A point spread function（PSF） for the blurring component in positron emission tomography（PET） is studied. The PSF matrix is derived from the single photon incidence response function. A statistical iterative recons...A point spread function（PSF） for the blurring component in positron emission tomography（PET） is studied. The PSF matrix is derived from the single photon incidence response function. A statistical iterative reconstruction（IR） method based on the system matrix containing the PSF is developed. More specifically, the gamma photon incidence upon a crystal array is simulated by Monte Carlo（MC） simulation, and then the single photon incidence response functions are calculated. Subsequently, the single photon incidence response functions are used to compute the coincidence blurring factor according to the physical process of PET coincidence detection. Through weighting the ordinary system matrix response by the coincidence blurring factors, the IR system matrix containing the PSF is finally established. By using this system matrix, the image is reconstructed by an ordered subset expectation maximization（OSEM） algorithm. The experimental results show that the proposed system matrix can substantially improve the image radial resolution, contrast,and noise property. Furthermore, the simulated single gamma-ray incidence response function depends only on the crystal configuration, so the method could be extended to any PET scanner with the same detector crystal configuration.展开更多
Hyper-and multi-spectral image fusion is an important technology to produce hyper-spectral and hyper-resolution images,which always depends on the spectral response function andthe point spread function.However,few wo...Hyper-and multi-spectral image fusion is an important technology to produce hyper-spectral and hyper-resolution images,which always depends on the spectral response function andthe point spread function.However,few works have been payed on the estimation of the two degra-dation functions.To learn the two functions from image pairs to be fused,we propose a Dirichletnetwork,where both functions are properly constrained.Specifically,the spatial response function isconstrained with positivity,while the Dirichlet distribution along with a total variation is imposedon the point spread function.To the best of our knowledge,the neural network and the Dirichlet regularization are exclusively investigated,for the first time,to estimate the degradation functions.Both image degradation and fusion experiments demonstrate the effectiveness and superiority of theproposed Dirichlet network.展开更多
A set of point spread functions (PSF) has been obtained by means of Monte-Carlo simulation for asmall gamma camera with a pinhole collimator of various hole diameters. The FOV (field of view) of the camera isexpended ...A set of point spread functions (PSF) has been obtained by means of Monte-Carlo simulation for asmall gamma camera with a pinhole collimator of various hole diameters. The FOV (field of view) of the camera isexpended from 45 mm to 70 mm in diameter. The position dependence of the variances of PSF is presented, and theacceptance for the 140 kev gamma rays is explored. A phantom of 70 mm in diameter was experimentally imaged inthe camera with effective FOV of only 45 mm in diameter.展开更多
Based on the point spread function （PSF） theory, the side-lobe extension direction of the impulse response in bistatic synthetic aperture radar （BSAR） is analyzed in detail; in addition, the corresponding autofocu...Based on the point spread function （PSF） theory, the side-lobe extension direction of the impulse response in bistatic synthetic aperture radar （BSAR） is analyzed in detail; in addition, the corresponding autofocus in BSAR should be considered along iso-range direction, not the traditional azimuth resolution （AR） direction. The conclusion is verified by the computer simulation.展开更多
In this paper the progress of document image Point Spread Function （PSF） estimation will be presented. At the beginning of the paper, an overview of PSF estimation methods will be introduced and the reason why knife...In this paper the progress of document image Point Spread Function （PSF） estimation will be presented. At the beginning of the paper, an overview of PSF estimation methods will be introduced and the reason why knife-edge input PSF estimation method is chosen will be explained. Then in the next section, the knife-edge input PSF estimation method will be detailed. After that, a simulation experiment is performed in order to verify the implemented PSF estimation method. Based on the simulation experiment, in next section we propose a procedure that makes automatic PSF estimation possible. A real document image is firstly taken as an example to illustrate the procedure and then be restored with the estimated PSF and Lucy-Richardson deconvolution method, and its OCR accuracy before and after deconvolution will be compared. Finally, we conclude the paper with the outlook for the future work.展开更多
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
Computational fluorescence microscopy constantly breaks through imaging performance through advanced opticalmodulation technologies;however, conventional theoretical modeling and experimental measurement approachesare...Computational fluorescence microscopy constantly breaks through imaging performance through advanced opticalmodulation technologies;however, conventional theoretical modeling and experimental measurement approachesare challenging to meet the demand for accurate system characterization of diverse modulations. To this end, wepropose a point spread function (PSF) decoupling method that is intrinsically compatible with the optimaldemodulation in computational microscopic imaging modality. The critical core lies in designing a sample prior-basedcomputational imaging strategy, in which a regular fluorescent sample instead of generally used sub-diffractionlimited particles acts as a system modulator to demodulate the system response. PSF consequently can becomputationally optimized through the strong support from the modulated sample prior, achieving accurate nonparametricsystem characterization and thereby avoiding the modeling difficulty and the low signal-to-noise ratiomeasurement errors of the system specificity. Experimental results across various biological tissues demonstrated andverified that the proposed PSF decoupling method enables excellent volumetric imaging comparable to confocalmicroscopy and multicolor, large depth-of-field imaging under aperture modulation. It provides a promisingmechanism of system characterization and computational demodulation for high-contrast and high-resolutionimaging of cellular and subcellular biological structures and life activities.展开更多
目的针对运动模糊图像复原中,传统频谱法存在的检测误差大、抗噪性弱及有效检测范围有限等问题,本文提出一种融合高斯拉普拉斯(Laplacian of Gaussian,LoG)滤波与Radon变换的边缘增强频谱分析法,旨在实现点扩散函数(point spread functi...目的针对运动模糊图像复原中,传统频谱法存在的检测误差大、抗噪性弱及有效检测范围有限等问题,本文提出一种融合高斯拉普拉斯(Laplacian of Gaussian,LoG)滤波与Radon变换的边缘增强频谱分析法,旨在实现点扩散函数(point spread function,PSF)参数的精准估计。方法基于脑部MRI仿真运动模糊模型,分析频谱中明暗条纹的分布特征,采用LoG滤波提取频谱亮条纹边缘,抑制中心宽条纹与Gibbs现象导致的干扰,生成保留方向特征的离散边缘点集;利用Radon变换包容非共线点集的抗噪特性(离散边缘点沿角度θ投影时,真实边缘贡献相干叠加,噪声点投影随机抵消),显著提升峰信噪比,进而精准定位模糊角度并计算模糊长度;采用配对t检验,对传统中心亮条纹检测方法与本文方法的PSF参数估计误差进行统计学分析比较。结果本研究方法的角度估计平均误差0.08°,显著低于传统方法的3.28°,长度估计平均误差0.15像素,传统方法为0.88像素,有效角度检测范围由传统方法的±60°扩展到0~180°,且组间误差差异均达极显著水平(P<0.001)。结论本方法通过LoG滤波与Radon变换的协同机制,避免了对中心条纹完整性的依赖,解决了宽条纹导致的检测失效问题,同时有效抑制了噪声和Gibbs现象导致的干扰,显著提高了运动模糊PSF参数估计的精度与鲁棒性,为医学影像运动伪影消除提供可靠的技术基础。展开更多
空间变化PSF(Space-variant Point Spread Function,SVPSF)图像,即物空间各点的退化随位置的改变而改变的图像,由于其复原技术涉及到多个甚至海量PSF的提取、存储和运算,相对于空间不变PSF(Space-Invariant Point Spread Function,SIPSF...空间变化PSF(Space-variant Point Spread Function,SVPSF)图像,即物空间各点的退化随位置的改变而改变的图像,由于其复原技术涉及到多个甚至海量PSF的提取、存储和运算,相对于空间不变PSF(Space-Invariant Point Spread Function,SIPSF)图像复原要困难得多。目前处理此类图像的主要方法包括空间坐标转换法,等晕区分块复原法,以减少数据存储量,降低计算量,提高收敛速度为目标的直接复原法等。本文回顾了这一课题的研究历史,对目前的研究工作进行了分析和总结,介绍了本实验室提出的结合GRM(Gradient Ringing Metric)评价算法的总变分最小化图像分块复原法,并提出了未来工作关注重点的展望。展开更多
基金Project supported by the National Natural Science Foundation of China(Grant Nos.Y4811H805C and 81101175)
文摘A point spread function（PSF） for the blurring component in positron emission tomography（PET） is studied. The PSF matrix is derived from the single photon incidence response function. A statistical iterative reconstruction（IR） method based on the system matrix containing the PSF is developed. More specifically, the gamma photon incidence upon a crystal array is simulated by Monte Carlo（MC） simulation, and then the single photon incidence response functions are calculated. Subsequently, the single photon incidence response functions are used to compute the coincidence blurring factor according to the physical process of PET coincidence detection. Through weighting the ordinary system matrix response by the coincidence blurring factors, the IR system matrix containing the PSF is finally established. By using this system matrix, the image is reconstructed by an ordered subset expectation maximization（OSEM） algorithm. The experimental results show that the proposed system matrix can substantially improve the image radial resolution, contrast,and noise property. Furthermore, the simulated single gamma-ray incidence response function depends only on the crystal configuration, so the method could be extended to any PET scanner with the same detector crystal configuration.
基金the Postdoctoral ScienceFoundation of China(No.2023M730156)the NationalNatural Foundation of China(No.62301012).
文摘Hyper-and multi-spectral image fusion is an important technology to produce hyper-spectral and hyper-resolution images,which always depends on the spectral response function andthe point spread function.However,few works have been payed on the estimation of the two degra-dation functions.To learn the two functions from image pairs to be fused,we propose a Dirichletnetwork,where both functions are properly constrained.Specifically,the spatial response function isconstrained with positivity,while the Dirichlet distribution along with a total variation is imposedon the point spread function.To the best of our knowledge,the neural network and the Dirichlet regularization are exclusively investigated,for the first time,to estimate the degradation functions.Both image degradation and fusion experiments demonstrate the effectiveness and superiority of theproposed Dirichlet network.
基金Supported by the National Natural Science Foundation of China(10275063)
文摘A set of point spread functions (PSF) has been obtained by means of Monte-Carlo simulation for asmall gamma camera with a pinhole collimator of various hole diameters. The FOV (field of view) of the camera isexpended from 45 mm to 70 mm in diameter. The position dependence of the variances of PSF is presented, and theacceptance for the 140 kev gamma rays is explored. A phantom of 70 mm in diameter was experimentally imaged inthe camera with effective FOV of only 45 mm in diameter.
文摘Based on the point spread function （PSF） theory, the side-lobe extension direction of the impulse response in bistatic synthetic aperture radar （BSAR） is analyzed in detail; in addition, the corresponding autofocus in BSAR should be considered along iso-range direction, not the traditional azimuth resolution （AR） direction. The conclusion is verified by the computer simulation.
文摘In this paper the progress of document image Point Spread Function （PSF） estimation will be presented. At the beginning of the paper, an overview of PSF estimation methods will be introduced and the reason why knife-edge input PSF estimation method is chosen will be explained. Then in the next section, the knife-edge input PSF estimation method will be detailed. After that, a simulation experiment is performed in order to verify the implemented PSF estimation method. Based on the simulation experiment, in next section we propose a procedure that makes automatic PSF estimation possible. A real document image is firstly taken as an example to illustrate the procedure and then be restored with the estimated PSF and Lucy-Richardson deconvolution method, and its OCR accuracy before and after deconvolution will be compared. Finally, we conclude the paper with the outlook for the future work.
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supported by grants from the National Natural Science Foundation of China(NSFC)(62275173,62175109,62371311)Shenzhen Fundamental Research Program(JCYJ20220531101204010)+1 种基金Shenzhen Higher Education Stable Support Program(20231122025852001)Scientific Instrument Developing Project of Shenzhen University(2023YQ009).
文摘Computational fluorescence microscopy constantly breaks through imaging performance through advanced opticalmodulation technologies;however, conventional theoretical modeling and experimental measurement approachesare challenging to meet the demand for accurate system characterization of diverse modulations. To this end, wepropose a point spread function (PSF) decoupling method that is intrinsically compatible with the optimaldemodulation in computational microscopic imaging modality. The critical core lies in designing a sample prior-basedcomputational imaging strategy, in which a regular fluorescent sample instead of generally used sub-diffractionlimited particles acts as a system modulator to demodulate the system response. PSF consequently can becomputationally optimized through the strong support from the modulated sample prior, achieving accurate nonparametricsystem characterization and thereby avoiding the modeling difficulty and the low signal-to-noise ratiomeasurement errors of the system specificity. Experimental results across various biological tissues demonstrated andverified that the proposed PSF decoupling method enables excellent volumetric imaging comparable to confocalmicroscopy and multicolor, large depth-of-field imaging under aperture modulation. It provides a promisingmechanism of system characterization and computational demodulation for high-contrast and high-resolutionimaging of cellular and subcellular biological structures and life activities.
文摘目的针对运动模糊图像复原中,传统频谱法存在的检测误差大、抗噪性弱及有效检测范围有限等问题,本文提出一种融合高斯拉普拉斯(Laplacian of Gaussian,LoG)滤波与Radon变换的边缘增强频谱分析法,旨在实现点扩散函数(point spread function,PSF)参数的精准估计。方法基于脑部MRI仿真运动模糊模型,分析频谱中明暗条纹的分布特征,采用LoG滤波提取频谱亮条纹边缘,抑制中心宽条纹与Gibbs现象导致的干扰,生成保留方向特征的离散边缘点集;利用Radon变换包容非共线点集的抗噪特性(离散边缘点沿角度θ投影时,真实边缘贡献相干叠加,噪声点投影随机抵消),显著提升峰信噪比,进而精准定位模糊角度并计算模糊长度;采用配对t检验,对传统中心亮条纹检测方法与本文方法的PSF参数估计误差进行统计学分析比较。结果本研究方法的角度估计平均误差0.08°,显著低于传统方法的3.28°,长度估计平均误差0.15像素,传统方法为0.88像素,有效角度检测范围由传统方法的±60°扩展到0~180°,且组间误差差异均达极显著水平(P<0.001)。结论本方法通过LoG滤波与Radon变换的协同机制,避免了对中心条纹完整性的依赖,解决了宽条纹导致的检测失效问题,同时有效抑制了噪声和Gibbs现象导致的干扰,显著提高了运动模糊PSF参数估计的精度与鲁棒性,为医学影像运动伪影消除提供可靠的技术基础。
文摘空间变化PSF(Space-variant Point Spread Function,SVPSF)图像,即物空间各点的退化随位置的改变而改变的图像,由于其复原技术涉及到多个甚至海量PSF的提取、存储和运算,相对于空间不变PSF(Space-Invariant Point Spread Function,SIPSF)图像复原要困难得多。目前处理此类图像的主要方法包括空间坐标转换法,等晕区分块复原法,以减少数据存储量,降低计算量,提高收敛速度为目标的直接复原法等。本文回顾了这一课题的研究历史,对目前的研究工作进行了分析和总结,介绍了本实验室提出的结合GRM(Gradient Ringing Metric)评价算法的总变分最小化图像分块复原法,并提出了未来工作关注重点的展望。
