Non-line-of-sight(NLOS)imaging has emerged as a prominent technique for reconstructing obscured objects from images that undergo multiple diffuse reflections.This imaging method has garnered significant attention in d...Non-line-of-sight(NLOS)imaging has emerged as a prominent technique for reconstructing obscured objects from images that undergo multiple diffuse reflections.This imaging method has garnered significant attention in diverse domains,including remote sensing,rescue operations,and intelligent driving,due to its wide-ranging potential applications.Nevertheless,accurately modeling the incident light direction,which carries energy and is captured by the detector amidst random diffuse reflection directions,poses a considerable challenge.This challenge hinders the acquisition of precise forward and inverse physical models for NLOS imaging,which are crucial for achieving high-quality reconstructions.In this study,we propose a point spread function(PSF)model for the NLOS imaging system utilizing ray tracing with random angles.Furthermore,we introduce a reconstruction method,termed the physics-constrained inverse network(PCIN),which establishes an accurate PSF model and inverse physical model by leveraging the interplay between PSF constraints and the optimization of a convolutional neural network.The PCIN approach initializes the parameters randomly,guided by the constraints of the forward PSF model,thereby obviating the need for extensive training data sets,as required by traditional deep-learning methods.Through alternating iteration and gradient descent algorithms,we iteratively optimize the diffuse reflection angles in the PSF model and the neural network parameters.The results demonstrate that PCIN achieves efficient data utilization by not necessitating a large number of actual ground data groups.Moreover,the experimental findings confirm that the proposed method effectively restores the hidden object features with high accuracy.展开更多
Spectral distortion often occurs in spectral data due to the influence of the bandpass function of the spectrometer.Spectral deconvolution is an effective restoration method to solve this problem.Based on the theory o...Spectral distortion often occurs in spectral data due to the influence of the bandpass function of the spectrometer.Spectral deconvolution is an effective restoration method to solve this problem.Based on the theory of the maximum posteriori estimation,this paper transforms the spectral deconvolution problem into a multi-parameter optimization problem,and a novel spectral deconvolution method is proposed on the basis of Levenberg-Marquardt algorithm.Furthermore,a spectral adaptive operator is added to the method,which improves the effect of the regularization term.The proposed methods,Richardson-Lucy(R-L)method and Huber-Markov spectroscopic semi-blind deconvolution(HMSBD)method,are employed to deconvolute the white light-emitting diode(LED)spectra with two different color temperatures,respectively.The correction errors,root mean square errors,noise suppression ability,and the computation speed of above methods are compared.The experimental results prove the superiority of the proposed algorithm.展开更多
Purpose Scintillator Neutron Detectors Arrays(SNDA)were successfully installed at General Purpose Powder Diffractome-ter(GPPD)at the China Spallation Neutron Source(CSNS).The inhomogeneity of the detection efficiency ...Purpose Scintillator Neutron Detectors Arrays(SNDA)were successfully installed at General Purpose Powder Diffractome-ter(GPPD)at the China Spallation Neutron Source(CSNS).The inhomogeneity of the detection efficiency in each detector module,which caused by the gain nonuniformity of the multi-anode photo-multiplier tubes(MA-PMTs)and the inconsistency of the wave-length shifting fibers in collecting scintillation photons,need to be mitigated before the installation.Methods An automated rapid measurement system based on the blue laser and the two-dimensional mobile platform was developed to calibrate the light response of each channel in detector modules.According to the test results of this system,the electronics threshold of each channel of the SNDA is adjusted.Before the installation of the all 40 SNDA modules in GPPD,the electronics thresholds of each channel are adjusted according to the measurement results of this rapid measurement system.Results and Conclusion Compared with the unadjusted detector module,the adjusted one obtained a better uniformity of the neutron detection efficiency.The inhomogeneity of the detection efficiency is improved from 27.4%to 10.9%.The test result of the diffraction peak of the standard sample Si showed that the adjusted SNDA works well in GPPD.展开更多
基金supported by the Instrument Developing Project of the Chinese Academy of Sciences (Grant No.YJKYYQ20190044)the National Key Research and Development Program of China (Grant No.2022YFB3903100)+1 种基金the High-level introduction of talent research start-up fund of Hefei Normal University in 2020 (Grant No.2020rcjj34)the HFIPS Director’s Fund (Grant No.YZJJ2022QN12).
文摘Non-line-of-sight(NLOS)imaging has emerged as a prominent technique for reconstructing obscured objects from images that undergo multiple diffuse reflections.This imaging method has garnered significant attention in diverse domains,including remote sensing,rescue operations,and intelligent driving,due to its wide-ranging potential applications.Nevertheless,accurately modeling the incident light direction,which carries energy and is captured by the detector amidst random diffuse reflection directions,poses a considerable challenge.This challenge hinders the acquisition of precise forward and inverse physical models for NLOS imaging,which are crucial for achieving high-quality reconstructions.In this study,we propose a point spread function(PSF)model for the NLOS imaging system utilizing ray tracing with random angles.Furthermore,we introduce a reconstruction method,termed the physics-constrained inverse network(PCIN),which establishes an accurate PSF model and inverse physical model by leveraging the interplay between PSF constraints and the optimization of a convolutional neural network.The PCIN approach initializes the parameters randomly,guided by the constraints of the forward PSF model,thereby obviating the need for extensive training data sets,as required by traditional deep-learning methods.Through alternating iteration and gradient descent algorithms,we iteratively optimize the diffuse reflection angles in the PSF model and the neural network parameters.The results demonstrate that PCIN achieves efficient data utilization by not necessitating a large number of actual ground data groups.Moreover,the experimental findings confirm that the proposed method effectively restores the hidden object features with high accuracy.
基金This work is supported by the National Natural Science Foundation of China(NSFC)(Grant No.11504383)the National Natural Science Foundation of China and Chinese Academy of Science(Grant No.U131111).
文摘Spectral distortion often occurs in spectral data due to the influence of the bandpass function of the spectrometer.Spectral deconvolution is an effective restoration method to solve this problem.Based on the theory of the maximum posteriori estimation,this paper transforms the spectral deconvolution problem into a multi-parameter optimization problem,and a novel spectral deconvolution method is proposed on the basis of Levenberg-Marquardt algorithm.Furthermore,a spectral adaptive operator is added to the method,which improves the effect of the regularization term.The proposed methods,Richardson-Lucy(R-L)method and Huber-Markov spectroscopic semi-blind deconvolution(HMSBD)method,are employed to deconvolute the white light-emitting diode(LED)spectra with two different color temperatures,respectively.The correction errors,root mean square errors,noise suppression ability,and the computation speed of above methods are compared.The experimental results prove the superiority of the proposed algorithm.
基金supported by the National Natural Science Foundation of China(Grant No.11875273,No.U1832111)
文摘Purpose Scintillator Neutron Detectors Arrays(SNDA)were successfully installed at General Purpose Powder Diffractome-ter(GPPD)at the China Spallation Neutron Source(CSNS).The inhomogeneity of the detection efficiency in each detector module,which caused by the gain nonuniformity of the multi-anode photo-multiplier tubes(MA-PMTs)and the inconsistency of the wave-length shifting fibers in collecting scintillation photons,need to be mitigated before the installation.Methods An automated rapid measurement system based on the blue laser and the two-dimensional mobile platform was developed to calibrate the light response of each channel in detector modules.According to the test results of this system,the electronics threshold of each channel of the SNDA is adjusted.Before the installation of the all 40 SNDA modules in GPPD,the electronics thresholds of each channel are adjusted according to the measurement results of this rapid measurement system.Results and Conclusion Compared with the unadjusted detector module,the adjusted one obtained a better uniformity of the neutron detection efficiency.The inhomogeneity of the detection efficiency is improved from 27.4%to 10.9%.The test result of the diffraction peak of the standard sample Si showed that the adjusted SNDA works well in GPPD.