175 results about "Scattering correction" patented technology

A ginkgo canopy chlorophyll content estimation method based on an airborne hyperspectral image comprises the following steps: firstly, acquiring a ginkgo leaf canopy hyperspectral image and a chlorophyll content data set; and establishing a rapid prediction model for predicting the average content of chlorophyll in the canopy by using the hyperspectral image of the ginkgo leaf canopy, and predicting the corresponding chlorophyll content by using the model and taking the hyperspectral image of the ginkgo leaf canopy as input. The model establishment process comprises the following steps of: firstly, preprocessing an image by using multiple scatter correction (MSC); secondly, carrying out characteristic wave band selection on the preprocessed spectral data by adopting a competitive self-adaptive reweighted sampling algorithm CARS; and finally, substituting the screened characteristic wave band spectral data and the actually measured chlorophyll value into an XGBoost-RF machine learning model, and establishing a hyperspectral chlorophyll content estimation model. The method provided by the invention is a nondestructive rapid detection technology, and can monitor the plant health condition in real time.

The invention discloses a high-sensitivity carbendazim detection method based on terahertz metamaterial resonance enhancement, and provides a terahertz metamaterial sensor with a cross-shaped composite double-peak structure, a terahertz spectrum technology is utilized to collect transmission spectrums, the difference of different concentrations among terahertz spectrums is explored, and the detection accuracy is improved. The analysis finds that the transmission peak amplitude of the terahertz transmission spectrum of the structure is in a decreasing trend along with the increase of the concentration of a carbendazim solution, data is preprocessed through multiple scatter correction (MSC), feature extraction is performed by adopting an iterative information variable reservation (IRIV) method, and a quantitative detection model of the carbendazim terahertz transmission spectrum is established based on an LS-SVM (least squares support vector machine). The invention provides a novel approach for quantitative detection of carbendazim, and experimental results show that the RP of the model reaches 0.9825, the RMSEP is 0.2001, and the detection limit LOD is 0.672 mu g / ml. The invention provides a new method for rapid, lossless and high-sensitivity detection of carbendazim in food.

The invention relates to the field of water quality detection, in particular to a method, system and equipment for detecting solution impurities through a water quality detector and a medium. The method comprises the following steps: acquiring spectral data of a to-be-detected ion solution; a correction observation window is constructed on the spectral data according to the local concentration gradient and used for extracting local nonlinear features, and the local nonlinear features comprise absorbance gradient change features and fitting residual error anomaly features; establishing a mapping relation between the spectral data and a water quality detection result according to the local nonlinear characteristics in the correction observation window, and generating a correction coefficient through a scatter correction algorithm; correcting the spectral data according to the mapping relation and the correction coefficient; and identifying impurity components in the ion solution based on the corrected spectral data. The stable mapping relation between the absorbance and the impurity concentration can still be maintained in a strong ion interference environment, so that the measurement result can keep reliable precision even in a high-concentration ion intensity environment.

The invention relates to the technical field of image focus detection, in particular to a fiber endoscope image focus detection method and system, and the method comprises the following steps: providing a data quality guide interface; capturing narrow-band light images of the endoscope away from a first preset position and a second preset position of the intestinal wall in the axial movement process, calculating brightness gain values of a blue light channel and a green light channel, calculating an asymmetric scattering correction coefficient, and updating indication of color information calibration integrity; tracking the pixel moving speed of the interferent in the view in the propulsion operation process, calculating the relative depth of the interferent, and updating the indication of the validity of the depth data of the interference layer; identifying the image area which is not shielded by the interferent, and updating the indication of the information coverage degree of the background area; triggering an image synthesis operation; performing color compensation on the image information of the image area which is stored in the background canvas and is not shielded by the interferent; reconstructing a clear image; and performing focus detection on the clear image. The method improves the accuracy of focus detection.

The invention provides a near-infrared spectrum rapid detection method and system for effective components of a herbicide, which are applied to the technical field of agricultural detection.The near-infrared spectrum rapid detection method comprises the steps of obtaining near-infrared spectrum data of a to-be-detected soil area and moisture content and compactness numerical values reflecting soil texture differences, and calculating the effective components of the herbicide on the basis of a preset interval to which the moisture content numerical value belongs; according to the method, the corresponding preset scattering correction parameter is selected, the actual scattering correction parameter is obtained through compactness numerical calculation, fine adjustment of the scattering correction parameter is achieved, and the quality and reliability of spectral data are improved through spectral correction; and the corrected spectrum is analyzed, so that the real condition of the herbicide in the soil can be reflected more accurately. Therefore, the interference of complex soil texture in the field on near infrared spectrum herbicide detection is effectively overcome, and the beneficial effect of rapidly and accurately detecting the effective components of the herbicide under the complex soil condition is achieved.

The invention discloses an underground pipeline three-dimensional deformation quantitative inspection method, which comprises the following steps: processing a multi-source sensing sequence to generate a baseline data packet; the method comprises the following steps of: performing first-stage on-line correction, acquiring a medium state agent quantity by applying micro-excitation sampling combined by a micro-amplitude angle and double power to laser, and performing joint inversion to obtain section geometric data subjected to medium correction by utilizing a forward model with endogenous refraction and scattering correction items; second-stage scale robustness processing is carried out, in a unified optimization framework, motor current-based dynamic consistency constraint, geometric conservation constraint and wellhead external reference strong constraint are fused, and a globally consistent pose track is solved; and performing three-dimensional reconstruction and deformation quantification according to the corrected section data and the pose track. According to the invention, the accuracy of geometric measurement of the pipeline and the global consistency of the pose track in a complex environment such as a humid environment can be improved.

The invention discloses a method for detecting the nitrogen content of organ tissues of different varieties of oilseed rapes based on visible near-infrared spectroscopy, which comprises the following steps: firstly, collecting oilseed rape leaf, shell, stalk and root system samples under the conditions of multiple varieties and multiple nitrogen fertilizer levels, and acquiring spectral data within the range of 430-2500nm by using a visible near-infrared spectroscopy; and a Kjeldahl method is synchronously adopted to measure the real nitrogen content as a reference value. Preprocessing the spectral data, including de-noising, standard normal variable transformation, multivariate scatter correction and derivative processing, so as to weaken the influence of scattering and baseline drift; characteristic wavelengths related to the nitrogen content are screened through stepwise regression, variable projection importance, competitive self-adaptive reweighted sampling, a continuous projection algorithm and other methods, and a random forest model, a support vector machine model, a partial least squares discriminant analysis model, a partial least squares regression model, a support vector regression model, an XGBoost model and other models are combined. And respectively constructing a classification identification model and a regression prediction model.

Aiming at the problems of low efficiency, strong subjectivity, insufficient modeling ability, complex hyperspectral data noise, weak waxiness spectrum difference and the like of a traditional rice grain waxiness discrimination method, the invention provides a hyperspectral rice grain waxiness discrimination method based on multi-branch collaborative modeling. The method comprises the following steps: S1, acquiring glutinous and non-glutinous rice grain images by using a hyperspectral imaging system to obtain spectral data; s2, preprocessing spectral data through a combined method of SG smoothing, an asymmetric weighted penalty least square method and multivariate scatter correction to reduce noise and interference; s3, constructing a multi-branch modeling architecture which comprises a CNN local feature extraction module, an SRU spectrum sequence modeling module and a HorNet global high-order modeling module, and outputting a discrimination result through a classifier after multi-branch features are subjected to fusion and pooling attention weighting; and S4, evaluating the performance. According to the method, high-precision lossless discrimination of the waxiness is realized, and a technical support is provided for germplasm screening and quality evaluation in rice breeding.

The invention discloses a quantitative analysis method for heavy metals in soil, which comprises the following steps: dividing a high-difference region and a low-difference region according to the difference significance of the heavy metal concentration between in-situ spectral data and a pollution source distance, and carrying out scattering correction on the characteristic peak of the in-situ spectral data in the high-difference region by using the scattering characteristic of a heavy metal spectrum; reversely simulating heavy metal scattering artifacts according to the scattering correction spectrum and the in-situ spectrum data of the low-difference region, performing artifact repair through Mie scattering constraint based on the simulated heavy metal scattering artifacts, and performing bilinear interpolation based on spectral characteristics of an artifact repair spectrum in a detection region. And inputting the interpolated heavy metal spectrum distribution grid and heavy metal concentration into a multilayer perceptron quantitative model to obtain a heavy metal quantitative result and a heavy metal distribution result. According to the method, the accuracy of heavy metal detection and the interpretability of heavy metal distribution are improved by carrying out scattering correction and artifact repair on spectral data of a detection area.

The invention relates to a near-infrared coal quality quantitative analysis model construction method, and belongs to the technical field of coal quality analysis. In order to solve the problems that a traditional coal quality analysis method is tedious in operation, time-consuming, destructive and difficult to meet rapid nondestructive detection requirements, the invention aims to realize rapid, accurate and quantitative detection and analysis of moisture, ash and volatile components in coal by combining a near infrared spectrum analysis technology with a deep learning algorithm. The method comprises the following specific steps: collecting and preparing a coal sample according to the national standard, and measuring moisture, ash and volatile components; the method comprises the following steps: acquiring spectral data by using a near infrared spectrometer, marking the spectral data, performing smoothing, multiplicative scatter correction and normalization processing, and dividing a training set and a test set according to a Kennard-Stone algorithm; and constructing a cascaded integrated deep learning prediction model of CNN-LSTM, and performing training, evaluation and optimization to predict coal quality indexes. According to the method, data are obtained based on a strict national standard experiment method, the accuracy of quantitative analysis of coal quality and the robustness of the model are improved by combining the near infrared spectrum technology with the deep learning model, and powerful support is provided for quality control and reasonable utilization of resources in the coal industry.

The invention discloses a satellite remote sensing atmospheric pollution gas concentration inversion method and system based on DOAS, and belongs to the technical field of atmospheric environment remote sensing monitoring. The method comprises the following steps: acquiring solar back scattering spectrum data observed by a satellite load and preprocessing the solar back scattering spectrum data; performing spectrum correction by using a high-precision instrument response function; in a selected fitting window, constructing a fitting model comprising a target gas differential absorption cross section, a Ring effect, Mie scattering and multiple scattering contributions; carrying out spectrum fitting by adopting a nonlinear least square algorithm, and carrying out inversion to obtain a differential batter column concentration (DSCD) of the target gas; in conjunction with an Atmospheric Mass Factor (AMF), the DSCD is converted to a Vertical Column Concentration (VCD). The system comprises a data acquisition and preprocessing module, a spectrum fine correction module, a DOAS spectrum fitting module, a dynamic AMF calculation module and a column concentration inversion and output module, and optionally comprises a data assimilation and traceability analysis module. According to the satellite remote sensing atmospheric pollution gas concentration inversion method and system based on the DOAS, the parameterized multiple scattering correction items are introduced, a dynamic AMF calculation scheme is developed, and the inversion precision and reliability under the complex atmospheric condition are remarkably improved.

The invention belongs to the technical field of ocean laser radar remote sensing detection, and particularly relates to an ocean subsurface water body element vertical structure inversion method and system and a computer program. The method comprises the following steps: carrying out preprocessing, distance correction and logarithm conversion on a laser radar echo signal; constructing an iterative hybrid inversion model to invert a water body attenuation coefficient and a particulate matter backscattering coefficient; performing scattering correction for multiple times; matching multi-source data to construct a deep learning model; and finally inverting the chlorophyll concentration and the vertical section of the granular organic carbon. According to the method, the multiple scattering effect of the water body and the influence of system parameters are comprehensively considered, data driving and a physical model are combined, high-precision and automatic marine water body element inversion can be achieved under the complex water quality condition, and the method is suitable for ecological environment monitoring and research of large-range and multi-type sea areas.

The invention discloses a bed load particle movement velocity measurement method based on Doppler flow velocity profiler sound wave scattering correction. The method comprises the following steps: selecting a measurement river reach, sampling silt on the surface of a riverbed on a floodplain of an unsubmerged area, screening the sampled silt, and calculating the median particle size of the bed sand of the river reach according to the screening result; a measurement area is selected, an ADCP and a matched unmanned ship carrying DGPS are used for constant-speed cruise type flow measurement, and flow measurement data are obtained; calculating a riverbed movement speed according to the ship speed data; filtering the riverbed movement speed data, and calculating a characteristic value of the filtered riverbed movement speed data; respectively calculating a bed load vertical projection area ratio and a bed load scattering correction coefficient according to the filtered riverbed movement speed data characteristic value; and calculating the movement speed of the bed load according to the vertical projection area ratio of the bed load and the scattering correction coefficient of the bed load. According to the invention, the difficulty that the Doppler flow velocity profiler cannot be directly used to measure the accurate movement velocity of the bed load at present is overcome.

The invention provides a radiation inspection system which is provided with an inspection channel for an inspected object to pass through and comprises a transmission inspection device, a back scattering inspection device, a signal processing device and an image processing device. The transmission inspection device comprises a transmission ray source and a transmission detector which are arranged on the two opposite sides of the inspection channel. The back scattering inspection device comprises a back scattering ray source and a back scattering detector which are arranged on the same side of the inspection channel. The transmission ray beam output time of the transmission ray source and the pencil-shaped ray beam output time of the back scattering ray source are staggered. And the back scattering signal correction device is in signal connection with the back scattering detector and is configured to deduct an afterglow signal generated by the transmission ray beam to the back scattering detector from a back scattering detection signal generated by the back scattering detector when the pencil-shaped ray beam is output so as to form a back scattering correction signal. And the back scattering image processing device is in signal connection with the back scattering signal correction device and is configured to form a back scattering image according to the back scattering correction signal.

The embodiment of the invention discloses a scattering correction method, device and system for X-ray imaging and a medium. The method comprises the steps that for a CT imaging system based on a double-layer detector, an object signal scanned by the CT imaging system for a scanned object is obtained, an anti-scattering grid in the CT imaging system is placed between a low-energy detector and a high-energy detector, and the high-energy detector is arranged between the low-energy detector and the high-energy detector; the object signals comprise low-energy object signals received by the low-energy detector and high-energy object signals received by the high-energy detector, and the high-energy object signals are filtered by the anti-scattering grid; estimating a low-energy direct signal in the low-energy object signal according to the high-energy object signal; and performing CT image reconstruction according to the low-energy direct signal to obtain a low-energy image, and performing CT image reconstruction according to the high-energy object signal to obtain a high-energy image. According to the technical scheme of the embodiment of the invention, rapid scattering correction of CT imaging can be realized.

The invention relates to a scatter correction method, a cone beam computed tomography (CBCT) system and a computer storage medium. The method comprises the following steps: performing cone beam CT imaging on a to-be-measured object to obtain a first group of projection data of the to-be-measured object, and performing reconstruction according to the first group of projection data to obtain a first volume image containing scattering artifacts; performing fan-beam CT imaging on a plurality of different heights of the to-be-measured object to obtain a plurality of groups of fan-beam projection data of the to-be-measured object, performing reconstruction according to the plurality of groups of fan-beam projection data to obtain a plurality of fan-beam slice images, and using the plurality of fan-beam slice images as reference images without scattering artifacts; extracting a plurality of cone beam slice images from the first volume image; performing image registration on the plurality of fan-beam slice images and the plurality of cone-beam slice images; and training a scattering correction model based on deep learning by using the plurality of registered image pairs to obtain a trained scattering correction model.

The invention relates to a scattering correction method and system for a PET image. The method comprises the following steps: alternately and iteratively solving radioactivity distribution and an attenuation coefficient of a detector space based on an ADMM algorithm; mapping from PET detection data and a detector space attenuation coefficient to accurate radioactive activity distribution is realized based on the deep learning network G1; based on a deep learning network G2, mapping from a detector space attenuation coefficient to matched linear attenuation coefficient distribution is realized, and an object mask matrix is obtained; unfitted scattering estimation is obtained through single scattering simulation; keeping radioactive activity distribution and a detector space attenuation coefficient as constants, and obtaining a scattering correction factor by using unfitted scattering estimation; and obtaining a PET image subjected to scattering correction by utilizing the accurate scattering distribution after fitting of the spatial attenuation coefficient of the detector and the scattering correction factor. The beneficial effects are that compared with traditional scatter correction, the method is higher in correction precision and shorter in time consumption, further improves the image quality, and reduces the reconstruction time consumption.

A method for training an algorithm for machine learning for a correction of recordings obtained by an imaging apparatus. A number of output images are recorded. Moreover a smaller number of first, high-quality scattered radiation images are simulated from the output images. A corresponding number of second, low-quality scattered radiation images is further simulated, wherein the simulation is undertaken with a number of photons reduced by at least an order of magnitude. The algorithm is trained with the second, low-quality scattered radiation images as input data and the first, high-quality scattered radiation images as output data.

The invention discloses an accurate X-ray radiation dose measurement system based on artificial intelligence, which comprises the following steps: acquiring CT image data of a patient and pulse irradiation parameters of radiotherapy equipment through terminal equipment, and calculating a patient specific anatomical structure dynamic influence factor set, a pulse waveform time domain characteristic factor and a tissue interface scattering correction factor; all the parameters are substituted into a dose measurement formula to obtain an accurate X-ray radiation dose value; through a deep fusion image recognition technology (such as a high-resolution anatomical structure, tissue density gradient and heterogeneous region distribution provided by CT / MRI), a secondary particle scattering effect of a junction region of a tumor and a normal tissue can be accurately represented; and dynamic anatomical information (such as respiratory movement and organ displacement) in the image is combined to correct the time characteristic (such as dynamic change under an instantaneous high dose rate) of the pulse radiation field in real time, so that the dose calculation accuracy of a fast algorithm in a complex scene is remarkably improved.

The invention relates to the technical field of spectral analysis, discloses a soil carbon content rapid detection method and device based on spectral analysis, and provides a multi-scale attention fusion convolutional network model. The method comprises the following steps: firstly, carrying out smoothing, scattering correction and normalization preprocessing on collected soil spectral data; then, performing multi-scale feature extraction by using parallel convolution branches with convolution kernels of different sizes so as to comprehensively capture macroscopic and microscopic information in the spectrum; thirdly, a spectral attention module is originally introduced, so that the model can be adaptively focused on key features which have great contribution to SOC prediction, and interference such as moisture is inhibited; and finally, fusing the weighted features through a deep regression network and outputting an analysis result. An efficient technical scheme is provided for rapid and accurate detection of the carbon content of the soil.

The invention discloses an image processing system and method suitable for a turbid water area, and belongs to the technical field of image processing. The system comprises an underwater camera component for collecting original image data of a turbid water area in real time and an embedded image processing unit. The embedded image processing unit comprises a preprocessing module used for carrying out noise suppression and image calibration processing on original image data, and a physical parameter estimation module used for estimating environment background light intensity of a current water area, a water body attenuation coefficient and a distance map of an object in a scene and calculating initial transmissivity. The scattering correction module is used for removing back scattering and preliminarily compensating a color channel based on a physical imaging model in combination with physical parameters, and the deep learning enhancement module is used for improving image local contrast, adjusting brightness and finely correcting colors by adopting a lightweight deep convolutional neural network. According to the image processing system and method suitable for the turbid water area, the image definition and color of the turbid water area can be improved, the real-time performance is high, and the system and method are suitable for different water area environments.

The invention relates to a 1D-CNN (1D-Convolutional Neural Network)-based dual-band fused hyperspectral flaxseed incomplete grain identification method, which is characterized in that sample data of moldy grains, damaged grains, thermal damaged grains, germinated grains and normal grains are synchronously acquired through a visible-near infrared (400-1000nm) and short-wave infrared (1000-2000nm) dual-band hyperspectral imaging system; after black and white board correction and region-of-interest extraction, seamless splicing of dual-band spectral data is realized by adopting a linear interpolation technology, then data quality is optimized by preprocessing methods such as multivariate scatter correction and standard normal transformation, and finally, deep features are extracted by utilizing a 1D-CNN deep learning model with a three-layer convolution structure. According to the technology, the limitation of traditional single-band detection is broken through, the identification accuracy of the flaxseed imperfect grains is improved to 97.50%, and compared with a single-band detection method, the identification accuracy is remarkably improved by 3-5%.

The invention discloses a method and system for rapid detection of field fertility based on soil two-dimensional spectrum data migration learning. Firstly, spectrum data is input into a spectrum data two-dimensionalization module, and bicubic interpolation is performed according to short-time Fourier transform to convert a one-dimensional spectrum into a two-dimensional spectrum graph; the processed two-dimensional spectrum graph is input into an SS-MSC preprocessing module, and two-dimensional standardization processing and two-dimensional multivariate scattering correction processing are performed on the image in sequence; based on an improved Inception-v4 soil nitrogen, phosphorus and potassium content detection network model, pre-training of a neural network is completed according to a pre-training data set; finally, the neural network model is adaptively tuned according to soil nitrogen, phosphorus and potassium content data of a field standard sample point, migration of the neural network model is completed, and rapid detection of field fertility is realized; the problems of too much priori data of a farmland to be tested required for training a prediction model in an existing method and poor universality of the model for different soil types are solved, and the efficiency and accuracy of field fertility detection are improved.

The invention discloses an intelligent image recognition security early warning system and method based on AI, relates to the technical field of artificial intelligence and security, and solves the problem that effective collection of multi-modal data is difficult to realize in a dynamic scene. An image in a complex environment is difficult to process to ensure the quality; real-time detection of abnormal behaviors and prediction of potential criminal actions are difficult to consider; the identification sensitivity is difficult to dynamically optimize according to scene features; and grading response is difficult to carry out on the early warning information. According to the invention, by fusing multispectral imaging and edge computing nodes and by means of an atmospheric scattering correction algorithm, an image in a complex environment is effectively processed; a dual-channel deep learning architecture is constructed, and real-time detection of abnormal behaviors and prediction of potential criminal actions are realized; through a dynamic threshold adjustment mechanism, the recognition sensitivity is optimized based on scene features; and a multi-mode interaction protocol and a self-adaptive execution engine are utilized to complete response of three-level early warning information.

The invention relates to a cross scatter correction method and device, computer equipment, a computer readable storage medium and a computer program product, relates to the technical field of medical images, and can improve the accuracy of cross scatter correction. The method is applied to a double-source or multi-source CT imaging system, the double-source or multi-source CT imaging system comprises at least two detectors, each detector comprises a plurality of channels, and the method comprises the following steps: acquiring a signal intensity hopping relationship corresponding to the double-source or multi-source CT imaging system; the signal intensity hopping relationship is determined according to the detection signal intensity of each channel of the plurality of detectors, and the detection signal intensity is obtained by scanning different motifs and / or the same motif at different positions by the detectors; obtaining scattering estimation information obtained by scanning the to-be-measured object by the dual-source or multi-source CT imaging system; and correcting the scattering estimation information according to the signal intensity hopping relationship.

The invention relates to the technical field of cone-beam computed tomography, and discloses a scattering correction method and system for cone-beam computed tomography. The method comprises the following steps: acquiring original projection data and main scattering data acquired by a first flat panel detector; wherein the first flat panel detector is arranged in the emission direction of the radiation source; acquiring secondary scattering data acquired by a second flat panel detector; wherein the second flat panel detector is arranged in the scattering direction of the radiation source; determining target projection data based on the original projection data, the main scattering data and the auxiliary scattering data; the target projection data is used for cone beam computed tomography imaging. According to the method, scattering correction can be completed on the premise of not depending on special hardware, not additionally increasing radiation dose and not losing imaging view, and the imaging precision can be effectively improved.

The invention discloses an aerogel composite thermal insulation coating coefficient prediction method based on an MG model, and relates to the technical field of functional thermal insulation paints.The aerogel composite thermal insulation coating coefficient prediction method comprises the following steps that S1, a standard MG model of an aerogel composite thermal insulation coating is corrected, and a final model is obtained; s2, constructing a correction model by using the scattering correction factor; and S3, predicting the heat conductivity coefficient by using the final model, and predicting the infrared reflectivity by using the corrected model. The invention provides a reversible design path for the far infrared functional heat insulation coating, and is suitable for various heat management scenes such as building outer walls, roofs, flexible cloth and the like.

The present application relates to the field of water quality detection, and in particular to a method, system, device and medium for detecting solution impurities using a water quality detector. The method comprises: obtaining spectral data of an ion solution to be tested; constructing a correction observation window based on the local concentration gradient on the spectral data to extract local nonlinear features, the local nonlinear features including absorbance gradient variation features and fitting residual anomaly features; establishing a mapping relationship between the spectral data and the water quality detection results based on the local nonlinear features within the correction observation window, and generating a correction coefficient through a scattering correction algorithm; correcting the spectral data based on the mapping relationship and the correction coefficient; and identifying the impurity components in the ion solution based on the corrected spectral data. A stable mapping relationship between absorbance and impurity concentration can be maintained even in a strong ion interference environment, so that the measurement results can maintain reliable accuracy even in a high-concentration ion strength environment.

This invention belongs to the field of materials analysis and discloses an X-ray diffraction multiple scattering correction method, comprising: Step 1, obtaining equivalent diffraction points based on the symmetry characteristics of the crystal, wherein the equivalent diffraction points are the set of points with diffraction intensities I(h, k, l); Step 2, determining whether the criteria for judging outliers are met; if met, deleting; otherwise, retaining; Step 3, determining whether the deletion rate is met; if met, deleting; otherwise, retaining; Step 4, the remaining dataset I without outliers. 校正 (h, k, l) is used for subsequent fine-tuning; the corrected I 校正 (h, k, l) is more conducive to the accuracy of electronic structure refinement.

A method of performing scatter correction on an X-ray image is disclosed. The method comprises obtaining an input image for processing based on a source X-ray image of a sample acquired using an X-ray detector, and determining a model of the sample based on the input image. The model is then evaluated by computing, based on the model, simulated X-ray image data and evaluating the simulated image data against the input image to determine whether a convergence criterion is fulfilled. An updated model of the sample is generated if the convergence criterion is not fulfilled. The model evaluating step is repeated based on one or more successive updated models until the convergence criterion is fulfilled in a final iteration, and scatter correction is then performed on the source X-ray image using simulated image data computed during the final iteration.