Among hyperspectral imaging technologies, interferometric spectral imaging is widely used in remote sening due to advantages of large luminous flux and high resolution. However, with complicated mechanism, interferome...Among hyperspectral imaging technologies, interferometric spectral imaging is widely used in remote sening due to advantages of large luminous flux and high resolution. However, with complicated mechanism, interferometric imaging faces the impact of multi-stage degradation. Most exsiting interferometric spectrum reconstruction methods are based on tradition model-based framework with multiple steps, showing poor efficiency and restricted performance. Thus, we propose an interferometric spectrum reconstruction method based on degradation synthesis and deep learning.Firstly, based on imaging mechanism, we proposed an mathematical model of interferometric imaging to analyse the degradation components as noises and trends during imaging. The model consists of three stages, namely instrument degradation, sensing degradation, and signal-independent degradation process. Then, we designed calibration-based method to estimate parameters in the model, of which the results are used for synthesizing realistic dataset for learning-based algorithms.In addition, we proposed a dual-stage interferogram spectrum reconstruction framework, which supports pre-training and integration of denoising DNNs. Experiments exhibits the reliability of our degradation model and synthesized data, and the effectiveness of the proposed reconstruction method.展开更多
Segmentation of vegetation remote sensing images can minimize the interference of background,thus achieving efficient monitoring and analysis for vegetation information.The segmentation of vegetation poses a significa...Segmentation of vegetation remote sensing images can minimize the interference of background,thus achieving efficient monitoring and analysis for vegetation information.The segmentation of vegetation poses a significant challenge due to the inherently complex environmental conditions.Currently,there is a growing trend of using spectral sensing combined with deep learning for field vegetation segmentation to cope with complex environ-ments.However,two major constraints remain:the high cost of equipment required for field spectral data collection;the availability of field datasets is limited and data annotation is time-consuming and labor-intensive.To address these challenges,we propose a weakly supervised approach for field vegetation segmentation by using spectral reconstruction(SR)techniques as the foundation and drawing on the theory of vegetation index(Ⅵ).Specifically,to reduce the cost of data acquisition,we propose SRCNet and SRANet based on convolution and attention structure to reconstruct multispectral images of fields,respectively.Then,borrowing from theⅥprinciple,we aggregate the reconstructed data to establish the connection of spectral bands,obtaining more salient vegetation information.Finally,we employ the adaptation strategy to segment the fused feature map using a weakly supervised method,which does not require manual labeling to obtain a field vegetation segmentation result.Our segmentation method can achieve a Mean Intersection over Union(MIoU)of 0.853 on real field datasets,which outperforms the existing methods.In addition,we have open-sourced a dataset of unmanned aerial vehicle(UAV)RGB-multispectral images,comprising 2358 pairs of samples,to improve the richness of remote sensing agricultural data.The code and data are available at egment_SR,and.展开更多
Passive seismic data contain large amounts of low-frequency information. To effectively extract and compensate active seismic data that lack low frequencies, we propose a multitaper spectral reconstruction method base...Passive seismic data contain large amounts of low-frequency information. To effectively extract and compensate active seismic data that lack low frequencies, we propose a multitaper spectral reconstruction method based on multiple sinusoidal tapers and derive equations for multisource and multitrace conditions. Compared to conventional cross correlation and deconvolution reconstruction methods, the proposed method can more accurately reconstruct the relative amplitude of recordings. Multidomain iterative denoising improves the SNR of retrieved data. By analyzing the spectral characteristics of passive data before and after reconstruction, we found that the data are expressed more clearly after reconstruction and denoising. To compensate for the low-frequency information in active data using passive seismic data, we match the power spectrum, supplement it, and then smooth it in the frequency domain. Finally, we use numerical simulation to verify the proposed method and conduct prestack depth migration using data after low-frequency compensation. The proposed power-matching method adds the losing low frequency information in the active seismic data using the low-frequency information of passive- source seismic data. The imaging of compensated data gives a more detailed information of deep structures.展开更多
Objective:To investigate the diagnostic value of spectral CT reconstruction mode for carotid atherosclerotic plaque lesions.Methods:From January 2017 to January 2019,70 patients with carotid atherosclerotic plaque les...Objective:To investigate the diagnostic value of spectral CT reconstruction mode for carotid atherosclerotic plaque lesions.Methods:From January 2017 to January 2019,70 patients with carotid atherosclerotic plaque lesions in our hospita1 were selected as the research object.A11 patients were diagnosed with cervical vascular color Doppler ultrasound and spectral CT scan under spectral cr reconstruction mode.Taking the results of coronaryf angiography as the"gold standard",the clinical value of the two examination methods in the diagnosis of carotid atherosclerotic plaque lesions was compared and analyzed.Results:Coronary angiography diagnosis confirmed that 33 of 70 patients with suspected carotid atherosclerotic plaque lesions had vulnerable plaques and 37 had stable plaques.The accuracy of Spectral CT examination of carotid artery plaque was 87.14%(61/70),sensitivity was 90.91%(30/33),specificity was 83.78%(31/37),and the positive predictive value was 83.33%(30/36),the negative predictive value is 91.76%(31/34),which is higher than that of cervical vascular ultrasonography(61.43%,60.61%,56.76%,57.89%,65.63%),the difference is statistically significant(P<0.05).Conclusion:The application of Spectral CT in the clinical diagnosis and treatment of carotid atherosclerotic plaque lesions with higher accuracy,sensitivity and specificity,is more significant and can provide a more reliable and effective imaging basis.展开更多
This work introduces a novel method for measuring thin film thickness,employing a multi-wavelength method that significantly reduces the need for broad-spectrum data.Unlike traditional techniques that require sev⁃eral...This work introduces a novel method for measuring thin film thickness,employing a multi-wavelength method that significantly reduces the need for broad-spectrum data.Unlike traditional techniques that require sev⁃eral hundred spectral data points,the multi-wavelength method achieves precise thickness measurements with data from only 10 wavelengths.This innovation not only simplifies the process of spectral measurement analysis but also enables accurate real-time thickness measurement on industrial coating production lines.The method effectively reconstructs and fits the visible spectrum(400-800 nm)using a minimal amount of data,while maintaining measurement error within 7.1%.This advancement lays the foundation for more practical and efficient thin film thickness determination techniques in various industrial applications.展开更多
The chip-scale integrated spectrometers are opening new avenues for a much wider range of applications than their conventional benchtop counterparts.While spectral reconstruction should be in command of both spectral ...The chip-scale integrated spectrometers are opening new avenues for a much wider range of applications than their conventional benchtop counterparts.While spectral reconstruction should be in command of both spectral resolution and bandwidth,a large number of spectral channels is among the key goals of the spectrometer design.However,the chip footprint eventually limits the spectral channel capacities of well-established spectral-to-spatial mapping structures like dispersive elements,filter arrays,random media,and so on.Here we suggest an alternative scheme by encoding the spectral information using on-chip diffractive metasurfaces.The in-plane metasurface is capable of producing intensity speckles to resolve the spectra.The spectral richness is greatly increased by scaling the architecture via three layers of cascaded metasurfaces.The readout of speckles is realized by two-dimensional imaging of the grating-diffracted pattern,enabling a large matrix for spectrum reconstruction.The spectrometer has a resolution of 70 pm over a bandwidth of 100 nm.Up to 1400 spectral channels were obtained within a compact chip area of only 150μm×950μm.The on-chip diffractive spectrometer has a benchmark channel density of up to 10021 ch/mm^(2),which compares favorably against other state-of-art waveguide structures.展开更多
A spectral profile reconstruction method that can be applied to incomplete saturated-absorption spectra is proposed and demonstrated. Through simulation and theoretical calculation, it is proved that compared with the...A spectral profile reconstruction method that can be applied to incomplete saturated-absorption spectra is proposed and demonstrated. Through simulation and theoretical calculation, it is proved that compared with the traditional wholeprofile fitting method, this new method can increase the concentration detection upper limit of a single absorption line by about 8.7 times. High-concentration water vapor is measured using TDLAS technology, the total water vapor pressure and the self-broadened half-width coefficient of the spectrum were simultaneously measured from incomplete saturatedabsorption spectra and compared with high-precision pressure sensors and the HITRAN databases. Their maximum relative deviations were about 4.63% and 9.10%, respectively. These results show that the spectral profile reconstruction method has great application potential for expanding the dynamic range of single-line measurements to higher concentrations,especially for in-situ online measurements under complex conditions, such as over large temperature and concentration dynamic ranges.展开更多
By studying the traditional spectral reflectance reconstruction method, spectral reflectance and the relative spectral power distribution of a lighting source are sparsely decomposed, and the orthogonal property of th...By studying the traditional spectral reflectance reconstruction method, spectral reflectance and the relative spectral power distribution of a lighting source are sparsely decomposed, and the orthogonal property of the principal component orthogonal basis is used to eliminate basis; then spectral reflectance data are obtained by solving a sparse coefficient. After theoretical analysis, the spectral reflectance reconstruction based on sparse prior knowledge of the principal component orthogonal basis by a single-pixel detector is carried out by software simulation and experiment. It can reduce the complexity and cost of the system, and has certain significance for the improvement of multispectral image acquisition technology.展开更多
We propose and study an iterative sparse reconstruction for Fourier domain optical coherence tomography （FD OCT） image by solving a constrained optimization problem that minimizes L-1 norm. Our method takes the spec...We propose and study an iterative sparse reconstruction for Fourier domain optical coherence tomography （FD OCT） image by solving a constrained optimization problem that minimizes L-1 norm. Our method takes the spectral shape of the OCT light source into consideration in the iterative image reconstruction procedure that allows deconvolution of the axial point spread function from the blurred image during reconstruction rather than after reconstruction. By minimizing the L-1 norm, the axial resolution and the signal to noise ratio of image can both be enhanced. The effectiveness of our method is validated using numerical simulation and experiment.展开更多
文摘Among hyperspectral imaging technologies, interferometric spectral imaging is widely used in remote sening due to advantages of large luminous flux and high resolution. However, with complicated mechanism, interferometric imaging faces the impact of multi-stage degradation. Most exsiting interferometric spectrum reconstruction methods are based on tradition model-based framework with multiple steps, showing poor efficiency and restricted performance. Thus, we propose an interferometric spectrum reconstruction method based on degradation synthesis and deep learning.Firstly, based on imaging mechanism, we proposed an mathematical model of interferometric imaging to analyse the degradation components as noises and trends during imaging. The model consists of three stages, namely instrument degradation, sensing degradation, and signal-independent degradation process. Then, we designed calibration-based method to estimate parameters in the model, of which the results are used for synthesizing realistic dataset for learning-based algorithms.In addition, we proposed a dual-stage interferogram spectrum reconstruction framework, which supports pre-training and integration of denoising DNNs. Experiments exhibits the reliability of our degradation model and synthesized data, and the effectiveness of the proposed reconstruction method.
基金supported by National Key R&D Program of China(2024YFD2001100,2024YFE0214300)National Natural Science Foundation of China(No.62162008)+3 种基金Guizhou Provincial Science and Technology Projects([2024]002,CXTD[2023]027)Guizhou Province Youth Science and Technology Talent Project([2024]317)Guiyang Guian Science and Technology Talent Training Project([2024]2-15)The Talent Introduction Program of Guizhou University under Grant No.(2021)89.
文摘Segmentation of vegetation remote sensing images can minimize the interference of background,thus achieving efficient monitoring and analysis for vegetation information.The segmentation of vegetation poses a significant challenge due to the inherently complex environmental conditions.Currently,there is a growing trend of using spectral sensing combined with deep learning for field vegetation segmentation to cope with complex environ-ments.However,two major constraints remain:the high cost of equipment required for field spectral data collection;the availability of field datasets is limited and data annotation is time-consuming and labor-intensive.To address these challenges,we propose a weakly supervised approach for field vegetation segmentation by using spectral reconstruction(SR)techniques as the foundation and drawing on the theory of vegetation index(Ⅵ).Specifically,to reduce the cost of data acquisition,we propose SRCNet and SRANet based on convolution and attention structure to reconstruct multispectral images of fields,respectively.Then,borrowing from theⅥprinciple,we aggregate the reconstructed data to establish the connection of spectral bands,obtaining more salient vegetation information.Finally,we employ the adaptation strategy to segment the fused feature map using a weakly supervised method,which does not require manual labeling to obtain a field vegetation segmentation result.Our segmentation method can achieve a Mean Intersection over Union(MIoU)of 0.853 on real field datasets,which outperforms the existing methods.In addition,we have open-sourced a dataset of unmanned aerial vehicle(UAV)RGB-multispectral images,comprising 2358 pairs of samples,to improve the richness of remote sensing agricultural data.The code and data are available at egment_SR,and.
基金sponsored by the Natural Science Foundation of China(No.41374115)National High Technology Research and Development Program of China(863 project)(No.2014AA06A605)
文摘Passive seismic data contain large amounts of low-frequency information. To effectively extract and compensate active seismic data that lack low frequencies, we propose a multitaper spectral reconstruction method based on multiple sinusoidal tapers and derive equations for multisource and multitrace conditions. Compared to conventional cross correlation and deconvolution reconstruction methods, the proposed method can more accurately reconstruct the relative amplitude of recordings. Multidomain iterative denoising improves the SNR of retrieved data. By analyzing the spectral characteristics of passive data before and after reconstruction, we found that the data are expressed more clearly after reconstruction and denoising. To compensate for the low-frequency information in active data using passive seismic data, we match the power spectrum, supplement it, and then smooth it in the frequency domain. Finally, we use numerical simulation to verify the proposed method and conduct prestack depth migration using data after low-frequency compensation. The proposed power-matching method adds the losing low frequency information in the active seismic data using the low-frequency information of passive- source seismic data. The imaging of compensated data gives a more detailed information of deep structures.
文摘Objective:To investigate the diagnostic value of spectral CT reconstruction mode for carotid atherosclerotic plaque lesions.Methods:From January 2017 to January 2019,70 patients with carotid atherosclerotic plaque lesions in our hospita1 were selected as the research object.A11 patients were diagnosed with cervical vascular color Doppler ultrasound and spectral CT scan under spectral cr reconstruction mode.Taking the results of coronaryf angiography as the"gold standard",the clinical value of the two examination methods in the diagnosis of carotid atherosclerotic plaque lesions was compared and analyzed.Results:Coronary angiography diagnosis confirmed that 33 of 70 patients with suspected carotid atherosclerotic plaque lesions had vulnerable plaques and 37 had stable plaques.The accuracy of Spectral CT examination of carotid artery plaque was 87.14%(61/70),sensitivity was 90.91%(30/33),specificity was 83.78%(31/37),and the positive predictive value was 83.33%(30/36),the negative predictive value is 91.76%(31/34),which is higher than that of cervical vascular ultrasonography(61.43%,60.61%,56.76%,57.89%,65.63%),the difference is statistically significant(P<0.05).Conclusion:The application of Spectral CT in the clinical diagnosis and treatment of carotid atherosclerotic plaque lesions with higher accuracy,sensitivity and specificity,is more significant and can provide a more reliable and effective imaging basis.
基金Supported by National Key R&D Program of China(2021YFA0715500)National Natural Science Foundation of China(NSFC)(12227901)+2 种基金Strategic Priority Research Program(B)of the Chinese Academy of Sciences(XDB0580000)Shanghai Municipal Science and Technology Major Project(2019SHZDZX01)Chinese Academy of Sciences President's International Fellowship Initiative(2021PT0007).
文摘This work introduces a novel method for measuring thin film thickness,employing a multi-wavelength method that significantly reduces the need for broad-spectrum data.Unlike traditional techniques that require sev⁃eral hundred spectral data points,the multi-wavelength method achieves precise thickness measurements with data from only 10 wavelengths.This innovation not only simplifies the process of spectral measurement analysis but also enables accurate real-time thickness measurement on industrial coating production lines.The method effectively reconstructs and fits the visible spectrum(400-800 nm)using a minimal amount of data,while maintaining measurement error within 7.1%.This advancement lays the foundation for more practical and efficient thin film thickness determination techniques in various industrial applications.
基金funded by the National Natural Science Foundation of China(U21A20454)the Science,Technology,and Innovation Commission of Shenzhen Municipality(RCYX20210609103707009,JCYJ20220818102406013)the Natural Science Foundation of Guangdong Province for Distinguished Young Scholars(2022B1515020057).
文摘The chip-scale integrated spectrometers are opening new avenues for a much wider range of applications than their conventional benchtop counterparts.While spectral reconstruction should be in command of both spectral resolution and bandwidth,a large number of spectral channels is among the key goals of the spectrometer design.However,the chip footprint eventually limits the spectral channel capacities of well-established spectral-to-spatial mapping structures like dispersive elements,filter arrays,random media,and so on.Here we suggest an alternative scheme by encoding the spectral information using on-chip diffractive metasurfaces.The in-plane metasurface is capable of producing intensity speckles to resolve the spectra.The spectral richness is greatly increased by scaling the architecture via three layers of cascaded metasurfaces.The readout of speckles is realized by two-dimensional imaging of the grating-diffracted pattern,enabling a large matrix for spectrum reconstruction.The spectrometer has a resolution of 70 pm over a bandwidth of 100 nm.Up to 1400 spectral channels were obtained within a compact chip area of only 150μm×950μm.The on-chip diffractive spectrometer has a benchmark channel density of up to 10021 ch/mm^(2),which compares favorably against other state-of-art waveguide structures.
基金supported by the Guangdong Province Key Laboratory of Efficient and Clean Energy Utilization (Grant No. 2013A061401005)the Key Laboratory of Efficient and Clean Energy Utilization of Guangdong Higher Education Institutes (Grant No. KLB10004)。
文摘A spectral profile reconstruction method that can be applied to incomplete saturated-absorption spectra is proposed and demonstrated. Through simulation and theoretical calculation, it is proved that compared with the traditional wholeprofile fitting method, this new method can increase the concentration detection upper limit of a single absorption line by about 8.7 times. High-concentration water vapor is measured using TDLAS technology, the total water vapor pressure and the self-broadened half-width coefficient of the spectrum were simultaneously measured from incomplete saturatedabsorption spectra and compared with high-precision pressure sensors and the HITRAN databases. Their maximum relative deviations were about 4.63% and 9.10%, respectively. These results show that the spectral profile reconstruction method has great application potential for expanding the dynamic range of single-line measurements to higher concentrations,especially for in-situ online measurements under complex conditions, such as over large temperature and concentration dynamic ranges.
基金supported by the National Natural Science Foundation of China (Grant No.61405115)the Natural Science Foundation of Shanghai (Grant No.14ZR1428400)+1 种基金the Innovation Project of Shanghai Municipal Education Commission (Grant No.14YZ099)National Basic Research Program of China (973 Program) (Grant No.2015CB352004)
文摘By studying the traditional spectral reflectance reconstruction method, spectral reflectance and the relative spectral power distribution of a lighting source are sparsely decomposed, and the orthogonal property of the principal component orthogonal basis is used to eliminate basis; then spectral reflectance data are obtained by solving a sparse coefficient. After theoretical analysis, the spectral reflectance reconstruction based on sparse prior knowledge of the principal component orthogonal basis by a single-pixel detector is carried out by software simulation and experiment. It can reduce the complexity and cost of the system, and has certain significance for the improvement of multispectral image acquisition technology.
基金supported in part by the government of United States,NIH BRP grants 1R01 EB 007969NIH/NIE R011R01EY021540-01A1,and by internal start-up research funding from Michigan Technological University
文摘We propose and study an iterative sparse reconstruction for Fourier domain optical coherence tomography （FD OCT） image by solving a constrained optimization problem that minimizes L-1 norm. Our method takes the spectral shape of the OCT light source into consideration in the iterative image reconstruction procedure that allows deconvolution of the axial point spread function from the blurred image during reconstruction rather than after reconstruction. By minimizing the L-1 norm, the axial resolution and the signal to noise ratio of image can both be enhanced. The effectiveness of our method is validated using numerical simulation and experiment.
