149 results about "Scattering correction" patented technology

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 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 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.

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.

This invention discloses a highly sensitive detection method for carbendazim based on terahertz metamaterial resonance enhancement. The invention proposes a terahertz metamaterial sensor with a "+" shaped composite double-peak structure. Transmission spectra were acquired using terahertz spectroscopy, and the differences in terahertz spectra at different concentrations were investigated. Analysis revealed that the amplitude of the transmission peak in the terahertz transmission spectrum of this structure decreases with increasing carbendazim solution concentration. Data preprocessing was performed using multivariate scattering correction (MSC), feature extraction was conducted using the iterative information-preserving variable (IRIV) method, and a quantitative detection model for carbendazim terahertz transmission spectra was established based on LS-SVM. This provides a new approach for the quantitative detection of carbendazim. Experimental results show that the model has a high R-value. P The assay yielded a value of 0.9825, an RMSEP of 0.2001, and a detection limit (LOD) of 0.672 μg / ml. This invention provides a novel method for the rapid, non-destructive, and highly sensitive detection of carbendazim in food.

The invention relates to a method for training a machine learning algorithm to correct an image obtained by means of an imaging device. A plurality of initial images (10) are recorded. In addition, a smaller number of first high-quality scattered radiation images (14 ') are simulated from the initial image. Furthermore, a corresponding number of second low-mass scattered radiation images (13 ') are simulated, the simulation being performed by reducing the number of photons by at least one order of magnitude. The algorithm is trained using the second low-quality scattered radiation image (13 ') as input data and the first high-quality scattered radiation image (14') as output data.

The present application relates to the field of medical imaging technology, and in particular to a multi-energy CT spectral imaging method and apparatus, wherein the method comprises: obtaining scanning object parameters of a target object; constructing a target optimization criterion, and establishing a high- and low-energy spectral projection model based on the target optimization criterion combined with the scanning object parameters to obtain optimal spatiotemporal filter parameters; using the optimal spatiotemporal filter parameters to produce corresponding spatiotemporal filters to collect high- and low-energy projection data, and using the low-frequency properties of scattering to perform scatter correction on the high- and low-energy projection data to obtain descattered projection data, and performing base material decomposition in the projection domain or performing descattered image reconstruction to perform base material decomposition in the image domain to complete the multi-energy CT spectral imaging task. The present application can select appropriate spatiotemporal filter parameters based on a specific material decomposition task and apply them to the production of spatiotemporal filters, effectively improving the performance of multi-energy CT spectral imaging.

The invention provides an image correction method and a double-projection imaging system, and relates to the technical field of images, in particular to the technical field of image correction. According to the method, a first projection image and a second projection image of a target object under a target projection angle can be acquired, scattering correction is performed on the first projection image, and a corrected image under the target projection angle is determined based on the first projection image after scattering correction and the second projection image of the target object under the target projection angle. Wherein the target projection angle is a projection angle within a target projection angle range, the first projection image comprises a shadow area formed after shielding of the first BSA, and the target projection angle range is a projection angle range where the first projection group and the second projection group are overlapped. According to the method, the phenomenon of shielding artifacts of the image in the reconstruction process can be effectively reduced.

SOLUTION: An X-ray imaging apparatus and associated method are provided that receive measured projection data from a wide aperture scan of a wide body-axis direction and a narrow aperture scan of a narrow body-axis direction within the wide body-axis direction and determine an estimated scatter in the wide body-axis direction using an optimized scatter estimation technique. The optimized scattering evaluation technique is based on the difference between the measured scattering in the narrow body-axis direction and the evaluated scattering in the narrow body-axis direction. The kernel-based scatter evaluation / scatter correction approach can be fitted to minimize scatter differences in a narrow body-axis direction. Thereafter, the fitted (optimized) kernel-based scatter evaluation / correction is applied to the wide body-axis direction region. Optimization may occur in the projection data domain or in the reconstruction domain. An iterative process is also utilized.SELECTED DRAWING: None

Methods for identifying micron-scale features below a surface of a tissue include emitting from one or more emitters at the surface of the tissue electromagnetic signals capable of penetrating tissue of different densities; obtaining from a sensor positioned at the surface of the tissue electromagnetic signals reflected from or transmitted through tissues of different densities as one or more obtained images; inputting the one or more obtained images into a machine learning model, wherein the machine learning model is trained to: identify scattering of certain electromagnetic signals comprising each of the one or more obtained images; correct for the scattering to create one or more images tissues at one or more subsurface depths; and identify micron-scale features of interest within the one or more images of tissues at the one or more subsurface depths.