Imaging quality is a critical component of compressive imaging in real applications. In this study, we propose a compressive imaging method based on multi-scale modulation and reconstruction in the spatial frequency d...Imaging quality is a critical component of compressive imaging in real applications. In this study, we propose a compressive imaging method based on multi-scale modulation and reconstruction in the spatial frequency domain. Theoretical analysis and simulation show the relation between the measurement matrix resolution and compressive sensing(CS)imaging quality. The matrix design is improved to provide multi-scale modulations, followed by individual reconstruction of images of different spatial frequencies. Compared with traditional single-scale CS imaging, the multi-scale method provides high quality imaging in both high and low frequencies, and effectively decreases the overall reconstruction error.Experimental results confirm the feasibility of this technique, especially at low sampling rate. The method may thus be helpful in promoting the implementation of compressive imaging in real applications.展开更多
A frequency and spatial domain decomposition method （FSDD） for operational modal analysis （OMA） is presented in this paper, which is an extension of the complex mode indicator function （CMIF） method for experime...A frequency and spatial domain decomposition method （FSDD） for operational modal analysis （OMA） is presented in this paper, which is an extension of the complex mode indicator function （CMIF） method for experimental modal analysis （EMA）. The theoretical background of the FSDD method is clarified, Singular value decomposition is adopted to separate the signal space from the noise space. Finally, an enhanced power spectrum density （PSD） is proposed to obtain more accurate modal parameters by curve fitting in the frequency domain. Moreover, a simulation case and an application case are used to validate this method.展开更多
Cultivated land quality(CLQ)is related to national food security.Rapid and high-precision monitoring of CLQ is crucial for the sustainable development of agriculture.However,current satellite image-based evaluation me...Cultivated land quality(CLQ)is related to national food security.Rapid and high-precision monitoring of CLQ is crucial for the sustainable development of agriculture.However,current satellite image-based evaluation methods that only consider the crop's spatial spectrum characteristics in the key growth stages cannot accurately estimate CLQ.This study proposes a new method based on time-series spectral data of crop growth to improve the accuracy of CLQ estimation.This study was conducted in the Conghua District of Guangzhou,Guangdong Province,China.The results showed that seven spectral indicators were determined as the optimal indicators based on the gradient boosting decision tree(GBDT)and variance inflation factor(VIF).And the genetic algorithm-back propagation neural network(GA-BPNN)model provided more accurate CLQ estimates than the partial least squares regression(PLSR)model,indicating a nonlinear relationship between CLQ and the indicators.In addition,the GA-BPNN model with a normalized root mean square error(NRMSE)of 9.91%demonstrates the excellent potential for mapping CLQ over large areas.The model based on the seven optimal indicators of crop phenology provided higher accuracy than the GA-BPNN model based on the normalized difference vegetation index(NDVI)indicators in the spatial domain,significantly decreasing the NRMSE of the CLQ estimates by 3.17%.This further implied that the spectral indicators in the spatial frequency domain can improve the accuracy of estimating CLQ.展开更多
A scheme to improve the quality in ghost imaging(GI)by controlling the bandwidth of light source(BCGI)is proposed.The theoretical and numerical results show that the reconstruction result with high quality can be obta...A scheme to improve the quality in ghost imaging(GI)by controlling the bandwidth of light source(BCGI)is proposed.The theoretical and numerical results show that the reconstruction result with high quality can be obtained by adjusting the bandwidth range of the light source appropriately,and the selection criterion of the bandwidth is analyzed by the power distribution of the imaging target.A proof-of-principle experiment is implemented to verify the theoretical and numerical results.In addition,the BCGI also presents better anti-noise performance when compared with some popular GI methods.展开更多
Photodynamic therapy(PDT)dosimetry,induding light dose,photosensitizer dose and tissue oxygen,has been a research focus in PDT.In this work,we present a three-dimensional(3D)quantification of protoporphyrin X(PpIX)usi...Photodynamic therapy(PDT)dosimetry,induding light dose,photosensitizer dose and tissue oxygen,has been a research focus in PDT.In this work,we present a three-dimensional(3D)quantification of protoporphyrin X(PpIX)using combined spatial frequency domain imaging(SFDI)and diffuse fuorescence tomography(DFT).The SFDI maps both the distributions of tissue absorption and scattering properties at three wavelengths and accordingly provides the optical background for DFT and extracts the tissue oxygenation for assessing the therapeutic outcomes,while DFT dynamically monitors the 3D distribution of PpEX dose from measured fluorescence signals for the procedure optimization.A pilot in vrivo application in tumor nude models showed that the proposed SFDI/DFT is able to dynamically trace changes in the PpX concentration and tissue oaxygen during the treatment,rendering it a potentially powerful tool for PDT to improve clinical eficacy.展开更多
Recently,there have been several uses for digital image processing.Image fusion has become a prominent application in the domain of imaging processing.To create one final image that provesmore informative and helpful ...Recently,there have been several uses for digital image processing.Image fusion has become a prominent application in the domain of imaging processing.To create one final image that provesmore informative and helpful compared to the original input images,image fusion merges two or more initial images of the same item.Image fusion aims to produce,enhance,and transform significant elements of the source images into combined images for the sake of human visual perception.Image fusion is commonly employed for feature extraction in smart robots,clinical imaging,audiovisual camera integration,manufacturing process monitoring,electronic circuit design,advanced device diagnostics,and intelligent assembly line robots,with image quality varying depending on application.The research paper presents various methods for merging images in spatial and frequency domains,including a blend of stable and curvelet transformations,everageMax-Min,weighted principal component analysis(PCA),HIS(Hue,Intensity,Saturation),wavelet transform,discrete cosine transform(DCT),dual-tree Complex Wavelet Transform(CWT),and multiple wavelet transform.Image fusion methods integrate data from several source images of an identical target,thereby enhancing information in an extremely efficient manner.More precisely,in imaging techniques,the depth of field constraint precludes images from focusing on every object,leading to the exclusion of certain characteristics.To tackle thess challanges,a very efficient multi-focus wavelet decomposition and recompositionmethod is proposed.The use of these wavelet decomposition and recomposition techniques enables this method to make use of existing optimized wavelet code and filter choice.The simulated outcomes provide evidence that the suggested approach initially extracts particular characteristics from images in order to accurately reflect the level of clarity portrayed in the original images.This study enhances the performance of the eXtreme Gradient Boosting(XGBoost)algorithm in detecting brain malignancies with greater precision through the integration of computational image analysis and feature selection.The performance of images is improved by segmenting them employing the K-Means algorithm.The segmentation method aids in identifying specific regions of interest,using Particle Swarm Optimization(PCA)for trait selection and XGBoost for data classification.Extensive trials confirm the model’s exceptional visual performance,achieving an accuracy of up to 97.067%and providing good objective indicators.展开更多
In this study, a Discriminator Model for Glaucoma Diagnosis (DMGD)using soft computing techniques is presented. As the biomedical images such asfundus images are often acquired in high resolution, the Region of Intere...In this study, a Discriminator Model for Glaucoma Diagnosis (DMGD)using soft computing techniques is presented. As the biomedical images such asfundus images are often acquired in high resolution, the Region of Interest (ROI)for glaucoma diagnosis must be selected at first to reduce the complexity of anysystem. The DMGD system uses a series of pre-processing;initial cropping by thegreen channel’s intensity, Spatially Weighted Fuzzy C Means (SWFCM), bloodvessel detection and removal by Gaussian Derivative Filters (GDF) and inpaintingalgorithms. Once the ROI has been selected, the numerical features such as colour, spatial domain features from Local Binary Pattern (LBP) and frequencydomain features from LAWS are generated from the corresponding ROI forfurther classification using kernel based Support Vector Machine (SVM). TheDMGD system performances are validated using four fundus image databases;ORIGA, RIM-ONE, DRISHTI-GS1, and HRF with four different kernels;LinearKernel (LK), Polynomial Kernel (PK), Radial Basis Function (RBFK) kernel,Quadratic Kernel (QK) based SVM classifiers. Results show that the DMGD system classifies the fundus images accurately using the multiple features and kernelbased classifies from the properly segmented ROI.展开更多
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61601442,61605218,and 61575207)the National Key Research and Development Program of China(Grant No.2018YFB0504302)the Youth Innovation Promotion Association of the Chinese Academy of Sciences(Grant Nos.2015124 and 2019154)。
文摘Imaging quality is a critical component of compressive imaging in real applications. In this study, we propose a compressive imaging method based on multi-scale modulation and reconstruction in the spatial frequency domain. Theoretical analysis and simulation show the relation between the measurement matrix resolution and compressive sensing(CS)imaging quality. The matrix design is improved to provide multi-scale modulations, followed by individual reconstruction of images of different spatial frequencies. Compared with traditional single-scale CS imaging, the multi-scale method provides high quality imaging in both high and low frequencies, and effectively decreases the overall reconstruction error.Experimental results confirm the feasibility of this technique, especially at low sampling rate. The method may thus be helpful in promoting the implementation of compressive imaging in real applications.
基金China Postdoctoral Science Foundation Under Grant No. 2004035215 Jiangsu Planned Projects for Postdoctoral Research Funds 2004 Aeronautical Science Research Foundation Under Grant No. 04152065
文摘A frequency and spatial domain decomposition method （FSDD） for operational modal analysis （OMA） is presented in this paper, which is an extension of the complex mode indicator function （CMIF） method for experimental modal analysis （EMA）. The theoretical background of the FSDD method is clarified, Singular value decomposition is adopted to separate the signal space from the noise space. Finally, an enhanced power spectrum density （PSD） is proposed to obtain more accurate modal parameters by curve fitting in the frequency domain. Moreover, a simulation case and an application case are used to validate this method.
基金supported by The Guangdong Province Agricultural Science and Technology Innovation and Promotion Project(No.2020KJ102,No.2021KJ102)Natural Science Foundation of Guangdong Province(2021A1515011643).
文摘Cultivated land quality(CLQ)is related to national food security.Rapid and high-precision monitoring of CLQ is crucial for the sustainable development of agriculture.However,current satellite image-based evaluation methods that only consider the crop's spatial spectrum characteristics in the key growth stages cannot accurately estimate CLQ.This study proposes a new method based on time-series spectral data of crop growth to improve the accuracy of CLQ estimation.This study was conducted in the Conghua District of Guangzhou,Guangdong Province,China.The results showed that seven spectral indicators were determined as the optimal indicators based on the gradient boosting decision tree(GBDT)and variance inflation factor(VIF).And the genetic algorithm-back propagation neural network(GA-BPNN)model provided more accurate CLQ estimates than the partial least squares regression(PLSR)model,indicating a nonlinear relationship between CLQ and the indicators.In addition,the GA-BPNN model with a normalized root mean square error(NRMSE)of 9.91%demonstrates the excellent potential for mapping CLQ over large areas.The model based on the seven optimal indicators of crop phenology provided higher accuracy than the GA-BPNN model based on the normalized difference vegetation index(NDVI)indicators in the spatial domain,significantly decreasing the NRMSE of the CLQ estimates by 3.17%.This further implied that the spectral indicators in the spatial frequency domain can improve the accuracy of estimating CLQ.
基金the National Natural Science Foundation of China(Grant Nos.61871431,61971184,and 62001162).
文摘A scheme to improve the quality in ghost imaging(GI)by controlling the bandwidth of light source(BCGI)is proposed.The theoretical and numerical results show that the reconstruction result with high quality can be obtained by adjusting the bandwidth range of the light source appropriately,and the selection criterion of the bandwidth is analyzed by the power distribution of the imaging target.A proof-of-principle experiment is implemented to verify the theoretical and numerical results.In addition,the BCGI also presents better anti-noise performance when compared with some popular GI methods.
基金supported by the National Natural Science Foundation of China under Grant Nos.(81871393 and 62075156).
文摘Photodynamic therapy(PDT)dosimetry,induding light dose,photosensitizer dose and tissue oxygen,has been a research focus in PDT.In this work,we present a three-dimensional(3D)quantification of protoporphyrin X(PpIX)using combined spatial frequency domain imaging(SFDI)and diffuse fuorescence tomography(DFT).The SFDI maps both the distributions of tissue absorption and scattering properties at three wavelengths and accordingly provides the optical background for DFT and extracts the tissue oxygenation for assessing the therapeutic outcomes,while DFT dynamically monitors the 3D distribution of PpEX dose from measured fluorescence signals for the procedure optimization.A pilot in vrivo application in tumor nude models showed that the proposed SFDI/DFT is able to dynamically trace changes in the PpX concentration and tissue oaxygen during the treatment,rendering it a potentially powerful tool for PDT to improve clinical eficacy.
基金Princess Nourah bint Abdulrahman University and Researchers Supporting Project Number(PNURSP2024R346)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Recently,there have been several uses for digital image processing.Image fusion has become a prominent application in the domain of imaging processing.To create one final image that provesmore informative and helpful compared to the original input images,image fusion merges two or more initial images of the same item.Image fusion aims to produce,enhance,and transform significant elements of the source images into combined images for the sake of human visual perception.Image fusion is commonly employed for feature extraction in smart robots,clinical imaging,audiovisual camera integration,manufacturing process monitoring,electronic circuit design,advanced device diagnostics,and intelligent assembly line robots,with image quality varying depending on application.The research paper presents various methods for merging images in spatial and frequency domains,including a blend of stable and curvelet transformations,everageMax-Min,weighted principal component analysis(PCA),HIS(Hue,Intensity,Saturation),wavelet transform,discrete cosine transform(DCT),dual-tree Complex Wavelet Transform(CWT),and multiple wavelet transform.Image fusion methods integrate data from several source images of an identical target,thereby enhancing information in an extremely efficient manner.More precisely,in imaging techniques,the depth of field constraint precludes images from focusing on every object,leading to the exclusion of certain characteristics.To tackle thess challanges,a very efficient multi-focus wavelet decomposition and recompositionmethod is proposed.The use of these wavelet decomposition and recomposition techniques enables this method to make use of existing optimized wavelet code and filter choice.The simulated outcomes provide evidence that the suggested approach initially extracts particular characteristics from images in order to accurately reflect the level of clarity portrayed in the original images.This study enhances the performance of the eXtreme Gradient Boosting(XGBoost)algorithm in detecting brain malignancies with greater precision through the integration of computational image analysis and feature selection.The performance of images is improved by segmenting them employing the K-Means algorithm.The segmentation method aids in identifying specific regions of interest,using Particle Swarm Optimization(PCA)for trait selection and XGBoost for data classification.Extensive trials confirm the model’s exceptional visual performance,achieving an accuracy of up to 97.067%and providing good objective indicators.
文摘In this study, a Discriminator Model for Glaucoma Diagnosis (DMGD)using soft computing techniques is presented. As the biomedical images such asfundus images are often acquired in high resolution, the Region of Interest (ROI)for glaucoma diagnosis must be selected at first to reduce the complexity of anysystem. The DMGD system uses a series of pre-processing;initial cropping by thegreen channel’s intensity, Spatially Weighted Fuzzy C Means (SWFCM), bloodvessel detection and removal by Gaussian Derivative Filters (GDF) and inpaintingalgorithms. Once the ROI has been selected, the numerical features such as colour, spatial domain features from Local Binary Pattern (LBP) and frequencydomain features from LAWS are generated from the corresponding ROI forfurther classification using kernel based Support Vector Machine (SVM). TheDMGD system performances are validated using four fundus image databases;ORIGA, RIM-ONE, DRISHTI-GS1, and HRF with four different kernels;LinearKernel (LK), Polynomial Kernel (PK), Radial Basis Function (RBFK) kernel,Quadratic Kernel (QK) based SVM classifiers. Results show that the DMGD system classifies the fundus images accurately using the multiple features and kernelbased classifies from the properly segmented ROI.
