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Applied Physics 2021
基于像增强器的紫外光谱探测系统中的噪声分析
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Abstract:
为大幅度提升紫外光谱探测的灵敏度,本文将紫外像增强器与CCD器件耦合而成的紫外ICCD与分光组件再次耦合,构成直读式紫外光谱探测系统,并对其进行研究。设计了基于像增强器的紫外光谱探测系统,并分别对紫外像增强器的噪声和CCD相机的噪声进行了具体分析。研究实验结果表明:曝光时间越长,CCD图像噪声越小,灰度曲线越平滑;反之,曝光时间越短,CCD图像受噪声影响越大,灰度曲线越剧烈。研究发现,帧积分时间的延长,能够有效降低随机噪声,使图像信噪比增高。本文工作为减少噪声对系统的影响从而提高探测效率,极大拓展紫外光谱探测技术的应用领域,同时为微光夜视技术的发展提供依据。
In order to substantially improve the sensitivity of UV spectral detection, this paper couples the UV ICCD, which is made by coupling the UV image intensifier and CCD device, with the spectral component again to form a direct reading UV spectral detection system. The UV spectral detection system based on the image intensifier is designed, and the noise of the UV image intensifier and the noise of the CCD camera are specifically analyzed respectively. The experimental results of the study show that the longer the exposure time is, the less noise the CCD image has and the smoother the grey scale curve is; conversely, the shorter the exposure time is, the more the CCD image is affected by noise and the more drastic the grey scale curve is. It is found that the extension of the frame integration time can effectively reduce the random noise and make the image signal-to-noise ratio higher. The work in this paper provides a basis for reducing the impact of noise on the system and thus improving the detection efficiency, greatly expanding the application area of UV spectral detection technology, and providing a basis for the development of micro-optical night vision technology.
[1] | 余金中. 半导体光电子技术[M]. 北京: 化学工业出版社, 2003. |
[2] | 李志鹏, 李大成, 王燕燕. 一种基于日盲紫外定焦成像系统的海上搜寻定向系统[P]. 中国专利, CN201822186311.8, 2019-9-6. |
[3] | Lavigne, C., Roblin, A. and Langlois, S. (2002) Solar-Blind UV Imaging Photon Detector with Automatic Gain Control. Measurement Science & Technology, 13, 713. https://doi.org/10.1088/0957-0233/13/5/309 |
[4] | Ying, L. and Zhu, T. (2008) A Study on Solar Blind UV ICCD Detection Performance. Proc of SPIE, 6621, 21-27 |
[5] | Denvir, D.J. and Conroy, E. (2003) Electron-Multiplying CCD: The New ICCD. International Symposium on Optical Science and Technology, Vol. 4796, Seattle, 3-8 August 2003, 167-174. https://doi.org/10.1117/12.457779 |
[6] | 周蓓蓓. 电子倍增 CCD 的工作模式及其光子计数成像研究[D]: [博士学位论文]. 南京: 南京理工大学, 2010.
http://dx.chinadoi.cn/10.7666/d.y1919664 |
[7] | Chen, G.P., Gao, Z.S., Zhu, P.J. and Chen, Z.J. (2020) Learning a Mul-ti-Scale Deep Residual Network of Dilated-Convolution for Image Denoising. 2020 IEEE 5th International Conference on Cloud Computing and Big Data Analytics, Chengdu, 10-13 April 2020, 348-353. https://doi.org/10.1109/ICCCBDA49378.2020.9095754 |
[8] | Kumwilaisak, W., Piriyatharawet, T., Lasang, P. and Thatphithakkul, N. (2020) Image Denoising with Deep Convolutional Neural and Multi-directional Long Short-Term Memory Networks under Poisson Noise Environments. IEEE Access, 8, 86998-87010. https://doi.org/10.1109/ACCESS.2020.2991988 |
[9] | Saeedzarandi, M., Nezamabadi-Pour, H. and Jamalizadeh, A. (2020) Dual-Tree Complex Wavelet Coefficient Magnitude Modeling Using Scale Mixtures of Rayleigh Distribution for Image Denoising. Circuits, Systems, and Signal Processing, 39, 2968-2993. https://doi.org/10.1007/s00034-019-01291-y |
[10] | 陈梦雅, 李润鑫, 刘辉, 尚振宏. 基于预滤波的组稀疏残差约束图像去噪模型[J]. 传感器与微系统, 2020, 39(2): 48-51. http://dx.chinadoi.cn/10.13873/J.1000-9787(2020)02-0048-04 |
[11] | 常圆圆, 张选德. 基于多尺度相似先验的非局部图像去噪算法[J]. 电脑知识与技术, 2020, 16(2): 200-203. |
[12] | Seo, S. (2020) Image Denoising and Refinement Based on an Iteratively Reweighted Least Squares Filter. KSCE Journal of Civil Engineering, 24, 943-953. https://doi.org/10.1007/s12205-020-2103-x |
[13] | 闫丰, 于子江, 于晓, 娄洪伟, 隋永新, 杨怀江. 电晕探测紫外ICCD相机图像噪声分析与处理[J]. 光学精密工程, 2006, 14(4): 709-713. |
[14] | 但唐仁, 田景全, 高延军, 等. 低强度X射线影像系统的噪声分析及图像去噪处理[J]. 发光学报, 2002, 23(6): 615-618. |
[15] | 许宏涛, 邵晓鹏, 王杨. CCD芯片性能参数测量系统[J]. 仪器仪表学报, 2011, 32(6增): 271-275. |
[16] | 罗转翼, 程桂芬. 随机信号处理与控制基础[M]. 北京: 化学工业出版社, 2002: 21-22. |
[17] | 刘一畅, 马伟, 徐士彪, 张晓鹏. 基于卷积神经网络的边缘保真图像去噪算法[J]. 计算机辅助设计与图形学学报, 2020(11): 1822-1831. |