Performance optimization of the neutron-sensitive image intensifier used in neutron imaging

As a non-destructive testing technology,neutron imaging plays an important role in various fields,including material science,nuclear engineering,and fundamental science.An imaging detector with a neutron-sensitive image intensifier has been developed and demonstrated to achieve good spatial resolution and timing resolution.However,the influence of the working voltage on the performance of the neutron-sensitive imaging intensifier has not been studied.To optimize the performance of the neutron-sensitive image intensifier at different voltages,experiments have been performed at the China Spallation Neutron Source(CSNS)neutron beamline.The change in the light yield and imaging quality with different voltages has been acquired.It is shown that the image quality benefits from the high gain of the microchannel plate(MCP)and the high accelerating electric field between the MCP and the screen.Increasing the accelerating electric field is more effective than increasing the gain of MCPs for the improvement of the imaging quality.Increasing the total gain of the MCP stack can be realized more effectively by improving the gain of the standard MCP than that of the n MCP.These results offer a development direction for image intensifiers in the future.

More Than Lightening:A Self-Supervised Low-Light Image Enhancement Method Capable for Multiple Degradations

Low-light images suffer from low quality due to poor lighting conditions,noise pollution,and improper settings of cameras.To enhance low-light images,most existing methods rely on normal-light images for guidance but the collection of suitable normal-light images is difficult.In contrast,a self-supervised method breaks free from the reliance on normal-light data,resulting in more convenience and better generalization.Existing self-supervised methods primarily focus on illumination adjustment and design pixel-based adjustment methods,resulting in remnants of other degradations,uneven brightness and artifacts.In response,this paper proposes a self-supervised enhancement method,termed as SLIE.It can handle multiple degradations including illumination attenuation,noise pollution,and color shift,all in a self-supervised manner.Illumination attenuation is estimated based on physical principles and local neighborhood information.The removal and correction of noise and color shift removal are solely realized with noisy images and images with color shifts.Finally,the comprehensive and fully self-supervised approach can achieve better adaptability and generalization.It is applicable to various low light conditions,and can reproduce the original color of scenes in natural light.Extensive experiments conducted on four public datasets demonstrate the superiority of SLIE to thirteen state-of-the-art methods.Our code is available at https://github.com/hanna-xu/SLIE.

RF-Net: Unsupervised Low-Light Image Enhancement Based on Retinex and Exposure Fusion

Low-light image enhancement methods have limitations in addressing issues such as color distortion,lack of vibrancy,and uneven light distribution and often require paired training data.To address these issues,we propose a two-stage unsupervised low-light image enhancement algorithm called Retinex and Exposure Fusion Network(RFNet),which can overcome the problems of over-enhancement of the high dynamic range and under-enhancement of the low dynamic range in existing enhancement algorithms.This algorithm can better manage the challenges brought about by complex environments in real-world scenarios by training with unpaired low-light images and regular-light images.In the first stage,we design a multi-scale feature extraction module based on Retinex theory,capable of extracting details and structural information at different scales to generate high-quality illumination and reflection images.In the second stage,an exposure image generator is designed through the camera response mechanism function to acquire exposure images containing more dark features,and the generated images are fused with the original input images to complete the low-light image enhancement.Experiments show the effectiveness and rationality of each module designed in this paper.And the method reconstructs the details of contrast and color distribution,outperforms the current state-of-the-art methods in both qualitative and quantitative metrics,and shows excellent performance in the real world.

Intensified CCD Image Sensor

Work has been done with extending the useful imaging and detection range of CCD.This was accomplished through direct optical coupling and bonding of i-mage intensifiers to the CCD.It has been shown that the useful range of a CCD may be extended two orders of magnitude using these techniques in coupling a microchannel plate image intensifier to the CCD array.All of these works were done with presently available CCD made by China.

用于X射线像增强器便携成像的观瞄目镜设计研究

结合X射线像增强器便携成像这一特殊需求,分析了针对该类观瞄目镜开展人体工程学设计的必要性。从像增强器器件的层面,给出了该类观瞄目镜的系统设计约束与要求。在此基础上,给出了一个该类观瞄目镜的具体设计示例,分析了该类光学系统的具体优化设计方法,并分别从厚度、曲率半径、圆锥系数3个维度开展了具体的系统优化设计。最终,基于光学传递函数评价方法,逐步地对比分析了优化设计与系统性能提升之间的内在联系与对应关系。

基于离散系数与Harris角点的微光像增强器闪烁噪声测试方法

为了弥补信噪比无法在二维空间上准确定位分析像增强器闪烁噪声特性的不足,本文针对像增强器闪烁噪声特性设计了一种基于离散系数与Harris角点检测的微光像增强器闪烁噪声测试方法。本方法采用基于Gsense400BSI CMOS图像传感器的高帧频图像采集系统实现与像增强器荧光屏余晖时间相匹配的闪烁噪声图像采集。通过对连续多帧采集到的图像进行像素级离散系数计算,热点图实现可视化,与Harris角点检测算法能够准确分析像增强器荧光屏各区域内的闪烁噪声情况并准确标记荧光屏上的高亮噪点。实验结果表明,该方法能够实现像增强器闪烁噪声的二维分析与定位,从而为像增强器性能优化以及噪声特性测试提供技术支持。

紫外像增强器在航空发动机燃烧诊断中的应用

航空发动机燃烧过程中,OH^(*)、NO^(*)、NH^(*)等激发态自由基伴随着紫外波段的微弱自发光,且随燃烧的进程而快速变化。紫外像增强器最高具有百万倍电子增益、纳秒量级快门,可以实现紫外波段燃烧流场瞬态结构的捕获,为定量化测量、阐释燃烧特性随多物理参数的变化规律奠定基础。本文总结紫外像增强器在航空发动机燃烧光学诊断中的应用,包括针对OH^(*)自发光进行成像的被动光学诊断技术、利用激光激发OH成像的主动光学诊断技术等。最后,结合新型航空发动机高时空分辨探测的要求,指出紫外像增强器在燃烧诊断中的发展方向。

影响电晕检测仪用紫外像增强管成品率因素分析

总结归纳了影响电晕检测仪用紫外像增强管成品率的因素,分析了器件不合格原因和机理,针对问题采取了有效的优化措施,较好地提升了紫外像增强管成品率,降低了生产成本,有利于促进产品的推广应用。

直耦增强型CMOS相机技术研究

直耦增强型CMOS(intensified complementary metal-oxide-semiconductor,ICMOS)相机通过将像增强器输出窗与CMOS直接耦合而成,具备灵敏度高、响应速度快、光谱范围可调等特点。本文根据ICMOS的组成结构,分析了阴极、微通道板、荧光屏、CMOS等各组成部件对直耦ICMOS成像性能的影响,提出ICMOS用像增强器、CMOS成像器件的选型依据,并结合公司对像增强器的加工优势,基于18 mm NVT-7像增强器和1英寸CMOS开展了实际微光成像验证。结果表明,采用直耦方式的ICMOS相机能够在5×10^(-4)lx条件下清晰成像,分辨力达到16 lp/mm。此外,用于ICMOS耦合的像增强器增益不宜过高,4000 cd/(m^(2)·lx)比较适宜,另外,在增益适宜的条件下,荧光屏输出亮度对其性能影响不大。

微光像增强器光晕消失时间测试方法

光晕(halo)效应对微光像增强器探测的应用造成了不利影响且不可避免。微光像增强器的光晕消失时间缺乏测试方法,因此提出了基于数字目视的光晕消失时间测试系统。该系统通过开关电源给LED光源提供频率为25 Hz、占空比可调的脉冲信号,利用高帧率相机连续采集1500张像增强器经直径为3.5 mm小孔后的图像,其中包含若干个完整的明暗周期。通过重复计算标准差,去除偏离平均值的周期序列来优化周期信息,获得亮暗周期内光源熄灭的图片索引,同理可获得光晕消失的图片数量,从而计算得到光晕消失时间。编号为GZ318118A的像增强器光晕消失时间为3.33 ms。测试结果表明,测量装置的重复性为0.863%,可以对光晕消失时间进行有效测试。

DEANet: Decomposition Enhancement and Adjustment Network for Low-Light Image Enhancement

Poor illumination greatly affects the quality of obtained images.In this paper,a novel convolutional neural network named DEANet is proposed on the basis of Retinex for low-light image enhancement.DEANet combines the frequency and content information of images and is divided into three subnetworks:decomposition,enhancement,and adjustment networks,which perform image decomposition;denoising,contrast enhancement,and detail preservation;and image adjustment and generation,respectively.The model is trained on the public LOL dataset,and the experimental results show that it outperforms the existing state-of-the-art methods regarding visual effects and image quality.

基于DCGAN的紫外像增强器视场瑕疵图片的生成

传统数据增强方法容易过拟合,为了解决紫外像增强器视场瑕疵图像数据集样本不平衡的问题,提升基于深度学习的条纹状瑕疵识别精度,提出了一种基于深度卷积生成对抗网络(Deep Convolution Generative Adversarial Network,DCGAN)的紫外像增强器视场瑕疵图像生成方法。通过对DCGAN进行损失函数的改进以及添加卷积注意力机制的优化,建立了紫外像增强器视场瑕疵图像生成模型,成功实现了紫外像增强器视场瑕疵图像的生成。随后,利用图像质量评价指标以及瑕疵检测模型来验证生成图像的有效性。实验结果显示,生成的紫外像增强器视场瑕疵图像可以满足使用需求,将生成图像融合到真实图像中再输入瑕疵检测模型可提高其检测精度。这一研究成果为三代微光像增强器和紫外像增强器的基于深度学习的视场瑕疵检测提供了技术支撑。

国内紫外像增强器视场瑕疵检测技术研究现状

紫外像增强器是一种对紫外辐射敏感的成像器件,视场瑕疵是其成像效果的主要制约因素。目前,视场瑕疵检测技术主要分为人工和机器视觉两种方法。本文首先阐述了视场瑕疵的定义和检测标准。接着从瑕疵交叠靠近、大小和数量特性的角度,分析了视场瑕疵检测的难点。随后,重点介绍了紫外像增强器视场瑕疵检测技术的研究现状。结合当前的检测需求和不足,调研了深度学习技术在其他领域的瑕疵检测效果。最后,从理论上进行了可行性分析,并提出了基于深度学习视场瑕疵检测的思路,旨在为紫外像增强器视场瑕疵检测提供一种新的解决方案,推动其向着更加实用、智能化的方向发展。

Neural Network-Powered License Plate Recognition System Design

The development of scientific inquiry and research has yielded numerous benefits in the realm of intelligent traffic control systems, particularly in the realm of automatic license plate recognition for vehicles. The design of license plate recognition algorithms has undergone digitalization through the utilization of neural networks. In contemporary times, there is a growing demand for vehicle surveillance due to the need for efficient vehicle processing and traffic management. The design, development, and implementation of a license plate recognition system hold significant social, economic, and academic importance. The study aims to present contemporary methodologies and empirical findings pertaining to automated license plate recognition. The primary focus of the automatic license plate recognition algorithm was on image extraction, character segmentation, and recognition. The task of character segmentation has been identified as the most challenging function based on my observations. The license plate recognition project that we designed demonstrated the effectiveness of this method across various observed conditions. Particularly in low-light environments, such as during periods of limited illumination or inclement weather characterized by precipitation. The method has been subjected to testing using a sample size of fifty images, resulting in a 100% accuracy rate. The findings of this study demonstrate the project’s ability to effectively determine the optimal outcomes of simulations.

Toward Robust and Efficient Low-Light Image Enhancement:Progressive Attentive Retinex Architecture Search

In recent years,learning-based low-light image enhancement methods have shown excellent performance,but the heuristic design adopted by most methods requires high engineering skills for developers,causing expensive inference costs that are unfriendly to the hardware platform.To handle this issue,we propose to automatically discover an efficient architecture,called progressive attentive Retinex network(PAR-Net).We define a new attentive Retinex framework by introducing the attention mechanism to strengthen structural representation.A multi-level search space containing micro-level on the operation and macro-level on the cell is established to realize meticulous construction.To endow the searched architecture with the hardware-aware property,we develop a latency-constrained progressive search strategy that successfully improves the model capability by explicitly expressing the intrinsic relationship between different models defined in the attentive Retinex framework.Extensive quantitative and qualitative experimental results fully justify the superiority of our proposed approach against other state-of-the-art methods.A series of analytical evaluations is performed to illustrate the validity of our proposed algorithm.

Fast fluorescence lifetime imaging techniques:A review on challenge and development

Fluorescence lifetime imaging microscopy(FLIM)is increasingly used in biomedicine,material science,chemistry,and other related research fields,because of its advantages of high specificity and sensitivity in monitoring cellular microenvironments,studying interaction between proteins,metabolic state,screening drugs and analyzing their efficacy,characterizing novel materials,and diagnosing early cancers.Understandably,there is a large interest in obtaining FLIM data within an acquisition time as short as possible.Consequently,there is currently a technology that advances towards faster and faster FLIM recording.However,the maximum speed of a recording technique is only part of the problerm.The acquisition time of a FLIM image is a complex function of many factors.These include the photon rate that can be obtained from the sample,the amount of information a technique extracts from the decay functions,the fficiency at which it determines fluorescence decay parameters from the recorded photons,the demands for the accuracy of these parameters,the number of pixels,and the lateral and axial resolutions that are obtained in biological materials.Starting from a discussion of the parameters which determine the acquisition time,this review will describe existing and emerging FLIM techniques and data analysis algo-rithms,and analyze their performance and recording speed in biological and biomedical applications.

Simulating the Intensifying Effect of Phosphors on Imaging Plate with UV photons

It is assumed from the energy level that ultraviolet (UV) photons may have intensifying effect on BaFBr∶Eu 2+. In this paper, effect of UV photons (220～290 nm) on the PSL intensity of X-ray irradiated BaFBr∶Eu 2+ was measured and compared to that on the PSL intensity of 220 nm photons irradiated BaFBr∶Eu 2+. It was found that after the excitation of UV photons the PSL intensity of X-ray irradiated samples decreases least at 250 nm and that of 220 nm photons irradiated samples increases most at 250 nm. When the irradiation sources are X-rays and 220 nm photons the excited electrons are photoelectron and thermal-electrons, respectively, and they have different possibility of being captured by electron traps or combined with luminescent centers. And the peak at 250 nm can be explained with the model of electrons tunneling. It is assumed that the electrons excited by 250 nm have the most possibility of tunneling.

超二代像增强器分辨力提高方法

为进一步提高超二代像增强器分辨力,分析了阴极输入窗、多碱光电阴极、微通道板、荧光屏等对超二代像增强器分辨力的影响,提出了减小阴极近贴距离、减小微通道板通道孔径、减小光纤面板输出窗丝径以及对微通道板镀制防电子弥散膜来提高分辨力的方法,并通过实验得到了验证。实验结果表明,随着阴极近贴距离的不断减小,分辨力可以得到逐步提高。阴极近贴距离为0.08mm的条件下,缩小光纤面板输出窗的丝径,缩小微通道板的孔径,且在微通道板的输出面镀制防电子弥散膜,可以使超二代像增强器的分辨力达到72 lp/mm,最高可达到76 lp/mm,比原有超二代像增强器的分辨力提高了33.33%。

像增强器性能梯次及发展路线研究

描述了一种判断夜视仪用微光像增强器性能梯次的方法,基于视距模型对影响探测能力因素进行了分析,研究了积分灵敏度、极限分辨力特性测试条件与实际夜视环境的差异,分析了夜天光辐射光谱特性、大气传输的光谱衰减特性、背景反射特性的光谱差异以及光阴极响应光谱特性对视距的影响,梳理了传统上以极限探测性能来判断像增强器优劣方法的不足,提出了“能力因数”模型,包含信噪比、低照度及低对比度下分辨力参数和技术特征因素,采用该方法对像增强器两大技术路线(砷化镓器件、多碱器件)进行梯次和代际分析,设计了三代像增强器发展路线。结果表明,“能力因数”模型(Figure of Capability,FOC)能够准确反映像增强器技术路线和性能梯次发展规律。

日盲紫外像增强器产品及标准情况分析

日盲紫外像增强器是电晕检测仪的核心器件,在国民经济和生活中具有重要作用。本文介绍了日盲紫外像增强器的工作原理和产品发展状况,分析了国家标准《日盲紫外像增强器技术要求》的意义和标准主要内容。