739 results about "High spatial resolution" patented technology

The invention discloses an intelligent operation abnormity monitoring and positioning method for a polypropylene cable, and relates to the technical field of intelligent operation and maintenance of a power system, and the method comprises the steps: collecting multi-source physical signals in the operation process of the cable, and constructing a multi-dimensional feature matrix fusing multiple physical quantities through multi-scale time window division and space mapping processing; extracting space-time coupling characteristics among nodes by using a graph attention embedding network, and training a running state recognition model in combination with a label perception contrast learning mechanism; constructing a cable topological graph based on an identification result, introducing an improved Bayesian space reasoning network, and calculating abnormal probability distribution of each node; a weighted abnormal heat map is further generated, an abnormal propagation path is extracted through an abnormal state flow model and a directional propagation scoring algorithm, and abnormal node positioning and trend evolution prediction are achieved; the method has the advantages of high spatial resolution, high identification precision and good online adaptability, and is suitable for intelligent state perception and abnormity early warning of the polypropylene cable in a complex operation environment.

The invention discloses a space-time spectrum combined super-resolution reconstruction method based on a giant remote sensing satellite group. The method comprises the following steps: acquiring time series, multi-view and multi-spectral data of the same area from a plurality of heterogeneous satellites, and performing radiometric calibration and atmospheric correction; sub-pixel-level alignment of the multi-source data is realized by adopting a joint registration model; extracting time change features by using three-dimensional convolution, extracting space structure and texture features by using two-dimensional convolution, and extracting and reducing the dimension of spectral features by using one-dimensional convolution; performing adaptive weighted fusion on time, space and spectral features through an attention mechanism to generate a joint feature tensor; and carrying out super-resolution reconstruction to obtain a target image with high spatial resolution, high time resolution and high spectral fidelity. The method gives consideration to both resolution improvement and spectrum authenticity, and is suitable for high-precision remote sensing application scenes such as fine urban mapping, agricultural monitoring, ecological environment assessment, disaster emergency and battlefield situation awareness, remote reconnaissance, target change detection and damage assessment.

The invention provides a high-speed defect identification method and system based on optical diffraction imaging, and relates to the technical field of optics, and the method comprises the following steps: constructing a composite optical diffraction unit comprising a programmable optical metasurface and a quantum phase modulator, optimizing and determining modulation characteristics through a multi-target constraint function, and collecting a quantum coding diffraction image. The quantum state phase reconstruction network is used for processing the image and extracting characteristic parameters, and a result is obtained through analysis of a defect identification model. According to the invention, rapid defect identification with high spatial resolution is realized, and the detection efficiency and accuracy are improved.

The invention discloses a high-resolution hyperspectral image reconstruction method based on wavelet domain feature fusion and contrast learning, and belongs to the technical field of image fusion and super-resolution reconstruction. The method comprises the following steps: constructing a fusion network model comprising a wavelet transformation module, a cross-modal feature fusion module, a high-frequency contrast learning module and an image reconstruction module; performing end-to-end supervised training by using a training data set containing the low-resolution hyperspectral image and the high-resolution multispectral image; and after training is completed, inputting a test image pair to realize image reconstruction. According to the method, the detail retention capability is improved by combining wavelet decomposition and a directional fusion mechanism, the cross-modal high-frequency feature alignment capability is enhanced through comparative learning, a fusion image with high spatial resolution and high spectral consistency is finally generated, and the method is suitable for multi-modal image reconstruction tasks such as remote sensing, medical and natural images.

The invention discloses a panchromatic sharpening method and system based on cross-resolution adversarial learning and a Mama network. The method comprises the following steps: acquiring a low-resolution multispectral image LRMS and a high-spatial-resolution panchromatic image PAN; extracting features of the low-resolution multispectral image LRMS to obtain a first feature map; extracting features of the high-spatial-resolution panchromatic image PAN to obtain a second feature map; performing wavelet decomposition and weighted fusion on the first feature map and the second feature map to obtain a multi-channel feature map, and performing dynamic fusion on the multi-channel feature map through a cross attention gating mechanism to obtain a fused feature map; and carrying out residual connection on the fused feature map and the low-resolution multispectral image, and then carrying out feature reconstruction to obtain a high-resolution multispectral remote sensing image. According to the method, the panchromatic sharpening performance of the remote sensing image can be improved, and the high-quality and high-resolution multispectral remote sensing image is obtained.

The invention relates to the technical field of medical elastography and material multi-scale mechanical measurement, in particular to an elastography method and system based on an instantaneous scattering displacement field, and the method comprises the steps: driving a passive / active method scattering displacement field excitation device through a frequency domain controllable sine wave signal, and generating a scattering displacement field on a target material, an instantaneous displacement field is obtained through high spatial resolution measurement, then sliding window local interception, two-dimensional autocorrelation calculation and nonlinear fitting based on the Rayleigh / shear wave spatial autocorrelation theory are performed on the single-frame instantaneous displacement field, shear wave velocity distribution is obtained, and then the viscoelasticity modulus of a target area is inverted to achieve comprehensive characterization of the viscoelasticity of the material. According to the scheme, the single-frame scattering displacement field is excited and collected, and the elasticity inversion is performed based on the instantaneous displacement field, so that the dependence of traditional elasticity imaging on high-frame-rate equipment is broken through, the material viscoelasticity evaluation can be efficiently completed at low cost, and the application prospect in the fields of clinical diagnosis and mechanical measurement is wide.

The invention discloses a multi-frame photoacoustic image reconstruction method based on optical flow alignment and depth feature fusion. The method comprises the following steps: S1, obtaining photoacoustic signal data; s2, reconstructing a photoacoustic cross-sectional image; s3, performing optical flow calculation and image alignment; and S4, training the deep feature fusion network. According to the method, through optical flow motion correction and a potential space learning mechanism, space-time information and complementary features in the aligned multiple frames of images are dynamically integrated, complementary information in the multiple frames of images is adaptively fused, noise is suppressed, and finally reconstruction of high signal-to-noise ratio and high spatial resolution images of biological tissues is achieved. Experimental results show that the method can significantly improve image quality, recover image distortion and detail loss caused by motion and noise, and provide a new effective scheme for promoting robust clinical application of a photoacoustic imaging technology.

The invention discloses an area intelligent identification alarm system for tower crane hoisting, which relates to the technical field of alarm systems and comprises a front-end data acquisition module, an acquired data processing module, a wireless network communication module, a hoisting safety analysis module and a safety alarm execution module. The visual detector, the detection sensor and the environment sensor form a multi-element acquisition module, hoisting parameters and environment information can be synchronously acquired in real time on the hoisting site of the tower crane, the comprehensiveness of information acquisition is ensured, and the problem that the traditional configuration is single in acquisition element and the cost is low is solved through the redundancy design of multiple acquisition elements. The method solves the problem that the existing tower crane is easy to fail, realizes real-time intelligent identification and graded early warning of potential safety hazards in a tower crane hoisting operation area, has the characteristics of high dynamic adaptability, high spatial resolution, intelligent response, high deployability and the like, and provides scientific and efficient technical support for safety management of tower crane operation.

The invention discloses a DEM intelligent super-division method based on high-spatial-resolution remote sensing data, and the method comprises the steps: inputting a high-resolution remote sensing image to a guide selection branch module, inputting a low-resolution DEM to a terrain reconstruction branch module, carrying out the interaction through a terrain perception cross attention module, obtaining guide information and DEM features, and carrying out the super-division of the high-spatial-resolution remote sensing image. And performing multi-source feature fusion on the guide information and the DEM features to obtain fused features, training the guide information, the DEM features and the fused features by adopting a total loss function until the total loss function is converged, and outputting a target high-resolution DEM. According to the method, the high-resolution remote sensing image which is easy to obtain is used as guide information to guide the super-resolution reconstruction process of the low-resolution DEM, so that a high-resolution DEM product which is high in precision and rich in terrain details can be generated with relatively low cost and relatively high efficiency.

The invention belongs to the technical field of ocean observation, and discloses an underwater sound velocity distribution super-resolution construction method based on multi-modal data fusion. According to the method, a sound velocity distribution forecasting model is constructed, the model carries out temperature preliminary reconstruction on original space area temperature data through a Unet neural network, and sea surface temperature features are extracted through a Mama neural network; correcting the preliminarily reconstructed temperature data through sea surface temperature features to obtain high-spatial-resolution temperature data; and finally, obtaining high-spatial-resolution sound velocity distribution data through a sound velocity distribution calculation module. According to the method, the underwater temperature and the sea surface temperature are subjected to data fusion, so that the sound velocity distribution of the whole space area is quickly and accurately forecasted under the condition of original low-spatial-resolution data, and the problem of insufficient spatial resolution of ocean sound velocity distribution estimation is solved.

The invention provides a panchromatic sharpening joint optimization method and system based on three-stage progressive fusion. The method comprises three stages of coarse fusion, deblurring detail enhancement and fine fusion. The method comprises the following steps: firstly, performing feature extraction and preliminary fusion on a low-resolution multispectral image and a high-resolution panchromatic image through a dual-path mutual enhancement network; secondly, a multi-layer stacked deblurring module is introduced to perform deep enhancement on fusion features, and the details and definition of the image are effectively improved in combination with partial large kernel convolution, channel mixing and an element-level attention mechanism; and finally, realizing fine optimization of spectrum consistency and a space structure, and outputting a fused image with high spatial resolution and high spectral fidelity. According to the method, the stability and reconstruction quality of panchromatic sharpening under a complex degradation condition are effectively improved through multi-source remote sensing image collaborative modeling and progressive feature fusion, and the method has good practical value and popularization prospect and is superior to a current panchromatic sharpening method based on deep learning.

The invention discloses a flood monitoring and deduction method based on unmanned aerial vehicle time sequence radar remote sensing, which comprises the following steps: S1, collecting multi-source flood disaster high-spatial-resolution time sequence radar remote sensing data through unmanned aerial vehicle radar remote sensing, and processing the data into a remote sensing data set with consistent space-time reference; s2, preprocessing each time sequence radar remote sensing image in the remote sensing data set; s3, segmenting a submerged area and a non-submerged area of the preprocessed time sequence radar remote sensing image, removing fine water in the submerged area, and extracting a radar remote sensing high-resolution submerged range; and S4, quantitatively extracting flood evolution characteristic elements, and performing flood disaster potential influence area deduction on the current submerging range in combination with the digital elevation model and hydrological connectivity. According to the method, the problem that the large-range flood evolution process is difficult to dynamically monitor is solved, and support is provided for rapid emergency and risk avoiding transfer of watershed flood by forward deduction of the potential influence area of flood disasters.

The invention discloses a temperature difference-deformation decoupling method for thermal expansion coefficient in-situ monitoring, and belongs to the technical field of material physical property testing, and the method comprises the following steps: obtaining high spatial resolution temperature distribution data and full-field deformation data; performing three-dimensional reconstruction on the high-spatial-resolution temperature distribution data to generate dynamic temperature field data; theoretical thermal stress is calculated based on the dynamic temperature field data and the material mechanical parameters, and interference deformation data is generated; a dynamic correction coefficient is introduced to decouple full-field deformation data, and a real thermal expansion deformation field is output; calculating a thermal expansion coefficient based on the real thermal expansion deformation field and the temperature change trend data; and monitoring a uniformity index of a real thermal expansion deformation field, and adjusting a dynamic correction coefficient and a material mechanical parameter on line. According to the method, the temperature field and the deformation field are synchronously collected, thermal stress interference deformation caused by temperature difference is theoretically calculated and eliminated, and a closed-loop feedback optimization mode is combined, so that high-precision dynamic monitoring of the thermal expansion coefficient of the material under the non-uniform temperature condition can be realized.

The invention provides a method for rapidly determining the center position of an electron cloud cluster of a reading circuit in an XS anode detector, and aims to solve the problems that the existing method for calculating the center position of the electron cloud cluster of the reading circuit in the XS anode detector cannot realize higher spatial resolution, or easily causes image distortion, and is inconvenient to use. The software processing speed is slow, the FPGA is not easy to implement, or the calculation error at the edge strip is relatively large. The method for rapidly determining the center position of the electronic cloud cluster of the reading circuit in the XS anode detector is realized by adopting an FPGA (Field Programmable Gate Array), the peak value of effective data of each channel is obtained through single-channel processing, and then a corresponding drop point distribution index code is generated through table look-up and cross-channel processing; and then adaptive weight selection is carried out according to the drop point distribution index code, so that the weight accuracy of weighted average operation is ensured, key support is provided for rapid and accurate calculation of the center position of the electronic cloud cluster, and high efficiency and accuracy of the whole data processing flow are ensured.

According to the flexible array type external field program control nerve stimulation device, the preparation method and the application thereof, the flexible array type external field program control nerve stimulation device comprises a flexible substrate layer and a stimulation response fiber array layer, and the stimulation response fiber array layer is prepared from a functional material capable of converting external field energy such as ultrasound, magnetism and light into electric energy; the flexible substrate layer with high flexibility is adopted, so that the flexible substrate layer can be matched with the surface of a nerve target tissue in a conformal manner; the stimulation response fiber array layer integrated on the flexible substrate layer in an array arrangement mode can meet the stimulation spatial resolution of neuron and even sub-neuron precision, has the characteristics of excellent flexibility, biocompatibility, passivity and wireless performance, can provide nerve electrical stimulation signals with high spatial resolution, and can be applied to the field of nerve electrical stimulation. The high-resolution nerve electrical stimulation device can be in conformal fit with the irregular surface of nerve soft tissue, and is suitable for high-resolution nerve electrical stimulation of different parts such as eyes, the brain and peripheral nerves.

A novel and useful direct time of flight distance measuring device that incorporates smart pixel architecture, real-time in-pixel histogram and intensity acquisition, optical power control, and variable resolution. The device avoids the use of time to digital (TDC) converters by generating in-pixel histograms based on a global high-speed clock that implements per pixel time bin single photon event accumulation using circuitry located under or near the SPAD pixels. Laser events are processed within a smart pixel architecture increasing substantially the data conversion throughput while reducing data movement in the device to achieve high spatial resolution, low power operation, and fast reliable depth capture. The device uses a compact split pseudo-random counter. Each smart pixel operates independently and concurrently, allowing the sensor to operate in global shutter while monitoring the SNR and determining the optimal exposure time for transmitter and receiver power savings. This enables full control of the pixel operation, enable / disable, and optimization.

The invention discloses a high-spatial-resolution distributed optical fiber sound wave sensing system based on FPGA real-time demodulation. The system comprises a narrow linewidth laser source module, a first coupler, a high-bandwidth sweep frequency signal modulation module, a circulator, a coherent receiving and photoelectric conversion module and an FPGA integrated module. A laser source emitted by the narrow linewidth laser source module is divided into about 10% of reference light and about 90% of detection light through the first coupler. The FPGA integrated module is connected with the high-bandwidth sweep frequency signal modulation module, and transmits a sweep frequency radio frequency pulse signal and a frequency shift radio frequency pulse signal to the high-bandwidth sweep frequency signal modulation module; the high-bandwidth sweep frequency signal modulation module is connected with the sensing optical fiber through the circulator so as to output sweep frequency optical pulses; the sensing optical fiber is connected with the coherent receiving and photoelectric conversion module through the circulator to transmit Rayleigh backscattered light; the coherent receiving and photoelectric conversion module is connected with the FPGA integrated module, and the FPGA integrated module carries out signal processing to obtain vibration information on the optical fiber.

The invention relates to the technical field of water ecology monitoring, in particular to a dynamic monitoring and early warning method and system for water ecology health. According to the method, the remote sensing image data is acquired and equally divided into a plurality of grids, so that high-precision and high-efficiency data acquisition is facilitated, the condition of each region is more refined, monitoring data with higher spatial resolution can be acquired, and the monitoring accuracy is improved. The calculation of the local heterogeneity index is beneficial to revealing the difference of different areas of the water body in the aspect of ecological health, and the health conditions of different water body areas can be accurately identified. The problem that in the prior art, ecological health assessment is inaccurate due to spatial resolution limitation and an unreasonable data acquisition mode is solved, a more scientific and accurate assessment means is provided for water health through real-time and dynamic water monitoring, accurate region division and health index calculation, and the ecological health assessment method is more accurate. And early warning and timely response based on data driving are realized.

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.

The invention relates to a cone beam CT imaging scanning parameter determination method and imaging equipment, and the method comprises the steps: designing an optimization model for a microgravity environment, carrying out the optimization processing of the cone beam CT imaging scanning parameters based on a pre-constructed optimization model with the maximum imaging performance as a target, and obtaining the optimal parameters of the cone beam CT imaging. Wherein optimization problem modeling and solving are sequentially carried out in a projection domain and an image domain so as to obtain target scanning parameters meeting constraint conditions, and according to the technical scheme of the invention, aiming at factors such as scanning space and energy consumption in space, requirements of high spatial resolution, compact physical size of equipment, high image quality and low scanning dose are met, and the method and the device have the advantages of being high in efficiency and high in reliability. The method is applied to cone beam CT imaging in the space microgravity environment, and the comprehensive requirements of scientific research, health guarantee and medical service in the space environment are met.

The invention discloses a gas leakage detection imaging device and method based on a single photon detection technology, and belongs to the technical field of gas detection and imaging. Aiming at the limitation problems of the existing gas leakage detection technology in the aspects of sensitivity, response speed, space imaging capability and anti-interference capability, nanosecond modulation control and synchronous photon counting are realized by utilizing an FPGA (Field Programmable Gate Array) through a visual gas detection imaging device combining an SPAD (Single Program Amplifier) array detector and a WMS modulation-demodulation mechanism; and the second harmonic of the absorption signal is further extracted by combining digital phase-locked amplification, so that gas leakage three-dimensional imaging with high spatial resolution, ppb-level sensitivity, strong background suppression capability and high time response is realized. The system has the advantages of high detection sensitivity, real-time imaging capability with high spatial resolution, high background light interference resistance and high signal-to-noise ratio, the demodulation method is novel, and the modular design has multi-gas compatibility and system expansibility.

The invention belongs to the field of tunnel engineering, and relates to a tunnel defect detection data spatial resolution enhancement method and device based on a Vine Copula dependent structure and a conditional random field, a computer readable medium and a computer program product. The method comprises the following steps: firstly, separating an overall trend and random fluctuation from original tunnel defect monitoring data; then establishing a Vine Copula multivariable dependence model to represent a statistical correlation relationship among the plurality of defect indexes; and then generating defect distribution data with high spatial resolution by using a conditional random field interpolation simulation technology in combination with a spatial autocorrelation analysis result. According to the invention, while the accuracy of the existing measuring point data is ensured, the spatial correlation structure and multivariable joint distribution characteristics of the defect data are maintained, and the precision of spatial interpolation prediction is improved.

The invention provides a multi-focus excitation confocal fluorescence microscopic imaging flow cytometry screening method. The method is characterized in that a laser beam generates three laser beams through a spatial light modulator, and the three laser beams are coherently superposed in a focal plane of a microscope objective to generate a two-dimensional optical lattice light field. And after the other laser beam is shaped by the cylindrical lens, linear laser optical tweezers are realized through the microscope objective, cells to be detected in an imaging detection area of the micro-flow chip are captured, and accurate active optical control on the rotation angle of the cells to be detected is realized. In a cell rotation process, a two-dimensional optical lattice light field rapidly scans in a focal plane at different angles, and a fluorophore in the cell is excited to generate a fluorescence signal. The confocal pinhole array installed at the position conjugate with the focal plane eliminates background fluorescence, a high-spatial-resolution fluorescence image of a three-dimensional structure in the cell is obtained, and cell screening is achieved. The method provided by the invention has the characteristics of high detection rate, high accuracy and the like, and has wide application prospects in the research fields of biology, medicine and life science.

The invention belongs to the field of land surface temperature reconstruction, and relates to a high-spatial-temporal-resolution land surface temperature reconstruction method used in a cloud environment, which comprises the following steps: calculating a first reference land surface temperature and a first land surface temperature residual error of a clear sky pixel under a low spatial resolution according to a first land surface temperature annual change model; seamless surface reflectance data under high spatial resolution are obtained; defining a relative solar radiation index based on the digital elevation model data with high spatial resolution; screening clear sky pixels participating in modeling of the XGBoost model; an XGBoost model is adopted, and a plurality of model relations between a low-spatial-resolution land surface temperature annual change model coefficient and a land surface attribute and between a day-by-day land surface temperature residual error and the land surface attribute are constructed; obtaining a high-spatial-resolution surface temperature annual change model coefficient and a day-by-day surface temperature residual error based on the constructed multiple model relationships, and obtaining a high-spatial-resolution daily surface temperature; the accuracy and the stability of a surface temperature reconstruction result are improved.

The invention discloses a living blood vessel and lymph multi-parameter quantitative photoacoustic microscopic imaging system which comprises a high repetition frequency laser source, a photoacoustic scanning probe and a molecular imaging device. Through the cooperation of the multi-wavelength fast switching laser and the high-speed scanning probe, synchronous, dynamic and quantitative imaging of vascular function parameters and lymph concentration in a living tissue microenvironment is realized. The system has a video-level imaging rate and can capture a rapid biological process; through coaxial design of dispersion correction and a photoacoustic focus, high spatial resolution and high signal-to-noise ratio of a multispectral image are guaranteed; and the compact probe design realizes large-range scanning. The technology provides a powerful in-vivo visualization tool for research on life science problems such as tumor evolution and drug metabolism.

The invention discloses a deep learning-based satellite remote sensing collapsible loess foundation evaluation method, which comprises the following steps of: acquiring an optical remote sensing image, a radar remote sensing image and an infrared remote sensing image of a target area, and preprocessing; collapsibility feature weighted data are screened, and feature weights are distributed; feature extraction is carried out through a local space exhibition structure and a multi-layer Transform structure of the collapsibility foundation evaluation network; collapsibility area self-supervised collaborative learning is carried out, and self-supervised training is carried out based on spatial correlation, pseudo labels and spatial consistency regular terms; and carrying out risk grade division on the target area, and outputting a collapsibility risk result of each spatial position. According to the method, multi-mode remote sensing and deep learning are fused, high-precision intelligent partitioning of the collapsible loess foundation is achieved, and the method has the advantages of being self-adaptive, low in manpower and high in spatial resolution.

The invention provides a rock-soil moisture content and permeability testing system and method based on electromagnetic waves and infrared rays, and particularly relates to the technical field of geotechnical engineering testing and multi-source signal detection. According to the test system, construction of a rock-soil permeability experiment is realized through a rock-soil sample permeability test platform, the electromagnetic wave detection module realizes perception of a water-containing state in rock-soil, and the infrared thermal imaging module realizes perception of a wet boundary and seepage expansion characteristics of a rock-soil surface. The data acquisition and control module realizes integral control of a penetration experiment and acquisition of dielectric characteristics and infrared images, the signal fusion and inversion module realizes fusion of the dielectric characteristics and the infrared images and joint inversion of dynamic moisture content and penetration performance, and the result processing and visualization module realizes visual display of an inversion result; the method has the remarkable advantages of non-destructiveness, real-time performance, high spatial resolution, high adaptability, intelligent processing capacity and the like, and comprehensive characterization of the permeability behavior and the moisture content evolution process of the rock and soil sample is achieved.

The invention provides a spectrum confocal three-dimensional shape measurement method and system based on an area light source, and belongs to the technical field of optical measurement, and the method comprises the steps: employing a Micro LED area light source capable of line-by-line driving to generate RGB three-color modulated isoenergetic white light, and after collimation and beam splitting of a beam splitter prism, the isoenergetic white light is converted into a light source; and the dispersion objective lens axially focuses light rays with different wavelengths at different height positions on the surface of a measured object. The reflected light is reflected to the array slit through the beam splitter prism and is split by the diffraction grating to form a spectral image containing space-wavelength two-dimensional information, and the surface height is analyzed by using a peak wavelength-height mapping relation. Full-field scanning is achieved by switching the area light sources line by line, and three-dimensional shape reconstruction can be completed without mechanical movement. The invention has the advantages of high light source brightness, high light source uniformity, uniform energy distribution of each wavelength, high scanning speed and high spatial resolution.

Disclosed are approaches to non-invasively characterize a tumor or other lesion in a region of interest (ROI) based on various analyses of magnetic resonance imaging (MRI) data. The MRI data may correspond to ultrafast dynamic contrast enhanced MRI (DCE-MRI) and high spatial resolution DCE-MRI scans, and diffusion-weighted MRI (DW-MRI) scans of the ROI. Vasculature metrics may be determined, and tumor-associated blood flow velocity and / or tumor interstitial pressure may be obtained using the vasculature metrics as inputs to a computational fluid dynamics model. A combination of morphological vascular metrics and functional vascular metrics may be used to characterize the tumor. Malignancy, aggressiveness, treatment response, and other features of tumors or other lesions, in the breast or other regions of a patient, may be characterized through disclosed analyses of MRI data.

The invention provides a super-resolution reconstruction method based on a CNN (convolutional neural network)-Transform hybrid architecture. According to the method, aiming at the defects of generalization and performance of a traditional super-resolution reconstruction algorithm of satellite-borne passive microwave remote sensing data, accurate matching and dynamic fusion of orbit ascending and orbit descending data are realized through an interactive feature alignment module, and a double-branch network is constructed by means of a multi-scale space-time attention mechanism module; extracting and integrating spatio-temporal characteristics under different receptive fields; meanwhile, a long-distance dependency relationship is established by using a hybrid adaptive attention module, the global feature modeling capability is enhanced, and finally the spatial resolution of passive microwave remote sensing data is remarkably improved through an efficient up-sampling module. According to the invention, passive microwave remote sensing data with higher spatial resolution can be obtained, and passive microwave remote sensing data with higher quality can be provided for global scale earth surface parameter inversion and monitoring research.