The single-pixel imaging(SPI) technique is able to capture two-dimensional(2 D) images without conventional array sensors by using a photodiode. As a novel scheme, Fourier single-pixel imaging(FSI) has been proven cap...The single-pixel imaging(SPI) technique is able to capture two-dimensional(2 D) images without conventional array sensors by using a photodiode. As a novel scheme, Fourier single-pixel imaging(FSI) has been proven capable of reconstructing high-quality images. Due to the fact that the Fourier basis patterns(also known as grayscale sinusoidal patterns)cannot be well displayed on the digital micromirror device(DMD), a fast FSI system is proposed to solve this problem by binarizing Fourier pattern through a dithering algorithm. However, the traditional dithering algorithm leads to low quality as the extra noise is inevitably induced in the reconstructed images. In this paper, we report a better dithering algorithm to binarize Fourier pattern, which utilizes the Sierra–Lite kernel function by a serpentine scanning method. Numerical simulation and experiment demonstrate that the proposed algorithm is able to achieve higher quality under different sampling ratios.展开更多
In the imaging observation system, imaging task scheduling is an important topic. Most scholars study the imaging task scheduling from the perspective of static priority, and only a few from the perspective of dynamic...In the imaging observation system, imaging task scheduling is an important topic. Most scholars study the imaging task scheduling from the perspective of static priority, and only a few from the perspective of dynamic priority. However,the priority of the imaging task is dynamic in actual engineering. To supplement the research on imaging observation, this paper proposes the task priority model, dynamic scheduling strategy and Heuristic algorithm. At first, this paper analyzes the relevant theoretical basis of imaging observation, decomposes the task priority into four parts, including target priority, imaging task priority, track, telemetry & control(TT&C)requirement priority and data transmission requirement priority, summarizes the attribute factors that affect the above four types of priority in detail, and designs the corresponding priority model. Then, this paper takes the emergency tasks scheduling problem as the background, proposes the dynamic scheduling strategy and heuristic algorithm. Finally, the task priority model,dynamic scheduling strategy and heuristic algorithm are verified by experiments.展开更多
Full matrix focusing method of ultrasonic phased array has been proved with advantages of good signal-to-noise ratio and imaging resolution in the field of Ultrasonic NDT.However,it is still suffering from the time-co...Full matrix focusing method of ultrasonic phased array has been proved with advantages of good signal-to-noise ratio and imaging resolution in the field of Ultrasonic NDT.However,it is still suffering from the time-consuming data acquisition and processing.In order to solve the problem,two simplified matrix focusing methods are provided in the paper.One provided method is a triangular matrix focusing algorithm based on the principle of reciprocity for the multi-channel ultrasonic system.The other provided method is a trapezoidal matrix focusing algorithm based on the energy weight of the different channel to the focusing area.Time of data acquisition and computational is decreased with the provided simplified matrix focusing methods.In order to prove the validity of two provided algorithms,both side-drilled holes and oblique cracks are used for imaging experiments.The experimental results show that the imaging quality of the triangular matrix focusing algorithm is basically consistent to that of the full matrix focusing method.And imaging quality of the trapezoidal matrix focusing algorithm is slightly reduced with the amount of multi-channel data decreasing.Both data acquisition and computational efficiency using the triangular matrix focusing algorithm and the trapezoidal matrix focusing algorithm have been improved significantly compared with original full matrix focusing method.展开更多
At present,both the point source and the imaging polarization navigation devices only can output the angle information,which means that the velocity information of the carrier cannot be extracted from the polarization...At present,both the point source and the imaging polarization navigation devices only can output the angle information,which means that the velocity information of the carrier cannot be extracted from the polarization field pattern directly.Optical flow is an image-based method for calculating the velocity of pixel point movement in an image.However,for ordinary optical flow,the difference in pixel value as well as the calculation accuracy can be reduced in weak light.Polarization imaging technology has the ability to improve both the detection accuracy and the recognition probability of the target because it can acquire the extra polarization multi-dimensional information of target radiation or reflection.In this paper,combining the polarization imaging technique with the traditional optical flow algorithm,a polarization optical flow algorithm is proposed,and it is verified that the polarized optical flow algorithm has good adaptation in weak light and can improve the application range of polarization navigation sensors.This research lays the foundation for day and night all-weather polarization navigation applications in future.展开更多
Towards efficient implementation of x-ray ghost imaging(XGI),efficient data acquisition and fast image reconstruction together with high image quality are preferred.In view of radiation dose resulted from the incident...Towards efficient implementation of x-ray ghost imaging(XGI),efficient data acquisition and fast image reconstruction together with high image quality are preferred.In view of radiation dose resulted from the incident x-rays,fewer measurements with sufficient signal-to-noise ratio(SNR)are always anticipated.Available methods based on linear and compressive sensing algorithms cannot meet all the requirements simultaneously.In this paper,a method based on a modified compressive sensing algorithm with conjugate gradient descent method(CGDGI)is developed to solve the problems encountered in available XGI methods.Simulation and experiments demonstrate the practicability of CGDGI-based method for the efficient implementation of XGI.The image reconstruction time of sub-second implicates that the proposed method has the potential for real-time XGI.展开更多
Coherent diffractive imaging （CDI） is a lensless imaging technique and can achieve a resolution beyond the Rayleigh or Abbe limit. The ptychographical iterative engine （PIE） is a CDI phase retrieval algorithm that...Coherent diffractive imaging （CDI） is a lensless imaging technique and can achieve a resolution beyond the Rayleigh or Abbe limit. The ptychographical iterative engine （PIE） is a CDI phase retrieval algorithm that uses multiple diffraction patterns obtained through the scan of a localized illumination on the specimen, which has been demonstrated successfully at optical and X-ray wavelengths. In this paper, a general PIE algorithm （gPIE） is presented and demonstrated with an He-Ne laser light diffraction dataset. This algorithm not only permits the removal of the accurate model of the illumination function in PIE, but also provides improved convergence speed and retrieval quality.展开更多
An improved"three steps"mountain-climb searching(MCS)algorithm is proposed which is applied to auto-focusing for microscopic imaging accurately and efficiently.By analyzing the performance of several evaluat...An improved"three steps"mountain-climb searching(MCS)algorithm is proposed which is applied to auto-focusing for microscopic imaging accurately and efficiently.By analyzing the performance of several evaluation functions,the variance function and the Brenner function are synthesized as a new evaluation function.In the first step,a self-adaptive step length which is much dependent on the reciprocal of the evaluation function value at the beginning position of climbing is used for approaching the halfway up the mountain roughly.Secondly,a fixed moderate step length is applied for approaching the mountaintop of the variance function as closer as possible.Finally,afine step is employed for reaching the exact mountaintop of the Brenner function.The microscope auto-focusing experiments based on the proposed algorithm for blood smear detection have been carried out comprehensively.The results show that the improved algorithm can not only guarantee the precision to get clear focal images,but also improve the auto-focusing e±ciency.展开更多
In this study,eight different varieties of maize seeds were used as the research objects.Conduct 81 types of combined preprocessing on the original spectra.Through comparison,Savitzky-Golay(SG)-multivariate scattering...In this study,eight different varieties of maize seeds were used as the research objects.Conduct 81 types of combined preprocessing on the original spectra.Through comparison,Savitzky-Golay(SG)-multivariate scattering correction(MSC)-maximum-minimum normalization(MN)was identified as the optimal preprocessing technique.The competitive adaptive reweighted sampling(CARS),successive projections algorithm(SPA),and their combined methods were employed to extract feature wavelengths.Classification models based on back propagation(BP),support vector machine(SVM),random forest(RF),and partial least squares(PLS)were established using full-band data and feature wavelengths.Among all models,the(CARS-SPA)-BP model achieved the highest accuracy rate of 98.44%.This study offers novel insights and methodologies for the rapid and accurate identification of corn seeds as well as other crop seeds.展开更多
A millimeter-wave linear frequency modulated continuous wave(LFM CW)radar is applied to water surface detection.This paper presents the experiment and imaging algorithm.In imaging processing,water surface texture can ...A millimeter-wave linear frequency modulated continuous wave(LFM CW)radar is applied to water surface detection.This paper presents the experiment and imaging algorithm.In imaging processing,water surface texture can hardly be seen in the results obtained by traditional imaging algorithm.To solve this problem,we propose a millimeter-wave LFMCW radar imaging algorithm for water surface texture.Different from the traditional imaging algorithm,the proposed imaging algorithm includes two improvements as follows:Firstly,the interference from static targets is removed through a frequency domainfilter;Secondly,the multiplicative noises are reduced by the maximum likelihood estimation method,which is used to estimatethe azimuth spectrum parameters to calculate the energy of water surface echo.Final results show that the proposed algorithmcan obtain water surface texture,which means that the proposed algorithm is superior to the traditional imaging algorithm.展开更多
This letter presents a novel approach for the Synthetic Aperture Radar (SAR) stereo imaging based on the Capon spectrum estimation technique. In order to deal with nonuniform sampling space and lead to super resolutio...This letter presents a novel approach for the Synthetic Aperture Radar (SAR) stereo imaging based on the Capon spectrum estimation technique. In order to deal with nonuniform sampling space and lead to super resolution in the elevation direction, Capon approach is used to focus the SAR data on a certain height. Results obtained on simulated data demonstrate the feasibility of the Capon based algorithm. Compared with the classical Fast Fourier Transform (FFT), the Capon based algo-rithm shows better resolution quality.展开更多
Brain tumor segmentation from Magnetic Resonance Imaging(MRI)supports neurologists and radiologists in analyzing tumors and developing personalized treatment plans,making it a crucial yet challenging task.Supervised m...Brain tumor segmentation from Magnetic Resonance Imaging(MRI)supports neurologists and radiologists in analyzing tumors and developing personalized treatment plans,making it a crucial yet challenging task.Supervised models such as 3D U-Net perform well in this domain,but their accuracy significantly improves with appropriate preprocessing.This paper demonstrates the effectiveness of preprocessing in brain tumor segmentation by applying a pre-segmentation step based on the Generalized Gaussian Mixture Model(GGMM)to T1 contrastenhanced MRI scans from the BraTS 2020 dataset.The Expectation-Maximization(EM)algorithm is employed to estimate parameters for four tissue classes,generating a new pre-segmented channel that enhances the training and performance of the 3DU-Net model.The proposed GGMM+3D U-Net framework achieved a Dice coefficient of 0.88 for whole tumor segmentation,outperforming both the standard multiscale 3D U-Net(0.84)and MMU-Net(0.85).It also delivered higher Intersection over Union(IoU)scores compared to models trained without preprocessing or with simpler GMM-based segmentation.These results,supported by qualitative visualizations,suggest that GGMM-based preprocessing should be integrated into brain tumor segmentation pipelines to optimize performance.展开更多
Color Fourier single-pixel imaging(FSI)enables efficient spectral and spatial imaging.Here,we propose a Fourier single-pixel imaging scheme with a random color filter array(FSI-RCFA).The proposed method employs a rand...Color Fourier single-pixel imaging(FSI)enables efficient spectral and spatial imaging.Here,we propose a Fourier single-pixel imaging scheme with a random color filter array(FSI-RCFA).The proposed method employs a random color filter array(RCFA)to modulate Fourier patterns.A three-step phase-shifting technique reconstructs the Fourier spectrum,followed by an RCFA-based demosaicing algorithm to recover color images.Compared to traditional color FSI based on Bayer color filter array schemes(FSI-BCFA),our approach achieves superior separation between chrominance and luminance components in the frequency domain.Simulation results demonstrate that the FSI-RCFA method achieves a lower mean squared error(MSE),a higher peak signal-to-noise ratio(PSNR),and superior noise resistance compared to FSI-BCFA,while enabling direct single-channel pixel measurements for targeted applications such as agricultural defect detection.展开更多
Image enhancement utilizes intensity transformation functions to maximize the information content of enhanced images.This paper approaches the topic as an optimization problem and uses the bald eagle search(BES)algori...Image enhancement utilizes intensity transformation functions to maximize the information content of enhanced images.This paper approaches the topic as an optimization problem and uses the bald eagle search(BES)algorithm to achieve optimal results.In our proposed model,gamma correction and Retinex address color cast issues and enhance image edges and details.The final enhanced image is obtained through color balancing.The BES algorithm seeks the optimal solution through the selection,search,and swooping stages.However,it is prone to getting stuck in local optima and converges slowly.To overcome these limitations,we propose an improved BES algorithm(ABES)with enhanced population learning,position updates,and control parameters.ABES is employed to optimize the core parameters of gamma correction and Retinex to improve image quality,and the maximization of information entropy is utilized as the objective function.Real benchmark images are collected to validate its performance.Experimental results demonstrate that ABES outperforms the existing image enhancement methods,including the flower pollination algorithm,the chimp optimization algorithm,particle swarm optimization,and BES,in terms of information entropy,peak signal-to-noise ratio(PSNR),structural similarity index(SSIM),and patch-based contrast quality index(PCQI).ABES demonstrates superior performance both qualitatively and quantitatively,and it helps enhance prominent features and contrast in the images while maintaining the natural appearance of the original images.展开更多
Background:Quantum-enhanced medical imaging algorithms–quantum entanglement reconstruction,quantum noise suppression,and quantum beamforming–propose possible remedies for significant constraints in traditional diagn...Background:Quantum-enhanced medical imaging algorithms–quantum entanglement reconstruction,quantum noise suppression,and quantum beamforming–propose possible remedies for significant constraints in traditional diagnostic imaging,such as resolution,radiation efficiency,and real-time processing.Methods:This work used a mixed-methods strategy,including controlled phantom experiments,retrospective multi-center clinical data analysis,and quantum-classical hybrid processing to assess enhancements in resolution,dosage efficiency,and diagnostic confidence.Statistical validation included analysis of variance(ANOVA)and receiver-operating characteristic curve analysis,juxtaposing quantum-enhanced methodologies with conventional and deep learning approaches.Results:Quantum entanglement reconstruction enhanced magnetic resonance imaging spatial resolution by 33.2%(P<0.01),quantum noise suppression facilitated computed tomography scans with a 60%reduction in radiation,and quantum beamforming improved ultrasound contrast by 27%while preserving real-time processing(<2 ms delay).Inter-reader variability(12%in Diagnostic Confidence Scores)showed that systematic training is needed,even if the performance was better.The research presented(1)a reusable clinical quantum imaging framework,(2)enhanced hardware processes(field-programmable gate array/graphics processing unit acceleration),and(3)cost-benefit analyses demonstrating a 22-month return on investment breakeven point.Conclusion:Quantum-enhanced imaging has a lot of promise for use in medicine,especially in neurology and cancer.Future research should focus on multi-modal integration(e.g.,positron emission tomography–magnetic resonance imaging),cloud-based quantum simulations for enhanced accessibility,and extensive trials to confirm long-term diagnostic accuracy.This breakthrough gives healthcare systems a technology roadmap and a reason to spend money on quantum-enhanced diagnostics.展开更多
Non-line-of-sight imaging recovers hidden objects around the corner by analyzing the diffuse reflection light on the relay surface that carries hidden scene information.Due to its huge application potential in the fie...Non-line-of-sight imaging recovers hidden objects around the corner by analyzing the diffuse reflection light on the relay surface that carries hidden scene information.Due to its huge application potential in the fields of autonomous driving,defense,medical imaging,and post-disaster rescue,non-line-of-sight imaging has attracted considerable attention from researchers at home and abroad,especially in recent years.The research on non-line-of-sight imaging primarily focuses on imaging systems,forward models,and reconstruction algorithms.This paper systematically summarizes the existing non-line-of-sight imaging technology in both active and passive scenes,and analyzes the challenges and future directions of non-line-of-sight imaging technology.展开更多
Aimed at the long and narrow geometric features and poor generalization ability of the damage detection in conveyor belts with steel rope cores using the X-ray image,a detection method of damage X-ray image is propose...Aimed at the long and narrow geometric features and poor generalization ability of the damage detection in conveyor belts with steel rope cores using the X-ray image,a detection method of damage X-ray image is proposed based on the improved fully convolutional one-stage object detection(FCOS)algorithm.The regression performance of bounding boxes was optimized by introducing the complete intersection over union loss function into the improved algorithm.The feature fusion network structure is modified by adding adaptive fusion paths to the feature fusion network structure,which makes full use of the features of accurate localization and semantics of multi-scale feature fusion networks.Finally,the network structure was trained and validated by using the X-ray image dataset of damages in conveyor belts with steel rope cores provided by a flaw detection equipment manufacturer.In addition,the data enhancement methods such as rotating,mirroring,and scaling,were employed to enrich the image dataset so that the model is adequately trained.Experimental results showed that the improved FCOS algorithm promoted the precision rate and the recall rate by 20.9%and 14.8%respectively,compared with the original algorithm.Meanwhile,compared with Fast R-CNN,Faster R-CNN,SSD,and YOLOv3,the improved FCOS algorithm has obvious advantages;detection precision rate and recall rate of the modified network reached 95.8%and 97.0%respectively.Furthermore,it demonstrated a higher detection accuracy without affecting the speed.The results of this work have some reference significance for the automatic identification and detection of steel core conveyor belt damage.展开更多
The numerical dispersion phenomenon in the finite-difference forward modeling simulations of the wave equation significantly affects the imaging accuracy in acoustic reflection logging.This issue is particularly prono...The numerical dispersion phenomenon in the finite-difference forward modeling simulations of the wave equation significantly affects the imaging accuracy in acoustic reflection logging.This issue is particularly pronounced in the reverse time migration(RTM)method used for shear-wave(S-wave)logging imaging.This not only affects imaging accuracy but also introduces ambiguities in the interpretation of logging results.To address this challenge,this study proposes the use of a least-squares difference coefficient optimization algorithm aiming to suppress the numerical dispersion phenomenon in the RTM of S-wave reflection imaging logging.By optimizing the difference coefficients,the high-precision finite-difference algorithm serves as an effective operator for both forward and backward RTM processes.This approach is instrumental in eliminating migration illusions,which are often caused by numerical dispersion.The effectiveness of this optimized algorithm is demonstrated through numerical results,which indicate that it can achieve more accurate forward imaging results across various conditions,including high-and low-velocity strata,and is effective in both large and small spatial grids.The results of processing real data demonstrate that numerical dispersion optimization effectively reduces migration artifacts and diminishes ambiguities in logging interpretations.This optimization offers crucial technical support to the RTM method,enhancing its capability for accurately modeling and imaging S-wave reflections.展开更多
BACKGROUND Despite the promising prospects of using artificial intelligence and machine learning(ML)for disease classification and prediction purposes,the complexity and lack of explainability of this method make it d...BACKGROUND Despite the promising prospects of using artificial intelligence and machine learning(ML)for disease classification and prediction purposes,the complexity and lack of explainability of this method make it difficult to apply the constructed models in clinical practice.We developed and validated an interpretable ML model based on magnetic resonance imaging(MRI)radiomics and clinical features for the preoperative prediction of the pathological grades of hepatocellular carcinomas(HCCs).This model will help clinicians better understand the situation and develop personalized treatment plans.AIM To develop and validate an interpretable ML model for preoperative pathological grade prediction in HCC patients via a combination of multisequence MRI radiomics and clinical features.METHODS MRI and clinical data derived from 125 patients with HCCs confirmed by postoperative pathological examinations were retrospectively analyzed.The patients were randomly split into training and validation groups(7:3 ratio).Univariate and multivariate logistic regression analyses were performed to identify independent clinical predictors.The tumor lesions observed on axial fatsuppressed T2-weighted imaging(FS-T2WI),arterial phase(AP),and portal venous phase(PVP)images were delineated in a slice-by-slice manner using 3D-slicer to generate volumetric regions of interest,and radiomic features were extracted.Interclass correlation coefficients were calculated,and least absolute selection and shrinkage operator regression were conducted for feature selection purposes.Six predictive models were subsequently developed for pathological grade prediction:FS-T2WI,AP,PVP,integrated radiomics,clinical,and combined radiomics-clinical(RC)models.The effectiveness of these models was assessed by calculating their area under the receiver operating characteristic curve(AUC)values.The clinical applicability of the models was evaluated via decision curve analysis.Finally,the contributions of the different features contained in the model with optimal performance were interpreted via a SHapley Additive exPlanations analysis.RESULTS Among the 125 patients,87 were assigned to the training group,and 38 were assigned to the validation group.The maximum tumor diameter,hepatitis B virus status,and monocyte count were identified as independent predictors of pathological grade.Twelve optimal radiomic features were ultimately selected.The AUC values obtained for the FS-T2WI model,AP model,PVP model,radiomics model,clinical model,and combined RC model in the training group were 0.761[95%confidence interval(CI):0.562-0.857],0.870(95%CI:0.714-0.918),0.868(95%CI:0.714-0.959),0.917(95%CI:0.857-0.959),0.869(95%CI:0.643-0.973),and 0.941(95%CI:0.857-0.945),respectively;in the validation group,the AUC values were 0.724(95%CI:0.625-0.833),0.802(95%CI:0.686-1.000),0.797(95%CI:0.688-1.000),0.901(95%CI:0.833-0.906),0.865(95%CI:0.594-1.000),and 0.932(95%CI:0.812-1.000),respectively.The combined RC model demonstrated the best performance.Additionally,the decision curve analysis revealed that the combined RC model had satisfactory prediction efficiency,and the SHapley Additive exPlanations value analysis revealed that the“FS-T2WI-wavelet-HLL_gldm_Large Dependence High Gray Level Emphasis”feature contributed the most to the model,exhibiting a positive effect.CONCLUSION An interpretable ML model based on MRI radiomics provides a noninvasive tool for predicting the pathological grade of HCCs,which will help clinicians develop personalized treatment plans.展开更多
Snow cover plays a critical role in global climate regulation and hydrological processes.Accurate monitoring is essential for understanding snow distribution patterns,managing water resources,and assessing the impacts...Snow cover plays a critical role in global climate regulation and hydrological processes.Accurate monitoring is essential for understanding snow distribution patterns,managing water resources,and assessing the impacts of climate change.Remote sensing has become a vital tool for snow monitoring,with the widely used Moderate-resolution Imaging Spectroradiometer(MODIS)snow products from the Terra and Aqua satellites.However,cloud cover often interferes with snow detection,making cloud removal techniques crucial for reliable snow product generation.This study evaluated the accuracy of four MODIS snow cover datasets generated through different cloud removal algorithms.Using real-time field camera observations from four stations in the Tianshan Mountains,China,this study assessed the performance of these datasets during three distinct snow periods:the snow accumulation period(September-November),snowmelt period(March-June),and stable snow period(December-February in the following year).The findings showed that cloud-free snow products generated using the Hidden Markov Random Field(HMRF)algorithm consistently outperformed the others,particularly under cloud cover,while cloud-free snow products using near-day synthesis and the spatiotemporal adaptive fusion method with error correction(STAR)demonstrated varying performance depending on terrain complexity and cloud conditions.This study highlighted the importance of considering terrain features,land cover types,and snow dynamics when selecting cloud removal methods,particularly in areas with rapid snow accumulation and melting.The results suggested that future research should focus on improving cloud removal algorithms through the integration of machine learning,multi-source data fusion,and advanced remote sensing technologies.By expanding validation efforts and refining cloud removal strategies,more accurate and reliable snow products can be developed,contributing to enhanced snow monitoring and better management of water resources in alpine and arid areas.展开更多
It has long been realized that the problem of radar imaging is a special case of image reconstruction in which the data are incomplete and noisy. In other fields, iterative reconstruction algorithms have been used suc...It has long been realized that the problem of radar imaging is a special case of image reconstruction in which the data are incomplete and noisy. In other fields, iterative reconstruction algorithms have been used successfully to improve the image quality. This paper studies the application of iterative algorithms in radar imaging. A discrete model is first derived, and the iterative algorithms are then adapted to radar imaging. Although such algorithms are usually time consuming, this paper shows that, if the algorithms are appropriately simplified, it is possible to realize them even in real time. The efficiency of iterative algorithms is shown through computer simulations.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.61271376)the Anhui Provincial Natural Science Foundation,China(Grant No.1208085MF114)
文摘The single-pixel imaging(SPI) technique is able to capture two-dimensional(2 D) images without conventional array sensors by using a photodiode. As a novel scheme, Fourier single-pixel imaging(FSI) has been proven capable of reconstructing high-quality images. Due to the fact that the Fourier basis patterns(also known as grayscale sinusoidal patterns)cannot be well displayed on the digital micromirror device(DMD), a fast FSI system is proposed to solve this problem by binarizing Fourier pattern through a dithering algorithm. However, the traditional dithering algorithm leads to low quality as the extra noise is inevitably induced in the reconstructed images. In this paper, we report a better dithering algorithm to binarize Fourier pattern, which utilizes the Sierra–Lite kernel function by a serpentine scanning method. Numerical simulation and experiment demonstrate that the proposed algorithm is able to achieve higher quality under different sampling ratios.
基金supported by the National Natural Science Foundation of China(61773120,61473301,71501180,71501179,61603400)。
文摘In the imaging observation system, imaging task scheduling is an important topic. Most scholars study the imaging task scheduling from the perspective of static priority, and only a few from the perspective of dynamic priority. However,the priority of the imaging task is dynamic in actual engineering. To supplement the research on imaging observation, this paper proposes the task priority model, dynamic scheduling strategy and Heuristic algorithm. At first, this paper analyzes the relevant theoretical basis of imaging observation, decomposes the task priority into four parts, including target priority, imaging task priority, track, telemetry & control(TT&C)requirement priority and data transmission requirement priority, summarizes the attribute factors that affect the above four types of priority in detail, and designs the corresponding priority model. Then, this paper takes the emergency tasks scheduling problem as the background, proposes the dynamic scheduling strategy and heuristic algorithm. Finally, the task priority model,dynamic scheduling strategy and heuristic algorithm are verified by experiments.
基金Supported by the National Natural Science Foundation of China(Grant No.51905070).
文摘Full matrix focusing method of ultrasonic phased array has been proved with advantages of good signal-to-noise ratio and imaging resolution in the field of Ultrasonic NDT.However,it is still suffering from the time-consuming data acquisition and processing.In order to solve the problem,two simplified matrix focusing methods are provided in the paper.One provided method is a triangular matrix focusing algorithm based on the principle of reciprocity for the multi-channel ultrasonic system.The other provided method is a trapezoidal matrix focusing algorithm based on the energy weight of the different channel to the focusing area.Time of data acquisition and computational is decreased with the provided simplified matrix focusing methods.In order to prove the validity of two provided algorithms,both side-drilled holes and oblique cracks are used for imaging experiments.The experimental results show that the imaging quality of the triangular matrix focusing algorithm is basically consistent to that of the full matrix focusing method.And imaging quality of the trapezoidal matrix focusing algorithm is slightly reduced with the amount of multi-channel data decreasing.Both data acquisition and computational efficiency using the triangular matrix focusing algorithm and the trapezoidal matrix focusing algorithm have been improved significantly compared with original full matrix focusing method.
基金supported by the National Natural Science Foundation of China(Nos.51675076 and 51505062)the Science Fund for Creative Research Groups of NSFC(No.51621064)the Basic scientific research fees for Central Universities(Nos.DUT17GF109 and DUT16TD20)
文摘At present,both the point source and the imaging polarization navigation devices only can output the angle information,which means that the velocity information of the carrier cannot be extracted from the polarization field pattern directly.Optical flow is an image-based method for calculating the velocity of pixel point movement in an image.However,for ordinary optical flow,the difference in pixel value as well as the calculation accuracy can be reduced in weak light.Polarization imaging technology has the ability to improve both the detection accuracy and the recognition probability of the target because it can acquire the extra polarization multi-dimensional information of target radiation or reflection.In this paper,combining the polarization imaging technique with the traditional optical flow algorithm,a polarization optical flow algorithm is proposed,and it is verified that the polarized optical flow algorithm has good adaptation in weak light and can improve the application range of polarization navigation sensors.This research lays the foundation for day and night all-weather polarization navigation applications in future.
基金supported by the National Key Research and Development Program of China(Grant Nos.2017YFA0206004,2017YFA0206002,2018YFC0206002,and 2017YFA0403801)National Natural Science Foundation of China(Grant No.81430087)。
文摘Towards efficient implementation of x-ray ghost imaging(XGI),efficient data acquisition and fast image reconstruction together with high image quality are preferred.In view of radiation dose resulted from the incident x-rays,fewer measurements with sufficient signal-to-noise ratio(SNR)are always anticipated.Available methods based on linear and compressive sensing algorithms cannot meet all the requirements simultaneously.In this paper,a method based on a modified compressive sensing algorithm with conjugate gradient descent method(CGDGI)is developed to solve the problems encountered in available XGI methods.Simulation and experiments demonstrate the practicability of CGDGI-based method for the efficient implementation of XGI.The image reconstruction time of sub-second implicates that the proposed method has the potential for real-time XGI.
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 11179009 and 50875013)the Beijing Municipal Natural Science Foundation, China (Grant No. 4102036)the Beijing NOVA Program, China (Grant No. 2009A09)
文摘Coherent diffractive imaging （CDI） is a lensless imaging technique and can achieve a resolution beyond the Rayleigh or Abbe limit. The ptychographical iterative engine （PIE） is a CDI phase retrieval algorithm that uses multiple diffraction patterns obtained through the scan of a localized illumination on the specimen, which has been demonstrated successfully at optical and X-ray wavelengths. In this paper, a general PIE algorithm （gPIE） is presented and demonstrated with an He-Ne laser light diffraction dataset. This algorithm not only permits the removal of the accurate model of the illumination function in PIE, but also provides improved convergence speed and retrieval quality.
基金This work is supported by 863 National Plan Foundation of China under grant No.2007 AA01Z333.
文摘An improved"three steps"mountain-climb searching(MCS)algorithm is proposed which is applied to auto-focusing for microscopic imaging accurately and efficiently.By analyzing the performance of several evaluation functions,the variance function and the Brenner function are synthesized as a new evaluation function.In the first step,a self-adaptive step length which is much dependent on the reciprocal of the evaluation function value at the beginning position of climbing is used for approaching the halfway up the mountain roughly.Secondly,a fixed moderate step length is applied for approaching the mountaintop of the variance function as closer as possible.Finally,afine step is employed for reaching the exact mountaintop of the Brenner function.The microscope auto-focusing experiments based on the proposed algorithm for blood smear detection have been carried out comprehensively.The results show that the improved algorithm can not only guarantee the precision to get clear focal images,but also improve the auto-focusing e±ciency.
基金supported by the Science and Technology Development Plan Project of Jilin Provincial Department of Science and Technology (No.20220203112S)the Jilin Provincial Department of Education Science and Technology Research Project (No.JJKH20210039KJ)。
文摘In this study,eight different varieties of maize seeds were used as the research objects.Conduct 81 types of combined preprocessing on the original spectra.Through comparison,Savitzky-Golay(SG)-multivariate scattering correction(MSC)-maximum-minimum normalization(MN)was identified as the optimal preprocessing technique.The competitive adaptive reweighted sampling(CARS),successive projections algorithm(SPA),and their combined methods were employed to extract feature wavelengths.Classification models based on back propagation(BP),support vector machine(SVM),random forest(RF),and partial least squares(PLS)were established using full-band data and feature wavelengths.Among all models,the(CARS-SPA)-BP model achieved the highest accuracy rate of 98.44%.This study offers novel insights and methodologies for the rapid and accurate identification of corn seeds as well as other crop seeds.
文摘A millimeter-wave linear frequency modulated continuous wave(LFM CW)radar is applied to water surface detection.This paper presents the experiment and imaging algorithm.In imaging processing,water surface texture can hardly be seen in the results obtained by traditional imaging algorithm.To solve this problem,we propose a millimeter-wave LFMCW radar imaging algorithm for water surface texture.Different from the traditional imaging algorithm,the proposed imaging algorithm includes two improvements as follows:Firstly,the interference from static targets is removed through a frequency domainfilter;Secondly,the multiplicative noises are reduced by the maximum likelihood estimation method,which is used to estimatethe azimuth spectrum parameters to calculate the energy of water surface echo.Final results show that the proposed algorithmcan obtain water surface texture,which means that the proposed algorithm is superior to the traditional imaging algorithm.
文摘This letter presents a novel approach for the Synthetic Aperture Radar (SAR) stereo imaging based on the Capon spectrum estimation technique. In order to deal with nonuniform sampling space and lead to super resolution in the elevation direction, Capon approach is used to focus the SAR data on a certain height. Results obtained on simulated data demonstrate the feasibility of the Capon based algorithm. Compared with the classical Fast Fourier Transform (FFT), the Capon based algo-rithm shows better resolution quality.
基金Princess Nourah Bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R826),Princess Nourah Bint Abdulrahman University,Riyadh,Saudi ArabiaNorthern Border University,Saudi Arabia,for supporting this work through project number(NBU-CRP-2025-2933).
文摘Brain tumor segmentation from Magnetic Resonance Imaging(MRI)supports neurologists and radiologists in analyzing tumors and developing personalized treatment plans,making it a crucial yet challenging task.Supervised models such as 3D U-Net perform well in this domain,but their accuracy significantly improves with appropriate preprocessing.This paper demonstrates the effectiveness of preprocessing in brain tumor segmentation by applying a pre-segmentation step based on the Generalized Gaussian Mixture Model(GGMM)to T1 contrastenhanced MRI scans from the BraTS 2020 dataset.The Expectation-Maximization(EM)algorithm is employed to estimate parameters for four tissue classes,generating a new pre-segmented channel that enhances the training and performance of the 3DU-Net model.The proposed GGMM+3D U-Net framework achieved a Dice coefficient of 0.88 for whole tumor segmentation,outperforming both the standard multiscale 3D U-Net(0.84)and MMU-Net(0.85).It also delivered higher Intersection over Union(IoU)scores compared to models trained without preprocessing or with simpler GMM-based segmentation.These results,supported by qualitative visualizations,suggest that GGMM-based preprocessing should be integrated into brain tumor segmentation pipelines to optimize performance.
基金supported by the National Natural Science Foundation of China(Grant Nos.62001249 and62375140)。
文摘Color Fourier single-pixel imaging(FSI)enables efficient spectral and spatial imaging.Here,we propose a Fourier single-pixel imaging scheme with a random color filter array(FSI-RCFA).The proposed method employs a random color filter array(RCFA)to modulate Fourier patterns.A three-step phase-shifting technique reconstructs the Fourier spectrum,followed by an RCFA-based demosaicing algorithm to recover color images.Compared to traditional color FSI based on Bayer color filter array schemes(FSI-BCFA),our approach achieves superior separation between chrominance and luminance components in the frequency domain.Simulation results demonstrate that the FSI-RCFA method achieves a lower mean squared error(MSE),a higher peak signal-to-noise ratio(PSNR),and superior noise resistance compared to FSI-BCFA,while enabling direct single-channel pixel measurements for targeted applications such as agricultural defect detection.
基金supported by the Research on theKey Technology of Damage Identification Method of Dam Concrete Structure based on Transformer Image Processing(242102521031)the project Research on Situational Awareness and Behavior Anomaly Prediction of Social Media Based on Multimodal Time Series Graph(232102520004)Key Scientific Research Project of Higher Education Institutions in Henan Province(25B520019).
文摘Image enhancement utilizes intensity transformation functions to maximize the information content of enhanced images.This paper approaches the topic as an optimization problem and uses the bald eagle search(BES)algorithm to achieve optimal results.In our proposed model,gamma correction and Retinex address color cast issues and enhance image edges and details.The final enhanced image is obtained through color balancing.The BES algorithm seeks the optimal solution through the selection,search,and swooping stages.However,it is prone to getting stuck in local optima and converges slowly.To overcome these limitations,we propose an improved BES algorithm(ABES)with enhanced population learning,position updates,and control parameters.ABES is employed to optimize the core parameters of gamma correction and Retinex to improve image quality,and the maximization of information entropy is utilized as the objective function.Real benchmark images are collected to validate its performance.Experimental results demonstrate that ABES outperforms the existing image enhancement methods,including the flower pollination algorithm,the chimp optimization algorithm,particle swarm optimization,and BES,in terms of information entropy,peak signal-to-noise ratio(PSNR),structural similarity index(SSIM),and patch-based contrast quality index(PCQI).ABES demonstrates superior performance both qualitatively and quantitatively,and it helps enhance prominent features and contrast in the images while maintaining the natural appearance of the original images.
文摘Background:Quantum-enhanced medical imaging algorithms–quantum entanglement reconstruction,quantum noise suppression,and quantum beamforming–propose possible remedies for significant constraints in traditional diagnostic imaging,such as resolution,radiation efficiency,and real-time processing.Methods:This work used a mixed-methods strategy,including controlled phantom experiments,retrospective multi-center clinical data analysis,and quantum-classical hybrid processing to assess enhancements in resolution,dosage efficiency,and diagnostic confidence.Statistical validation included analysis of variance(ANOVA)and receiver-operating characteristic curve analysis,juxtaposing quantum-enhanced methodologies with conventional and deep learning approaches.Results:Quantum entanglement reconstruction enhanced magnetic resonance imaging spatial resolution by 33.2%(P<0.01),quantum noise suppression facilitated computed tomography scans with a 60%reduction in radiation,and quantum beamforming improved ultrasound contrast by 27%while preserving real-time processing(<2 ms delay).Inter-reader variability(12%in Diagnostic Confidence Scores)showed that systematic training is needed,even if the performance was better.The research presented(1)a reusable clinical quantum imaging framework,(2)enhanced hardware processes(field-programmable gate array/graphics processing unit acceleration),and(3)cost-benefit analyses demonstrating a 22-month return on investment breakeven point.Conclusion:Quantum-enhanced imaging has a lot of promise for use in medicine,especially in neurology and cancer.Future research should focus on multi-modal integration(e.g.,positron emission tomography–magnetic resonance imaging),cloud-based quantum simulations for enhanced accessibility,and extensive trials to confirm long-term diagnostic accuracy.This breakthrough gives healthcare systems a technology roadmap and a reason to spend money on quantum-enhanced diagnostics.
基金the National Natural Science Foundation of China(No.62272421)。
文摘Non-line-of-sight imaging recovers hidden objects around the corner by analyzing the diffuse reflection light on the relay surface that carries hidden scene information.Due to its huge application potential in the fields of autonomous driving,defense,medical imaging,and post-disaster rescue,non-line-of-sight imaging has attracted considerable attention from researchers at home and abroad,especially in recent years.The research on non-line-of-sight imaging primarily focuses on imaging systems,forward models,and reconstruction algorithms.This paper systematically summarizes the existing non-line-of-sight imaging technology in both active and passive scenes,and analyzes the challenges and future directions of non-line-of-sight imaging technology.
文摘Aimed at the long and narrow geometric features and poor generalization ability of the damage detection in conveyor belts with steel rope cores using the X-ray image,a detection method of damage X-ray image is proposed based on the improved fully convolutional one-stage object detection(FCOS)algorithm.The regression performance of bounding boxes was optimized by introducing the complete intersection over union loss function into the improved algorithm.The feature fusion network structure is modified by adding adaptive fusion paths to the feature fusion network structure,which makes full use of the features of accurate localization and semantics of multi-scale feature fusion networks.Finally,the network structure was trained and validated by using the X-ray image dataset of damages in conveyor belts with steel rope cores provided by a flaw detection equipment manufacturer.In addition,the data enhancement methods such as rotating,mirroring,and scaling,were employed to enrich the image dataset so that the model is adequately trained.Experimental results showed that the improved FCOS algorithm promoted the precision rate and the recall rate by 20.9%and 14.8%respectively,compared with the original algorithm.Meanwhile,compared with Fast R-CNN,Faster R-CNN,SSD,and YOLOv3,the improved FCOS algorithm has obvious advantages;detection precision rate and recall rate of the modified network reached 95.8%and 97.0%respectively.Furthermore,it demonstrated a higher detection accuracy without affecting the speed.The results of this work have some reference significance for the automatic identification and detection of steel core conveyor belt damage.
基金supported by Scientific Research and Technology Development Project of CNPC(2021DJ4002,2022DJ3908).
文摘The numerical dispersion phenomenon in the finite-difference forward modeling simulations of the wave equation significantly affects the imaging accuracy in acoustic reflection logging.This issue is particularly pronounced in the reverse time migration(RTM)method used for shear-wave(S-wave)logging imaging.This not only affects imaging accuracy but also introduces ambiguities in the interpretation of logging results.To address this challenge,this study proposes the use of a least-squares difference coefficient optimization algorithm aiming to suppress the numerical dispersion phenomenon in the RTM of S-wave reflection imaging logging.By optimizing the difference coefficients,the high-precision finite-difference algorithm serves as an effective operator for both forward and backward RTM processes.This approach is instrumental in eliminating migration illusions,which are often caused by numerical dispersion.The effectiveness of this optimized algorithm is demonstrated through numerical results,which indicate that it can achieve more accurate forward imaging results across various conditions,including high-and low-velocity strata,and is effective in both large and small spatial grids.The results of processing real data demonstrate that numerical dispersion optimization effectively reduces migration artifacts and diminishes ambiguities in logging interpretations.This optimization offers crucial technical support to the RTM method,enhancing its capability for accurately modeling and imaging S-wave reflections.
文摘BACKGROUND Despite the promising prospects of using artificial intelligence and machine learning(ML)for disease classification and prediction purposes,the complexity and lack of explainability of this method make it difficult to apply the constructed models in clinical practice.We developed and validated an interpretable ML model based on magnetic resonance imaging(MRI)radiomics and clinical features for the preoperative prediction of the pathological grades of hepatocellular carcinomas(HCCs).This model will help clinicians better understand the situation and develop personalized treatment plans.AIM To develop and validate an interpretable ML model for preoperative pathological grade prediction in HCC patients via a combination of multisequence MRI radiomics and clinical features.METHODS MRI and clinical data derived from 125 patients with HCCs confirmed by postoperative pathological examinations were retrospectively analyzed.The patients were randomly split into training and validation groups(7:3 ratio).Univariate and multivariate logistic regression analyses were performed to identify independent clinical predictors.The tumor lesions observed on axial fatsuppressed T2-weighted imaging(FS-T2WI),arterial phase(AP),and portal venous phase(PVP)images were delineated in a slice-by-slice manner using 3D-slicer to generate volumetric regions of interest,and radiomic features were extracted.Interclass correlation coefficients were calculated,and least absolute selection and shrinkage operator regression were conducted for feature selection purposes.Six predictive models were subsequently developed for pathological grade prediction:FS-T2WI,AP,PVP,integrated radiomics,clinical,and combined radiomics-clinical(RC)models.The effectiveness of these models was assessed by calculating their area under the receiver operating characteristic curve(AUC)values.The clinical applicability of the models was evaluated via decision curve analysis.Finally,the contributions of the different features contained in the model with optimal performance were interpreted via a SHapley Additive exPlanations analysis.RESULTS Among the 125 patients,87 were assigned to the training group,and 38 were assigned to the validation group.The maximum tumor diameter,hepatitis B virus status,and monocyte count were identified as independent predictors of pathological grade.Twelve optimal radiomic features were ultimately selected.The AUC values obtained for the FS-T2WI model,AP model,PVP model,radiomics model,clinical model,and combined RC model in the training group were 0.761[95%confidence interval(CI):0.562-0.857],0.870(95%CI:0.714-0.918),0.868(95%CI:0.714-0.959),0.917(95%CI:0.857-0.959),0.869(95%CI:0.643-0.973),and 0.941(95%CI:0.857-0.945),respectively;in the validation group,the AUC values were 0.724(95%CI:0.625-0.833),0.802(95%CI:0.686-1.000),0.797(95%CI:0.688-1.000),0.901(95%CI:0.833-0.906),0.865(95%CI:0.594-1.000),and 0.932(95%CI:0.812-1.000),respectively.The combined RC model demonstrated the best performance.Additionally,the decision curve analysis revealed that the combined RC model had satisfactory prediction efficiency,and the SHapley Additive exPlanations value analysis revealed that the“FS-T2WI-wavelet-HLL_gldm_Large Dependence High Gray Level Emphasis”feature contributed the most to the model,exhibiting a positive effect.CONCLUSION An interpretable ML model based on MRI radiomics provides a noninvasive tool for predicting the pathological grade of HCCs,which will help clinicians develop personalized treatment plans.
基金funded by the Third Xinjiang Scientific Expedition Program(2021xjkk1400)the National Natural Science Foundation of China(42071049)+2 种基金the Natural Science Foundation of Xinjiang Uygur Autonomous Region(2019D01C022)the Xinjiang Uygur Autonomous Region Innovation Environment Construction Special Project&Science and Technology Innovation Base Construction Project(PT2107)the Tianshan Talent-Science and Technology Innovation Team(2022TSYCTD0006).
文摘Snow cover plays a critical role in global climate regulation and hydrological processes.Accurate monitoring is essential for understanding snow distribution patterns,managing water resources,and assessing the impacts of climate change.Remote sensing has become a vital tool for snow monitoring,with the widely used Moderate-resolution Imaging Spectroradiometer(MODIS)snow products from the Terra and Aqua satellites.However,cloud cover often interferes with snow detection,making cloud removal techniques crucial for reliable snow product generation.This study evaluated the accuracy of four MODIS snow cover datasets generated through different cloud removal algorithms.Using real-time field camera observations from four stations in the Tianshan Mountains,China,this study assessed the performance of these datasets during three distinct snow periods:the snow accumulation period(September-November),snowmelt period(March-June),and stable snow period(December-February in the following year).The findings showed that cloud-free snow products generated using the Hidden Markov Random Field(HMRF)algorithm consistently outperformed the others,particularly under cloud cover,while cloud-free snow products using near-day synthesis and the spatiotemporal adaptive fusion method with error correction(STAR)demonstrated varying performance depending on terrain complexity and cloud conditions.This study highlighted the importance of considering terrain features,land cover types,and snow dynamics when selecting cloud removal methods,particularly in areas with rapid snow accumulation and melting.The results suggested that future research should focus on improving cloud removal algorithms through the integration of machine learning,multi-source data fusion,and advanced remote sensing technologies.By expanding validation efforts and refining cloud removal strategies,more accurate and reliable snow products can be developed,contributing to enhanced snow monitoring and better management of water resources in alpine and arid areas.
文摘It has long been realized that the problem of radar imaging is a special case of image reconstruction in which the data are incomplete and noisy. In other fields, iterative reconstruction algorithms have been used successfully to improve the image quality. This paper studies the application of iterative algorithms in radar imaging. A discrete model is first derived, and the iterative algorithms are then adapted to radar imaging. Although such algorithms are usually time consuming, this paper shows that, if the algorithms are appropriately simplified, it is possible to realize them even in real time. The efficiency of iterative algorithms is shown through computer simulations.
