DiriNet:An Estimation Network for Spectral Response Function and Point Spread Function

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.

Physics-constrained deep-inverse point spread function model:toward non-line-of-sight imaging reconstruction

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.

PET image reconstruction with a system matrix containing point spread function derived from single photon incidence response

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.

Point spread functions for a small gamma camera with pinhole collimator

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.

Bistatic Synthetic Aperture Radar Point Spread Function Characteristic Analysis

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.

A Knife-edge Input Point Spread Function Estimation Method for Document Images

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.

Single exposure passive three-dimensional information reconstruction based on an ordinary imaging system

Existing three-dimensional(3D) imaging technologies have issues such as requiring active illumination, multiple exposures, or coding modulation. We propose a passive single 3D imaging method based on an ordinary imaging system.Using the point spread function of the imaging system to realize the non-coding measurement on the target, the full-focus images and depth information of the 3D target can be extracted from a single two-dimensional(2D) image through the compressed sensing algorithm. Simulation and experiments show that this approach can complete passive 3D imaging based on an ordinary imaging system without any coding operations. This method can achieve millimeter-level vertical resolution under single exposure conditions and has the potential for real-time dynamic 3D imaging. It improves the efficiency of 3D information detection, reduces the complexity of the imaging system, and may be of considerable value to the field of computer vision and other related applications.

Analysis of the optical quality by determining the modulation transfer function for anterior corneal surface in myopes

AIM: To describe the characteristics of modulation transfer function (MTF) of anterior corneal surface, and obtain the the normal reference range of MTF at different spatial frequencies and optical zones of the anterior corneal surface in myopes. METHODS: Four hundred eyes from 200 patients were examined under SIRIUS corneal topography system. Phoenis analysis software was applied to simulate the MTF curves of anterior corneal surface at vertical and horizontal meridians at the 3, 4, 5, 6, 7mm optical zones of cornea. The MTF values at spatial frequencies of 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55 and 60 cycles/degree (c/d) were selected. RESULTS: The MTF curve of anterior corneal surface decreased rapidly from low to intermediate frequency (0-15cpd) at various optical zones of cornea, the value decreased to 0 slowly at higher frequency (>15cpd). With the increase of the optical zones of cornea, MTF curve decreased gradually. 3) In the range of 3 mm- 6 mm optical zones of the cornea, the MTF values measured at horizontal meridian were greater than the corresponding values at horizontal meridian of each spatial frequency, the difference was statistically significant (P<0.05). At 7 mm optical zones of cornea, the MTF values measured at horizontal meridian were less than the corresponding values at vertical meridian at 10-60 spatial frequencies (cpd), and the difference was statistically significant in 25, 30, 35, 40, 45, 50 cpd(P<0.05). CONCLUSION: MTF can be used to describe the imaging quality of optical systems at anterior corneal surface objectively in detail.

A High Speed Detection Scheme for Point Targets in a Multitarget Environment

The purpose of this paper is to develop a high speed detection scheme for moving and / or stationary point targets in a multitarget environment as registered in an IR image sequence. An iterative approximate 3-D line searching algorithm based upon the geometric representation of lines (for non-maneuvering targets in space) in a 3-D space is derived. The convergency of the algorithm is proved. An analysis is performed of the theoretical detection performance of the algorithm. The statistical experiment results show high effectiveness and computational efficiency of the algorithm in the case of low SNR. The idea may be employed to satisfy the real-time processing requirement of an IR system.

基于显著性区域检测的多尺度图像盲复原算法

针对大多数基于先验的盲图像去模糊算法耗时较长和显著边缘结构提取不理想的问题,提出一种基于显著性区域检测的多尺度图像盲复原算法。为了复原出更加清晰的图像,采用由粗略到精细的多尺度交替迭代框架构建图像金字塔。在图像单一尺度方面,首先提取出图像中具有强边缘结构的显著性区域,并对其施加l_(0)范数约束,提出显著映射先验;将显著性映射先验和最大后验概率相结合并引入传统图像去模糊模型中,构造出点扩散函数估算模型,利用半二次分裂算法解决模型的非凸问题;对点扩散函数进行复原时,利用点扩散函数相似度的变化量限制每个尺度中的过渡迭代;对模糊图像和最终估计的点扩散函数进行非盲解卷积,获得复原图像。实验结果表明:与现有的主流去模糊算法相比,新算法在合成数据集和真实数据集中都可以有效抑制振铃和伪影现象,得到了很好的视觉体验,且评价指标均优于对比算法,同时大大缩减了复原时间。

飞行时间和点扩散函数对18F-FDGPET/CT肺癌纵隔淋巴结转移的增益价值

目的探讨飞行时间(TOF)和点扩散函数(PSF)重建对18F-FDGPET/CT显像肺癌纵隔淋巴结转移的增益价值。资料与方法回顾性分析2020年3月9日—2021年7月23日在佛山市第一人民医院行PET/CT检查的肺癌纵隔淋巴结转移患者68例。分别采用有序子集最大期望值迭代法(OSEM)、OSEM+TOF、OSEM+PSF、OSEM+TOF+PSF重建图像,比较不同重建算法对肺癌纵隔淋巴结转移病灶的分辨能力,以及病灶信噪比(SNR)和标准化摄取值(SUV)的差异。结果使用OSEM+TOF+PSF重建可获得病灶SUVmax、SUVmean和SNR的最高值,与常规OSEM比较分别增加了21.99%、22.86%和60.14%(t=28.321、19.11、11.059,P均<0.01);其差异百分比在直径≤22 mm的较小病灶中明显大于直径>22 mm的较大病灶(24.1%比21.1%、25.3%比19.3%、70.6%比63.3%;Z=-3.658、-4.313、-2.154,P均<0.05),在SNR≤15.31的低对比度病灶中明显大于SNR>15.31的高对比度病灶(23.6%比21.4%、25.3%比21.1%、85.7%比46.0%;Z=-3.519、-2.336、-5.106,P均<0.05);在不同重建算法的病灶可检测性评价结果中,OSEM+TOF+PSF图像对纵隔淋巴结转移病灶的显示最清晰,其中87.4%的病灶为明确存在,显著高于OSEM图像的73.1%(χ^(2)=11.704,P=0.001),但OSEM+PSF图像中病灶明确存在的比例与OSEM比较并未显著增加(73.1%比75.8%;χ^(2)=0.361,P=0.548)。结论TOF和PSF结合能显著提高肺癌纵隔淋巴结转移病灶的探测能力,以及病灶SNR和SUV,尤其在小病灶和低对比度病灶中更为显著。

基于边缘约束与范数比值的图像盲复原

针对现有的研究方法对运动所引起的模糊图像进行复原时效果欠佳的问题,提出一种基于边缘约束与范数比值的图像盲复原算法.该算法首先对退化图像的边缘进行约束,得到图像的强边缘结构,提高了点扩散函数估计的准确率;然后对要估计的清晰图像构造范数比值的稀疏惩罚约束项,并将得到的强边缘信息与构造的范数比值惩罚约束项进行结合用于指引点扩散函数的复原;在对点扩散函数进行复原时,由粗到细多尺度交替迭代估计点扩散函数,使得迭代出的最大尺度更加精确;最后对图像进行非盲解卷积求解,使其复原.该算法将退化图像的边缘信息与构造的范数比值惩罚约束项进行结合指导点扩散函数的复原,可以抑制图像在复原过程中产生的大量伪迹.实验结果表明,该算法对恢复图像边缘细节具有很好的效果,可以得到优质的复原图像.

基于超声C扫图像反卷积的棒材缺陷定量方法

缺陷定量评价一直是无损检测领域研究的重点,超声波的扩散会导致C扫描图像边缘模糊,影响缺陷定量的准确性。为了提高棒材缺陷定量的准确性,提出了一种棒材超声C扫描图像反卷积方法。基于多元高斯声束模型(multi-gaussian beam model, MGB),根据超声波在曲面界面的传播规律,推导了棒材水浸超声成像系统的点扩散函数(point spread function, PSF)。利用四轴超声水浸检测系统对含有平底孔的尼龙棒进行了检测,得到了C扫描图像,然后用Richardson-Lucy(RL)迭代算法对C扫描图像进行反卷积处理。使用6 dB下降法分别对原始图像和反卷积处理后的图像进行定量分析。结果表明,反卷积处理后定量误差更小,特别是将直径1.5 mm平底孔的误差由88%降低到了18.7%。

类THAAD导引头气动光学效应计算研究

针对类美国末段高空域防御(Terminal High Altitude Area Defense,THAAD)系统的红外导引头外形,开展了气动光学效应计算分析,并将其用于飞行器设计。利用国家数值风洞工程高速流场计算软件NNW-HyFLOW,考虑热化学非平衡效应和材料传热耦合效应,对导引头典型状态的流场进行了模拟,获得了流场的密度、温度、压力等参数和窗口的温度场参数。基于流场参数,利用HyFLOW气动光学传输效应计算功能,开展了红外光学传输成像计算;利用HyFLOW气动光学辐射效应计算模块,开展了流场和光学窗口的热辐射计算。计算结果表明,类THAAD导引头在30 km以上飞行时,流场和光学窗口基本不会影响目标信号的光学传输成像,但流场和窗口的热辐射效应会对导引头识别目标造成影响。不过随着飞行高度的升高,这种影响会减小。

基于谱分析的红外成像模糊去除算法研究

针对红外成像中出现的像差和运动模糊问题,提出了一种基于谱分析的红外成像模糊去除算法。该方法通过对模糊问题的分析,利用红外图像频谱信息构建了模糊函数估计方法,通过数据拟合项和正则项的优化,实现红外图像的模糊去除。实验在运动模糊图像数据集和静态像差图像上进行测试,结果表明:在数据集模糊去除实验上,相比之前的方法,本文方法峰值信噪比(PSNR,peak signal-to-noise ratio)提升了2.01 dB,结果相似性(SSIM,structural similarity index)提升了0.06;静态像差图像在实验室条件下通过短波红外探测器采集,算法在主观上有较明显的复原效果,且针对高通量光学系统成像复原效果更为显著。

新一代帕洛马天文台光谱仪的虚拟刀口欠采样像质测量方法

天文光谱仪的成像质量可用点扩散函数或线扩散函数来表征,但其相对于光谱仪探测器通常为欠采样,很难直接测量。在新一代帕洛马天文台光谱仪的像质测试中引入虚拟刀口法进行光源物面扫描,并应用虚拟刀口法开展欠采样光谱仪像质及光源尺度测量,通过模拟和实验研究,对比分析过采样经典直边刀口法和过采样直接测量方法,验证了虚拟刀口法具有良好的测量精度。在693 nm处,直边刀口法测得光谱仪过采样线扩散函数的半高全宽为7.05μm,虚拟刀口法测量欠采样线扩散函数的半高全宽为7.14μm,测量误差为1.3%。在0.3"视场的光源尺度下,虚拟刀口法测得689、693、701 nm欠采样线扩散函数的半高全宽分别为24.2、27.7、30.8μm,以过采样直接测量结果为参考,其测量准确度分别为98.3%,93.9%和88.8%。实验结果表明,探测器成像严重欠采样时,采用光源物面扫描的虚拟刀口法可准确测量光谱仪的成像质量,该方法结合色散系数还可实现光谱仪分辨率的测量,相关研究工作可为天文光谱仪像质的直接测量提供借鉴。

折反式变形光学系统偏振像差分析及其对点扩散函数的影响

变形光学系统是一种具有双平面对称性的相对特殊的光学系统,其结构会引入非旋转对称的偏振像差。针对这一问题,本文构建一个折反式变形光学系统,并对该系统的偏振像差及其对点扩散函数的影响进行系统分析。基于三维偏振光线追迹对折反式变形光学系统进行仿真计算,获得偏振像差的详细数据,并计算各个表面的二向衰减、相位延迟分布特性以及系统的琼斯瞳、振幅响应矩阵、点扩散函数和偏振串扰对比度。结果表明:最大二向衰减为0.145,最大相位延迟为1.46×10^(-2)rad,均出现在次镜位置。2∶1变形比的光学系统的振幅响应函数在长焦端和短焦端方向的偏振串扰项存在40.6%的差异,偏振串扰将该变形光学系统的对比度限制在10^(-6)量级。高精度变形光学系统中的偏振像差不可忽略,可采用膜层设计和折反式结构等方法降低偏振像差影响。该研究结论可为变形光学系统在深空探测、相干通信系统等领域的设计提供参考。

计算成像技术中的点扩散函数工程

围绕光学成像中点扩散函数(Point spread function,PSF)在计算成像中的新内涵与应用,介绍了传统光学成像中PSF的概念以及PSF在光学系统设计中关键作用,并简要说明了几种利用PSF恢复图像算法以及图像评价指标。在此基础上以计算成像框架下信息传递的视角重新审视了PSF的内涵,从狭义、广义光学系统两个方面对计算成像领域中的相关研究进行了归纳总结,最后展望了PSF工程技术的应用前景及发展趋势。

用于三维单颗粒示踪的多焦面双螺旋点扩散函数显微研究

双螺旋点扩散函数(double-helix point spread function,DH-PSF)显微可实现纳米尺度的三维单颗粒示踪(three-dimensional single particle tracking,3D SPT),被广泛应用于生命科学等领域,但DH-PSF显微的成像景深和定位精度有限,限制了其在活体厚样品中的应用.为了解决此问题,本文提出了一种基于轴向分光棱镜的多焦面DH-PSF显微(z-splitter prism-based multifocus DH-PSF microscopy,ZPMDM)方法和系统,通过将基于轴向分光棱镜的多焦面显微与DH-PSF相结合,在无需扫描的情况下提高DH-PSF显微的轴向定位范围和定位精度,解决完整活细胞内3D SPT的大景深探测难题.通过系统标定,ZPMDM中3个通道的平均三维定位精度分别为σ_(L(x,y,z))=(4.4 nm,4.6 nm,10.5 nm),σ_(M(x,y,z))=(4.3 nm,4.2 nm,8.2 nm),以及σ_(R(x,y,z))=(4.8 nm,4.4 nm,10.3 nm),DH-PSF的有效景深扩展至6μm,实现了大景深范围内的甘油-水混合溶液中的荧光微球示踪,并初步研究了活巨噬细胞的吞噬现象,进一步验证了该方法的有效性,对于3D SPT的发展和应用具有重要意义.

基于深度学习的太赫兹去卷积图像超分辨率重建

太赫兹光谱成像技术不仅能获取检测对象在三维图像空间中的几何信息,同时也能提取其在太赫兹波段的光谱信息,因此该技术已在众多领域表现出巨大的应用潜力.但由于技术设备结构复杂,成本较高,且太赫兹波长较长导致其成像空间分辨率较低,边缘细节模糊,如何在现有设备基础上提高太赫兹成像分辨率成为目前亟需解决的关键问题.针对该问题,以STC89C51单片机为检测对象,利用太赫兹时域光谱数据,结合深度学习,实现了太赫兹光谱成像的超分辨率重建.试验采用太赫兹时域光谱检测系统对样品进行逐点扫描,指定0.738 THz进行频域成像,通过构建点扩散函数对太赫兹图像去卷积增强,并确定以光束穿透深度z=2 mm的去卷积太赫兹图像作为参考图像.考虑太赫兹图像实际采集过程中可能受到多种复杂噪声的影响,通过双三次插值降采样(BI)、高斯模糊下采样(BD)、双三次插值下采样+高斯白噪声(BN)与高斯模糊下采样+高斯白噪声(DN)4种不同的降采样方式模拟太赫兹降质图像.采用Real-ESRGAN深度学习方法对降质图像进行超分辨率重建,同时与SRResNet、EDSR、SRGAN、ESRGAN方法的重建结果进行对比.采用峰值信噪比、结构相似性以及主观平均得分3种评价指标对重建结果进行评价.评价结果表明:Real-ESRGAN的BI、BD、BN、DN 4种降质图像的超分辨率重建效果的各项指标表现均优于其他算法,实现了对芯片特征信息的增强和图像成像精度的提高,为太赫兹图像超分辨率重建技术提供了一种新的优化思路.