, a large-channel inlet structure is combined to avoid algae cell blockage, and a sheath flow focusing technology is utilized to compress a sample flow to the center of a channel and limit the sample flow within the field depth range of a microscope objective to obtain a clear image. The arc-shaped structure of the sample channel reduces laminar flow disturbance, and a four-side transparent observation area supports panoramic depth imaging. The system comprises a microscopic imaging module, a fluid driving module and a sheath liquid flow chip module, a flow rate ratio of sheath liquid flow to sample flow is accurately controlled through a dual-channel liquid flow driving injection pump, and stable imaging within the depth of field of an objective lens is realized by combining a manual focusing platform. Experimental verification shows that the system can effectively compress sample flow to 21m nuclear flow diameter, imaging of floating algae such as chlorella, scenedesmus quadricauda and the like is clear, and the contradiction between flux and imaging quality of a traditional micro-fluidic chip is solved.

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 invention relates to the field of microscopic imaging, and discloses a miniature microscopic system optical precision improvement and image calibration error optimization method, which comprises the following steps: establishing an optical distortion model and a mechanical disturbance model, and respectively describing a static error and a dynamic error; constructing a joint dynamic error model, and uniformly expressing a coupling relationship between optical distortion and mechanical disturbance; decoupling and separating the joint error through a space-time separation method to obtain a static error component and a dynamic error component; performing iterative optimization on the optical distortion parameter and the mechanical disturbance parameter based on an optimization algorithm to minimize a system calibration error; a real-time sensor is utilized to obtain dynamic disturbance data, and real-time dynamic compensation is performed on an image calibration error in combination with an optimization result, so that a high-precision calibration result is obtained. The method can effectively improve the calibration precision of the microscopic system in a dynamic environment, remarkably improves the imaging quality of the system, and has a wide application prospect.

The invention relates to a super-resolution fluorescence lifetime microscopic imaging method and system based on pixel relocation. The method comprises the following steps: S1, carrying out multi-focus parallel scanning on a sample by using an acousto-optic deflector; s2, synchronously collecting a fluorescence intensity image and fluorescence lifetime data through an area array detector and a point detector; and S3, carrying out super-resolution reconstruction on the fluorescence intensity image based on a pixel relocation algorithm, and mapping fluorescence lifetime data to a reconstructed image to generate a super-resolution fluorescence lifetime microscopic image. According to the invention, an AOD is adopted to replace a scanning galvanometer in a traditional TCSPC-FLIM imaging system as a light beam scanner; addressing scanning of AOD, image acquisition of EMCCD and life data acquisition of PMT and TCSPC cards are closely linked by combining well-designed multiple paths of synchronizing signals, and fluorescence life data are distributed to each dot matrix through a later data processing method, so that organic combination of super-resolution and FLIM can be realized without complicated equipment, and SR-FLIM images of samples are obtained.

The invention relates to the technical field of optics, and discloses a microscopic imaging system. The system comprises a light source module used for generating a laser beam; the radio frequency drive generation module is used for generating radio frequency drive signals with various frequencies; the heterodyne interference light path module is used for respectively performing optical heterodyne interference processing on the laser beams according to the radio frequency driving signals with various frequencies, respectively forming a light spot array and a reference light source and then combining the light spots and the reference light source, and generating combined light beams to be incident on a sample to be detected; wherein different light spots in the light spot array correspond to different light frequencies; the spectral data receiving module is used for collecting light data generated after the combined light beam is reflected by the to-be-detected sample; wherein the to-be-detected sample is subjected to fluorescence labeling in advance; and the image inversion module is used for receiving the optical data and carrying out image inversion processing on the optical data to generate imaging data of the sample to be detected. According to the scheme, the microscopic imaging response speed is increased, the efficiency is high, and the accuracy is good.

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.

According to the multi-beam confocal mask defect detection system and method based on the fiber laser array, a multi-point scanning light field is obtained on the basis of the fiber laser array to serve as an illumination light source, the scanning light field can be regulated and controlled on the basis of arrangement of the laser array, and through the combined action of a motion table and a multi-face rotating mirror, the defect of the multi-beam confocal mask is detected. Rapid and accurate mask two-dimensional scanning imaging is achieved, and high-resolution detection of mask defects is achieved on the basis of a confocal microscopic imaging method in cooperation with a high-power microscope objective and a photomultiplier. Compared with a traditional point scanning type mask defect detection system, the system has the advantages that the detection resolution is high, the scanning speed is high, a multi-point scanning light field can be freely regulated and controlled, and no extra diffraction optical element needs to be introduced.

The invention relates to the technical field of electron microscopic imaging, in particular to an electron microscopic imaging automatic focusing method and device, computer equipment, a storage medium and a computer program product. The method comprises: acquiring an initial image; taking the initial image as a starting point, searching a to-be-detected object by using a current positioning model, and determining a focusing search starting point; the focusing search starting point is adjusted based on preset search parameters, a detection image corresponding to the adjusted imaging parameters is obtained, and the search step length and the search direction corresponding to the search parameters are dynamically adjusted according to the definition evaluation result of the historical detection image; in response to the condition that the adjusted detection image does not meet the definition evaluation index, updating the search parameter; and adjusting the imaging parameter based on the updated search parameter until the detection image meets a preset focusing requirement. By adopting the method, the efficiency of automatic focusing can be improved, and the accuracy of focus point positioning and the robustness of algorithm implementation can be improved.

The invention discloses a tunable excited Raman spectrum microimaging method and device, and belongs to the field of spectrum detection and optical imaging. The device comprises a broadband adjustable wavelength light source, a high-resolution wavelength scanning module, a stimulated Raman signal excitation module and a stimulated Raman signal detection module. The stimulated Raman signal excitation and imaging module is used for focusing the two beams of narrow-band femtosecond pulse light which are overlapped in time and space on a sample and exciting a stimulated Raman signal, and meanwhile, moving the sample to realize spot scanning microscopic imaging; and the stimulated Raman signal detection module is used for converting the stimulated Raman scattering signal into an electric signal, demodulating the electric signal by using a lock-in amplifier, and recording spectral information by using a data acquisition card. According to the invention, the broadband tunable characteristic of the optical parametric amplifier is utilized to tune the pump light to the wavelength region of the sample absorption peak length so as to realize electron pre-resonance stimulated Raman; and a balance detection technology is combined at a detection end, so that high-sensitivity, high-resolution and high-signal-to-noise-ratio detection of stimulated Raman signals is realized.

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 provides a multi-focus and multi-band channel auto-focusing confocal microscopic imaging system, and relates to the technical field of microscopic imaging, the system comprises an excitation end, a detection end and a control module, the control module is connected with the excitation end and the detection end; according to the invention, through integration of advanced optical design and an automatic control algorithm, rapid and accurate focusing is realized, and imaging under multiple wavebands is supported at the same time.

The invention provides an exosome automatic identification and analysis method based on microscopic imaging, and belongs to the technical field of exosome identification and analysis, and the method comprises the steps: collecting an exosome fluorescence microscopic image, carrying out the preprocessing, and obtaining an exosome region through adaptive threshold segmentation and morphological processing. A GPU is adopted to process and extract morphological, texture and statistical feature matrixes in parallel, and meanwhile, the CPU recognizes an abnormal region. And combining the features to form a region feature set, inputting the region feature set into the preliminary screening model for scoring and screening high-credibility regions. Deep feature extraction is carried out on the high-credibility region, and multiple features are fused by using an attention fusion mechanism to construct vectors; and inputting a depth classifier to obtain a classification probability matrix and a screening result. And finally outputting the number, position, classification and credibility score of the exosomes. The problems that an existing exosome analysis method often depends on manual recognition, and automatic and high-throughput analysis is difficult to achieve are solved.

The invention relates to the technical field of biological microscopic imaging, and discloses a denoising and deconvolution super-resolution imaging method and device based on optical switch molecules, electronic equipment and a computer readable storage medium. The method comprises the following steps: acquiring a plurality of fluorescence image sequences, wherein to-be-imaged structures of images in the fluorescence image sequences are marked by optical switch fluorescence molecules; each sequence is generated by utilizing the photoswitch characteristic of the photoswitch fluorescent molecules, and the sequence comprises at least one bright-state image and at least one dark-state image; performing de-noising processing on each bright-state image and each dark-state image in the plurality of fluorescence image sequences to obtain de-noised images; and carrying out deconvolution processing on the denoised image to obtain a corresponding super-resolution imaging image. The requirement for imaging equipment is low, the method can be used for various imaging technologies, the size of an imaging area is not limited, the requirement for large-view-field fixed biological sample imaging in an application scene can be met, and fixed cell and tissue large-view-field super-resolution imaging is achieved.