63 results about "Scattering correction" patented technology

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.

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 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.

The application discloses a kind of ocean polarization lidar multiple scattering correction methods based on machine learning, comprising: based on polarization Monte Carlo method, construct ocean polarization lidar radiation transfer numerical model, set multiple groups of lidar hardware and water body environment parameters, obtain a large number of simulation data, utilize machine learning method to construct the polarization multiple scattering correction model of different detection system, and use non-uniform water body to verify the effectiveness of model.According to the multiple scattering model of the effective attenuation coefficient of the measured signal and the lidar of different platforms, the signal depolarization ratio and water body parameter are calculated, and the polarization multiple scattering correction model is brought into the polarization multiple scattering correction model combined with the system parameters of lidar, to obtain seawater backscattering depolarization ratio.Compared with the direct seawater backscattering depolarization ratio of lidar signal depolarization ratio, the error between the seawater backscattering depolarization ratio corrected by the method and the in-situ value is significantly reduced.

This invention relates to a method and system for detecting fish lysate protein content based on near-infrared spectroscopy. The method includes: acquiring near-infrared spectral data of a fish lysate sample, and constructing a sample dataset based on the near-infrared spectral data and corresponding protein content labels; and performing partitioned scattering correction and characteristic peak enhancement processing on the near-infrared spectral data based on prior information of protein characteristic peaks to obtain an enhanced near-infrared spectral vector. Compared with existing technologies, the advantages of this invention are that it effectively preserves and enhances the key spectral features of fish lysate proteins, improves signal identification, eliminates interference from irrelevant components at the source, and significantly improves the prediction stability and repeatability accuracy of the model in complex industrial scenarios involving fish lysate.

This invention proposes a method for rapid determination of the content of Phellodendron amurense charcoal slices processed at different temperatures using near-infrared spectroscopy. Near-infrared spectroscopy is used to collect spectral information of Phellodendron amurense charcoal slices processed at different temperatures. Background interference is eliminated through preprocessing methods such as baseline correction, standard normal variable transformation, multivariate scattering correction, and first derivative. Combined with competitive adaptive reweighted sampling, CARS combined with iterative retention of information variables, and iterative combination optimization, a partial least squares regression quantitative prediction model is established. This model achieves accurate prediction of the content of five key components: 5-O-FQA, 3-O-FQA, berberine, berberine, and berberine, with prediction correlation coefficients all exceeding 0.90. This invention combines spectral information with chemometric methods to establish a digital content prediction model based on near-infrared spectroscopy for online monitoring of the Phellodendron amurense charcoal slice processing process. It has the advantages of objectivity, speed, non-destructive nature, and reliability, effectively solving the technical problem of lack of objectivity in existing technologies based on human judgment.

The invention discloses a system for multispectral transmission image breast lesion segmentation, and belongs to the technical field of medical image processing and optical imaging. The system comprises an image preprocessing subsystem and a semi-supervised segmentation network subsystem. The preprocessing subsystem adopts modulation and demodulation, frame accumulation and a scattering correction technology based on spatial pyramid matching, so that the signal-to-noise ratio and the contrast ratio of an image are remarkably improved; the segmentation network subsystem proposes a multi-head consistency semi-supervised network model, integrates a multi-head decomposition module and a cross supervision module, and effectively utilizes unlabeled data to carry out uncertainty modeling and consistency constraint. Under the condition that only 20% of labeled data is used, the segmentation Dice coefficient on the self-built mammary gland phantom data set reaches 91.47% and is obviously superior to that of an existing method, and a complete solution from image enhancement to intelligent segmentation is provided for early diagnosis of the breast cancer.

The invention relates to a rapid determination method for sewage quality based on a spectrum method, and belongs to the technical field of water quality detection. According to the method, a sewage sample is subjected to filtering, standing and temperature control pretreatment to remove interference, 200-2500nm full-spectrum data is obtained through a dynamic optical path spectrum acquisition module, and various interference is eliminated through multi-stage pretreatment of sub-band baseline correction, scattering correction and self-adaptive noise reduction; then inputting an attention-enhanced deep learning model to extract a targeted water quality index feature vector, realizing synchronous quantification of chemical oxygen demand, ammonia nitrogen, total phosphorus and total nitrogen through multi-model weighted fusion, and finally outputting a result through two-stage verification of model confidence verification and statistical abnormal value detection. According to the method, the problems of narrow detection concentration range, poor feature extraction pertinence, insufficient result reliability and poor interference elimination effect in the prior art are solved, the detection time is less than or equal to 3 minutes, the accuracy rate of each index is greater than or equal to 97%, the detection limit meets the GB 3838-2002 standard, and the method can be widely applied to on-site rapid monitoring of industrial drain outlets, sewage treatment plants and other scenes.

The application provides a rapid detection method and system for herbicide effective components based on near-infrared spectroscopy, which is applied to the field of agricultural detection technology, and the near-infrared spectroscopy data of a soil region to be detected, the water content and the compactness values reflecting the soil texture differences are obtained, based on the preset interval to which the water content value belongs, the corresponding preset scattering correction parameter is selected, the actual scattering correction parameter is calculated through the compactness value, the fine adjustment of the scattering correction parameter is realized, the quality and reliability of the spectral data are improved through spectral correction; the corrected spectrum is analyzed, and the real situation of the herbicide in the soil can be more accurately reflected. Therefore, the application effectively overcomes the interference of the complex soil texture in the field on the near-infrared spectroscopy herbicide detection, and realizes the beneficial effect of rapid and accurate detection of the herbicide effective components under the complex soil condition.

In order to reduce a computing effort for correcting initial images from an imaging apparatus, a scatter model is used to simulate scattered-radiation images, and corresponding initial images are selected from the original initial images. These are used to train a neural network. The neural network may be used to calculate scattered-radiation single images that may be used to correct each individual initial image into a corrected initial image.

Systems, methods, and devices for generating a corrected image are provided. A first robotic arm may be configured to orient a source at a first pose and a second robotic arm may be configured to orient a detector at a plurality of second poses. An image dataset may be received from the detector at each of the plurality of second poses to yield a plurality of image datasets. The plurality of datasets may comprise an initial image having a scatter effect. The plurality of image datasets may be saved. A scatter correction may be determined and configured to correct the scatter effect. The correction may be applied to the initial image to correct the scatter effect.

The application provides a scattering correction method and device, a digital device and a computer readable storage medium. The method comprises the following steps: obtaining a corresponding relationship function of one-dimensional detection energy spectrum and one-dimensional gamma photon energy spectrum; based on the corresponding relationship function, using a maximum likelihood-expectation maximization iterative algorithm to obtain two one-dimensional gamma photon energy spectrums corresponding to each down-sampling response line; based on the one-dimensional gamma photon energy spectrums, obtaining the number of non-scattering single events corresponding to each down-sampling response line; based on the total number of single events and the number of non-scattering single events corresponding to each down-sampling response line, calculating the number of scattering coincidence events corresponding to each down-sampling response line; performing up-sampling processing on the down-sampling response line to obtain an up-sampling response line; and calculating the number of scattering coincidence events corresponding to each up-sampling response line. When the scattering correction is performed, the application has the advantages of simple implementation and high accuracy.

The application provides a scattering correction method and device, a digital device and a computer readable storage medium. The method comprises: obtaining a down-sampling response line based on detection data; obtaining an estimated target function corresponding to each down-sampling response line by using a matrix estimation method based on a maximum likelihood-expectation maximization iterative algorithm; solving the estimated target function to obtain a number of scattering coincidence events corresponding to each down-sampling response line; performing up-sampling processing on the down-sampling response line to obtain an up-sampling response line; and calculating the number of scattering coincidence events corresponding to each up-sampling response line. When the embodiment of the application performs scattering correction, it does not depend on activity images and attenuation images, and the method does not require a hyperparameter, so that the hyperparameter no longer needs to be set in a determined range, and the method is simple and accurate.

This invention relates to the technical field of testing or analyzing materials by measuring their physicochemical properties, specifically to a real-time monitoring system for multiple parameters of environmental water quality based on spectral analysis. The system includes optical paths for acquiring ultraviolet-visible (UV-Vis) and Raman spectra. These two optical paths are coaxially coupled via a dichroic mirror and an optical fiber combiner and connected to the same water flow cell. A timing control module enables time-division multiplexing acquisition of the same spatial coordinates. An edge computing module receives the combined spectral data, performs nonlinear scattering correction based on the Beer-Lambert law, separates the baseline drift component representing suspended matter scattering and the transmission component representing absorption characteristics, and inputs the characteristic peak intensity of the Raman spectrum as a priori constraint into the partial least squares inversion model. By qualitatively identifying specific organic compounds through Raman characteristic peaks, the solution space boundary of the corresponding overlapping bands in the UV-Vis spectrum is locked, and the inversion results for chemical oxygen demand (COD), total organic carbon (TOC), and ammonia nitrogen are output.

The application discloses a scattering correction method, a PET imaging method, a device, equipment and a storage medium. The scattering correction method comprises the following steps: obtaining TOF-PET data and an attenuation image of a target object during scanning; obtaining a back projection image of TOF information based on the TOF-PET data; obtaining a scattering estimation of TOF-PET imaging based on the back projection image and the attenuation image; and performing scattering correction on the TOF-PET data based on the scattering estimation of TOF-PET imaging. The application solves the technical problem of long time consumption of scattering correction in TOF-PET imaging in the prior art.

The application discloses a kind of self-adapting direction and step of multi-energy spectrum CT iterative reconstruction method with scattering correction, comprising: step 1, setting multi-energy spectrum CT projection reconstruction model with scattering correction;Step 2, using iterative algorithm to solve out the increment of base material distribution projection value angle by angle, update the current base material image to new base material image;Wherein, through self-adapting direction and step to quickly obtain convergent base material projection image.The scheme provided by the application is used to remove scattering, and the result of base material decomposition is more accurate, and the reconstruction result artifact is less, which lays a foundation for quantitative analysis.

The application discloses a plant species accurate classification method using an image data processing algorithm, and relates to the technical field of plant species classification, and comprises the following steps: obtaining water body data and polarized multispectral image data of a target object; performing pixel polarization decomposition on the polarized multispectral image data to generate a polarization component image; performing pixel-by-pixel mapping on the water body data and the polarization component image to generate a scattering correction weight field; and performing coupling calculation on the scattering correction weight field and the polarization component image according to the pixel neighborhood gradient of the water body data to generate an initial compensation estimate. Through the introduction of the iteration mechanism controlled by the discrimination threshold, the scattering correction weight can be dynamically adjusted according to the polarization component change of the local area, the local compensation failure caused by the uneven spatial distribution of the water body turbidity is avoided, and through the iterative optimization of the fusion comparison result and the difference analysis data, the learner can be self-adapted to the water body conditions with different turbidity.

The invention relates to a ray inspection system and a scattering correction method. The ray inspection system comprises: a ray source (1) configured to generate a ray beam (10); at least part of the first detector array (2) is located in the coverage range of the ray beam (10); the second detector array (3) and the first detector array (2) are located on the same side of an inspection object (4) of the ray inspection system, the second detector array (3) is located outside the coverage range of the ray beam (10), and the second detector array (3) is configured to receive scattered signals of the ray beam (10) in the process of penetrating through the inspection object; and the processor is in signal connection with the first detector array and the second detector array, and is configured to convert the scattering signals according to a preset relationship between the scattering signal detection capabilities of the first detector array and the second detector array, and perform scattering correction on the received signals of the first detector array according to the converted scattering signals.

The invention relates to a scatter correction method based on a side irradiation scintillator. The cuboid scintillators are sliced into piles along the long sides in an equal-thickness mode; collimated beams matched with the sheet thickness only penetrate through the first sheet from the side face to irradiate, the camera directly faces for imaging, and the brightness is extracted one by one; fitting a scattering attenuation relation based on an illuminated experiment to obtain a scattering correction factor; and deducting scattering of the actually measured brightness item by item according to layers, recovering the real brightness, and obtaining three-dimensional dose distribution according to brightness-dose calibration. According to the method, initial scintillation light and interlayer scattering can be separated, the structure is simple, calibration and measurement are consistent, and the three-dimensional dose measurement precision and reproducibility are remarkably improved.

The invention discloses a tomato sugar degree and hardness online detection method based on hyperspectral depth characteristics. The method comprises the following steps: acquiring tomato hyperspectral data and executing data correction; calculating polarization degree data and polarization angle data, and constructing a polarization reflection cube; a PH-SSN model is established based on the polarization reflection cube, a constraint set is constructed, and Sheaf consistency embedding features are generated; in the decoding network, executing hot spot suppression and surface defect identification, and outputting a hot spot suppression reflection spectrum; noise removal, scattering correction and standard normal variable transformation are performed on the hot spot suppression reflection spectrum, and polluted wavebands are eliminated; extracting transmission features and reflection features and generating a transmission feature vector and a reflection feature vector; and obtaining sugar degree and hardness predicted values through a nonlinear regression network, and performing weighted correction to generate corresponding detection results. According to the invention, the spectrum modeling precision and the anti-interference capability are improved.

The invention discloses a hyperspectral detection method for the sugar degree and hardness of tomatoes. The method comprises the following steps: firstly, acquiring a hyperspectral reflection or transmission spectrum of tomatoes in a range of 400-1000nm, and carrying out pretreatment such as standard normal transformation, multivariate scatter correction, Savitzky-Golay smoothing and orthogonal signal correction on the original spectrum; then, characteristic wavelength screening is carried out on the preprocessed spectrum through a competitive adaptive reweighted sampling (CARS), an irreducible variable elimination (UVE) or a UVE-CARS joint algorithm, and a characteristic spectrum matrix is obtained; a Kennard-Stone (KS) algorithm is further adopted to divide a training sample into a correction set and a prediction set, a tomato sugar degree model and a tomato hardness model are constructed based on partial least squares regression (PLSR), and an optimal main factor number is determined through cross validation; and inputting the characteristic spectrum of the tomato to be detected into the model, and outputting the predicted values of sugar degree and hardness. The method has the advantages of no damage to fruits, high modeling stability, good prediction precision and the like, and is suitable for the fields of tomato quality evaluation, grading, postharvest treatment and the like.

The present application relates to a kind of vortex electromagnetic wave target high-frequency scattering calculation method based on equivalent edge electromagnetic current correction, belong to electromagnetic calculation field.Method includes: constructing target triangulation model and extracting convex split edge;Bessel vortex wave is decomposed into plane subwave superposition;The main scattering field under the irradiation of subwave is calculated using physical optics PO;Based on physical diffraction theory, the diffraction field of subwave in convex split edge is calculated using PTDEEC model of equivalent edge electromagnetic current;The main scattering field and edge diffraction field are superposed to obtain the modified total field, and the radar cross section (RCS) is calculated.The present application effectively corrects the diffraction error of traditional PO method at the edge of target by equivalent edge electromagnetic current, significantly improves the scattering calculation precision of large-size edge target under complex vortex beam irradiation, and provides more reliable high-frequency algorithm for radar target recognition and stealth design.

The invention provides a method for detecting the content of ergosterol in lentinus edodes based on near infrared spectroscopy and application. And rapidly detecting the content of the ergosterol in the shiitake mushrooms by adopting a near infrared spectrum technology, and inputting spectral data of a sample to be detected into the optimal model to obtain a predicted value of the ergosterol, so that the prediction of the content of the ergosterol in the shiitake mushrooms is realized. According to the method, the SG smoothing algorithm and the normalization algorithm are combined to preprocess the spectrum, noise generated when the sensor obtains the spectrum data can be effectively removed, and baseline and scattering correction is carried out; the variable combined population analysis-genetic algorithm is used for carrying out characteristic wavelength extraction on an original spectrum, so that required important variables can be effectively extracted, redundant information is efficiently removed, and the operation rate of the model is improved; the content of ergosterol in shiitake mushrooms is predicted by adopting the least square support vector machine model based on the crown porcupine optimization algorithm, and the model is high in adaptive capacity and high in prediction accuracy. And a new technical approach is provided for rapidly detecting the content of ergosterol.

The invention discloses a near-infrared spectrum identification method of polygonatum kingianum, which comprises the following steps: (1) drying different types of polygonatum kingianum, taking the same amount of sample powder, adopting an integrating sphere diffuse reflection sampling system, using an Antaris II Fourier transform near-infrared spectrometer and TQ Analyst spectral analysis software to collect spectrums, and comparing the spectrums with the sample powder; obtaining near infrared spectrums of different types of rhizoma polygonati; (2) carrying out multiplicative scatter correction and second-order derivative preprocessing on the near infrared spectrums of different types of polygonatum sibiricum to obtain a preprocessed OPLS-DA score map; (3) according to the OPLS-DA scoring graph, different types of rhizoma polygonati are concentrated in different quadrants; polygonatum kingianum is located in the second quadrant. The invention establishes a method for quickly, accurately and nondestructively identifying the polygonatum kingianum, and solves the technical bottleneck of the high-value variety in the traditional identification.

The application provides a hemoglobin concentration measuring endoscope device and method, and relates to the technical field of endoscopes. The target tissue is alternately illuminated by narrow-band light combination 1 and narrow-band light combination 2, color image 1 and color image 2 are obtained, reflection information sensitive to shallow, middle and deep hemoglobin is extracted therefrom, shallow, middle and deep hemoglobin indexes are obtained by using scattering correction and background light correction, the hemoglobin indexes are normalized and corrected by using a hemoglobin absorption coefficient, and finally, the hemoglobin concentration is calculated based on the corrected hemoglobin indexes and a concentration distribution map is output. The application can more accurately reflect the comprehensive distribution of blood vessels in the shallow, middle and deep tissues, and further more accurately measure the hemoglobin concentration of the tissues, while ensuring the need for real-time image observation of the endoscope.

This invention discloses a non-destructive method for detecting strawberry freshness based on hyperspectral imaging and machine learning, belonging to the field of agricultural product quality testing technology. The method includes: acquiring hyperspectral images of strawberries in the 900-1700 nm wavelength range and extracting the average spectrum of the region of interest; performing multivariate scattering correction and Savitzky-Golay second-order derivative filtering preprocessing on the spectrum; using a genetic algorithm to screen a subset of characteristic wavelengths; and inputting the screened characteristic wavelengths into a pre-trained backpropagation neural network classification model to output the freshness grade determination result. This invention, through the technical path of "MSC and SG2 preprocessing—GA feature screening—BPNN classification," achieves rapid, non-destructive, and high-precision detection of strawberry freshness. The model's recognition accuracy exceeds 99% under storage conditions of 4℃ and 20℃, and can be widely applied to post-harvest grading, cold chain monitoring, and online sorting equipment for strawberries.

The invention discloses a pesticide pollutant identification method and system based on multi-modal attention feature fusion, and relates to the technical field of intelligent agriculture and environmental monitoring, and the method comprises the following steps: obtaining multi-modal spectral data of a pesticide sample, including an excitation emission matrix (EEM) image and ultraviolet-visible spectrum (UV-Vis) data; preprocessing the multi-modal spectral data, wherein the preprocessing comprises noise removal, scattering correction, normalization and multi-label coding; the preprocessed EEM image and the UV-Vis data are input into an MAFC-NBeats model, and the MAFC-NBeats model is used for carrying out image segmentation; the MAFC-NBeats model carries out residual decomposition on the input signal through a plurality of cascaded processing blocks; superposition is carried out based on the prediction output of each processing block, and a final pesticide classification result is obtained. Outputting a pesticide classification result, and providing signal reconstruction and interpretability analysis. According to the method, a neural network architecture combining a multi-modal attention mechanism and N-BEATS residual decomposition is introduced, so that deep fusion and progressive decomposition of an excitation emission matrix and ultraviolet visible spectrum data can be realized.

The invention provides a spectrum detection method and system for modified ultra-high molecular plastic, and relates to the technical field of high molecular spectrum detection. The method comprises the following steps: firstly, constructing a scattering correction model based on scattering characteristics of an ultraviolet-visible spectrum in a non-absorption wave band, and carrying out accurate baseline correction on an infrared spectrum to obtain an intrinsic infrared absorption spectrum; then analyzing the intrinsic infrared absorption spectrum to obtain a relative content parameter of a modifier component, and introducing the relative content parameter as a constraint condition into an unmixing process of a Raman spectrum so as to effectively separate and extract a Raman characteristic signal of the modifier. And finally, carrying out complementary analysis and cross validation on the intrinsic infrared absorption spectrum and the Raman characteristic signal to determine key modification structure information such as chemical bonding state and spatial distribution of the modifier in the material. The method is deployed in a spectrum detection sensor, and high-sensitivity and high-reliability detection and analysis of low-content or complex-distribution modified components in the modified ultra-high molecular plastic can be effectively improved.