Advances in Underwater Photoelectric Imaging Technology
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摘要: 随着我国海洋、江河和地下水资源勘探、开发和利用的日益深入，以及领海主权防卫的军事需求日趋迫切，在水下获取远距离条件下高质量的目标图像已成为水下环境勘测、目标探测与敌我对抗等许多领域迫切需要解决的问题。目前，水下成像探测技术主要有声探测和光电探测两种途径。本文研究了目前主要水下高分辨力光电探测成像技术现状，分析了不同技术途径的优缺点，对比了各种水下探测/成像系统中采用的光电探测器的情况，结合自身技术背景，提出了应加快发展高灵敏度、低噪声、高增益、快响应、宽动态范围、良好线性度的GaAsP光阴极双微通道板像增强器，从而简化光电系统中因探测器性能不佳带来的灵敏度低、噪声大、增益低、处理时间长等不足，加速各种新技术向产品、实用化设备的转化。本文成果对水下光电成像技术发展将有一定支撑作用。Abstract: With the increasing exploration, development, and utilization of China's oceans, rivers, and groundwater resources, as well as the increasingly urgent military need for sovereign defense in territorial waters, obtaining high-quality target images under long-distance underwater conditions has become an urgent problem to be solved in many fields, such as underwater environmental surveys, target detection, and enemy-self confrontation. Currently, the underwater imaging detection technology includes two main methods: acoustic and photoelectric detection. In this study, the current status of the main underwater high-resolution photoelectric detection imaging technology is studied, the advantages and disadvantages of different technical approaches are analyzed, and the photodetectors used in various underwater detection/imaging systems are compared. Combined with its own technical background, it is proposed that the development of a GaAsP photocathode dual microchannel plate image intensifier with high sensitivity, low noise, high gain, fast response, wide dynamic range, and good linearity should be accelerated to simplify the low sensitivity, high noise, low gain, and long processing time, owing to the poor performance of the detector in the photoelectric system, and accelerate the conversion speed of various new technologies into products and practical equipment. The results of this study support the development of underwater photoelectric imaging technology.
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Keywords:
- underwater detection /
- photoelectric imaging /
- detector
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0. 引言
随着控制和通信技术的快速发展与应用，传统的工业逐渐实现自动化、智能化[1]。尤其是钢铁冶金方面，智能自动化的实现解决了很多传统操作存在的安全隐患问题，如基于红外测温仪的钢水测温系统[2-3]。红外测温系统的应用逐渐替代了传统的人工测温，不仅提高了钢水的测温精度，还减少了人工测温的安全事故。
目前，红外测温技术在冶金行业得到广泛应用，尤其是钢水测温方面。其中钢水辐射的红外波长位于0.75~1000 μm，红外测温技术基于钢水辐射的波长能量，得到其表面对面的温度[4]。以上红外测温技术是基于红外辐射理论，即，自然界任何物体（温度在绝对零度以上）时时刻刻都在以电磁波方式向外辐射不同波长的能量。基于这个原理，红外测温技术可以根据辐射体的辐射波长能量，得到其表面对面的温度量[5]。基于钢水的红外热图像，可以得到实时的钢水温度[6]。红外测温原理主要依据于普朗克黑体定律、斯特藩-玻尔兹曼定律和维恩位移定律[7]。根据钢水辐射能量的红外分布图，可以得到对应的辐射体/钢水的温度。即钢水测量温度的准确性取决于由红外热像仪获得的图像的质量[8-9]。
由于实际炼钢环境和测温仪器等不确定性因素的影响，获得的钢水红外热图像存在大量噪声，直接影响最后的钢水测温精度[10]。目前，传统的红外图像去噪处理方法有维纳滤波去噪[11]和稀疏分解去噪[12]等方法。但这些方法都是假设钢水红外图像中的噪声都是独立普通的高斯白噪声，因此对于钢水红外图像中的混合噪声处理效果不太理想。
为了提高钢水红外图像的去噪效果，本文提出基于自适应维纳滤波的去噪方法。与基于稀疏分解去噪方法相比，本文所提方法在去除噪声后提高了钢水红外图像的细节信息保真度，即去噪后的图像更加真实。此外，在传统维纳滤波去噪方法基础上，本文提出的去噪方法通过建立信号和噪声的相关模型来改进小波去噪。通过自相关的参数指数衰减模型来控制算法的计算复杂性和敏感性。由此产生的自适应维纳滤波适应于小波系数，并有效提高了传统维纳滤波器的去降噪性能。具体验证过程如下：首先，通过钢水测温平台获得不同温度下的钢水红外热图像。然后，利用所提方法对钢水红外热图像进行去噪处理，并与目前存在的维纳滤波去噪和稀疏分解去噪方法进行对比。实验对比结果验证了本文所提去噪方法可以去除红外热图像的噪声，并提高了去噪后钢水红外图像的峰值信噪比。
1. 稀疏分解的去噪方法
与传统图像去噪方法不同，稀疏分解图像去噪方法从图像自身的统计特性出发，将图像分解成稀疏成分和其他成分，如下式：
$$ x(m,n) = {x_1}(m,n) + {x_2}(m,n) $$ (1) 式中：$ {x}_{1}(m,n){=}{\displaystyle \sum _{k=0}^{n-1}\langle {R}^{k}x(m,n)，{g}_{\gamma k}\rangle }$；$ {x}_{2}(m,n){=}{\displaystyle \sum _{k=\rm{n}}^{\infty }\langle {R}^{k}x(m,n)，{g}_{\gamma k}\rangle }$；x(m, n)为原始图像；x1(m, n)为图像的稀疏成分（有用信息）；x2(m, n)为图像的其他成分（噪声）；gγk为由参数组γ定义的原子，〈Rkx(m, n), gγk〉为图像x(m, n)的残余Rkx(m, n)在对应原子gγk上的分量，然后以图像的稀疏成分为基础重建图像，得到去除噪声后的图像。
2. 传统维纳滤波器设计
假设图像信号s(m, n)含有噪声信号w(m, n)，含有噪声的图像估计信号为：
$$ x(m,n) = s(m,n) + w(m,n) $$ (2) 线性估计器为：
$$\hat s\left( {m,n} \right) = x\left( {m,n} \right)*h\left( {m,n} \right)$$ (3) 式中：h(m, n)最小均方误差。此外依据正交性原理，最优解h维纳滤波器满足：
$$ {R_{{\rm{ss}}}}(m,n) = {\left[ {{R_{{\rm{ss}}}}(m,n) + {R_{{\rm{ww}}}}(m,n)} \right]^*}h(m,n) $$ (4) 滤波器h的傅里叶变换为：
$$H({w_1},{w_2}) = \frac{{{P_{{\rm{ss}}}}({w_1},{w_2})}}{{{P_{{\rm{ss}}}}({w_1},{w_2}) + {P_{{\rm{ww}}}}({w_1},{w_2})}}$$ (5) 式中：Rss(m, n)和Rww(m, n)是图像信号s和噪声w的自相关函数；Pss(w1, w2)＝σs2是图像信号样本s(m, n)的功率谱；Pww(w1, w2)＝σn2是噪声w(m, n)的功率谱，其中σs2和σn2分别表示s(m, n)和w(m, n)的方差。
因此，我们可以得到维纳滤波器为：
$$h(m,n) = \frac{{\sigma _{\rm{s}}^{\rm{2}}}}{{\sigma _{\rm{s}}^{\rm{2}} + \sigma _{\rm{n}}^{\rm{2}}}}\delta (m,n)$$ (6) 综上，可以发现最小均方平方误差（Minimum mean square error，MMSE）估计的解是简单的，但是其对信号和噪声的不相关假设并不完全准确。同样考虑小波变换的情况下，非正交结构会在变换域中导致有色噪声，从而使MMSE的解无效。
3. 自适应维纳滤波器设计
为了限制计算复杂度并避免滤波器阶数增长带来的灵敏度问题和变换域导致的噪声问题，我们提出了一种自适应维纳滤波器，该滤波器使用指数衰减自相关模型进行设计。
类似于式(2)，FIR（Finite Impulse Response）维纳滤波器的卷积表达式为：
$$\hat s(m,n) = \sum\limits_{(i,j) \in {W_{m,n}}} {h(i,j)x(m - i,n - j)} $$ (7) $$ {W_{m,n}} = \{ (i,j);m - M \leqslant i \leqslant m + M,n - M \leqslant j \leqslant n + M\} $$ (8) 式中：Wm, n表示中心在(m, n)的(2M+1)(2M+1)的方形窗口函数；M是自适应滤波器的阶数。等式(7)右侧形成一个有限的块-Toeplitz矩阵[13]：
$$({\mathit{\boldsymbol{R}}_s} + {\mathit{\boldsymbol{R}}_w})\Re = \zeta $$ (9) 式中：Rs和Rw是(2M+1)(2M+1)的Toeplitz矩阵，对应于两组自相关函数；$\mathfrak{R}$和ζ是关于滤波器系数和信号s自相关函数的(2M+1)2×1向量。从方程(5)我们知道滤波器依赖于信号和噪声的相关性。一旦确定了相关系数，滤波器设计问题就简化为求解线性系统。
实验结果表明，自然图像的小波系数具有一定的聚类特性。换言之，小波系数的大小与其邻域无关。这种依赖性随着距离的增加而迅速衰减。使用简化符号rm, n: ＝Rss(m, n)，我们提出一个指数衰减模型：
$${r_{m,n}} = {r_{0,0}}{\gamma ^{\left| m \right| + \left| n \right|}},(m,n) \in {W_{0,0}}$$ (10) 式中：γ是衰变参数，其随小波系数的尺度变化而变化。指数衰减模型代表了真实图像在其邻居上的系数。此外，频带内自相关函数的变化通过变化的局部方差建模：
$${s_{m,n}}: = {R_{{\rm{ww}}}}(m,n) = \left\{ \begin{gathered} \sigma _n^2,\quad \quad m = 0,n = 0 \\ {\delta _1}\sigma _n^2,\quad {\kern 1pt} \left| m \right| + \left| n \right| = 1 \\ {\delta _2}\sigma _n^2,\quad \left| m \right| = \left| n \right| = 1 \\ 0,\quad \quad \;\,{\rm{else}} \\ \end{gathered} \right.$$ (11) 式中：δ是表示是特定表示尺度上小波域系数归一化的界。由于δ是随尺度变化，因此噪声的相关模型也是变化的。
为了估计在相关模型中的参数，我们为每个频带中的所有系数选择一个通用的衰减参数γ，但是信号方差σs2＝r0, 0是根据上下文从每个单独的系数估计的。综上，估计信号方差$\hat \sigma _s^2(m,n)$表达式为：
$${\hat m_s}(m,n) = \frac{1}{{{{{\rm{(2}}M + 1)}^2}}}\sum\limits_{{W_{m,n}}} {x(k,l)} $$ (12) $$\hat \sigma _x^2(m,n) = \frac{1}{{{{{\rm{(2}}M + 1)}^2}}}\sum\limits_{{W_{m,n}}} {(x(k,l) - } {\hat m_s}(m,n){)^2}$$ (13) $$\hat \sigma _s^2(m,n) = \max (0,\hat \sigma _x^2(m,n) - \sigma _n^2)$$ (14) 基于小波域中的系数聚类，通过式(12)~(13)建立信号和噪声的相关模型来改进小波去噪。通过自相关的参数指数衰减模型来控制算法的计算复杂性和敏感性。由此产生的自适应维纳滤波适应于小波系数，因此可以有效提高维纳滤波器的去降噪性能。
4. 实验及数据分析
4.1 实验方案和去噪评价标准
如图 1所示，实验平台采用的设备如下：中频率和对应电源变频柜的输出参数为额定功率1000 kV，额定频率700 Hz，输入电压750 V/50 Hz；红外热像仪型号为FLIR A65，灵敏度小于等于0.08℃。透镜型号为OLA30.4-350。本实验中采用融化较小的钢坯，通过红外热像仪采集融化后的钢水红外热图像，其中对应现场参数修正信息如表 1所示。
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图 1 钢水红外温度检测系统结构图Figure 1. Structural diagram of molten steel temperature detection system表 1 用于温度修正的现场参数信息Table 1. Information for temperature correctionTemperature/℃ 25.0 Target distance/m 2.2 Atmospheric transmittance/% 100 Window transmittance/% 100 Global emissivity/% 62 为了验证所提方法对钢水图像去噪的效果，本文用均方差（mean squared error，MSE）和峰值信噪比（peak-signal-to-noise ratio，PSNR）作为评价参数[14]。设U1(m, n)表示原始红外图像，U2(m, n)表示去噪后的红外图像，其中m和n代表行和列，则对应的均方差和峰值信噪比计算公式如下：：
$$ {\rm{MSE}}={{\displaystyle \sum _{m,n}[{U}_{1}(m,n）{-}{U}_{2}(m,n）]}}^{\frac{1}{2}}$$ (15) $$ {\rm{PSNR}}=10\mathrm{lg}\left\{\frac{K\times {\rm{MAX}}^{2}}{{{\displaystyle \sum _{m,n}[{U}_{1}(m,n）{-}{U}_{2}(m,n）]}}^{2}}\right\}$$ (16) 由上可知，去噪后图像对应的PSNR数值越高、MSE数值越小暗示去噪方法对钢水红外图像中的噪声处理的效果越好。
4.2 数据分析
本实验用红外热像仪分别采集温度为1600℃和1696℃下的钢水红外热图像，用本文提出的自适应维纳滤波方法进行去噪处理。对去噪后的图像分别与基于维纳滤波和基于稀疏分解去噪结果进行对比，通过对比各方法的MSE和PSNR评价参数验证本文去噪方法的优越性。
如图 2所示，对采集的1600℃时钢水红外图像进行去噪处理。Fig.2(b)~Fig.2(c)是采用维纳滤波和稀疏分解方法进行去噪处理后的钢水红外图像，Fig.2(d)为本文去噪方法处理后所得钢水红外图像。同样，对在1696℃时钢水红外图像进行去噪处理。基于以上3种去噪方法，所得去噪后的钢水红外图像如图Fig.3(b)~Fig.3(d)所示。
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图 2 1600℃的钢水红外图像去噪对比Figure 2. Denoising comparison of molten steel infrared image at 1600℃![]()
图 3 1696℃的钢水红外图像去噪对比Figure 3. Denoising comparison of molten steel infrared image at 1696℃基于图 2和图 3的去噪后钢水红外图像，我们用MSE和PSNR来评价去噪效果，如表 2所示。我们可以发现基于自适应维纳滤波去噪后图像的MSE和PSNR优于基于维纳滤波和稀疏分解去噪方法。
表 2 不同温度下钢水红外图像去噪效果对比Table 2. Comparison of denoising effect of molten steel infrared image under different temperaturesNoise processing method MSE PSNR/dB 1600℃ 1696℃ 1600℃ 1696℃ Wiener filter
Sparse decomposition
FIR wiener filter0.1130
0.0906
0.07980.1261
0.1001
0.080510.235
18.539
25.68310.095
19.168
26.956基于文献[10]中钢水温度与红外热图像灰度值之间的对应函数关系，我们取热电偶实时测量钢水温度1600℃时的去噪图像分析不同去噪方法对钢水测量精度的影响。表 3的对比数据验证了本文提出的去噪方法可以提高去噪后的钢水红外热图像对应的钢水温度准确性。
表 3 钢水温度数据对比Table 3. Comparison of the steel temperature data5. 结论
本文针对钢水红外图像存的噪声处理问题，提出了基于自适应维纳滤波的去噪处理方法。通过实验验证，所提的去噪方法可以有效地去除噪声。此外，与基于维纳滤波和稀疏分解去噪方法的对比，所提去噪方法可以更好去除钢水红外图像的噪声，提高图像质量保真度。下一步我们将继续优化所提去噪方法的计算复杂度，以便快速地应用于实际钢水红外测温系统中。
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图 16 成像效果对比图[54]：(a) 字母“XiDian UNIVERSITY”的原始图像；(b) 字母“XiDian UNIVERSITY”的被动水下偏振成像方法的结果；(c) 地中海的原始图像；(d) 地中海被动水下偏振成像方法的结果；(e) 图(d)中A区域的放大结果；(f) 图(d)中B区域的放大结果
Figure 16. Comparison of imaging effects[54]: (a) The original image of the letters"XiDian UNIVERSITY"; (b) The results by passive underwater polarization imaging method of the letters" XiDian UNIVERSITY"; (c) The original image of the Mediterranean; (d) The results by passive underwater polarization imaging method of the Mediterranean; (e) The enlarged result of area A in Fig (d); (f) The enlarged result of area B in Fig (d)
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表 1 各种水下光电成像技术的对比
Table 1 Comparison of various underwater photoelectric imaging technologies
Underwater imaging technology Advantages Fault Range-gated imaging High spatial resolution, small detector unit size, low cost and high imaging quality The requirements for laser, receiver and synchronous control technology are high Laser line scanning The imaging distance is far and the image precision is high The system has complex structure, high cost and large volume Structured light imaging technology With high integration, low cost and high resolution, underwater three-dimensional micro-topography can be obtained Fast and convenient measurement cannot be carried out, and the measurement accuracy is not high enough Scattering light computational imaging technology It has high imaging resolution, long detection distance, large optical field of view and low volume power consumption The process of establishing the model is not easy, the algorithm calculation process is complex, and the calculation amount is large. Polarized light imaging technology More information can be detected, and the detection ability in turbid water is strong. Color images can be obtained under special conditions. The stability of the system is general and easily affected by environmental factors. Underwater ghost imaging technology High sensitivity, anti-interference, wide working wavelength and long imaging distance. The structure complexity is very high, and the performance stability is not good enough. Underwater imaging based on deep learning Excellent imaging quality, strong adaptability and simple structure The construction, training and optimization of neural network are complex. 
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表 2 各种水下光电成像/探测系统的探测器的对比分析
Table 2 comparative analysis of detectors for various underwater photoelectric imaging/detection systems
Detector name Sensitivity of underwater blue-green light Dark current Response time Gain Linearity Photomultiplier tube High Low Fast High Good Avalanche photo diode Common High Common High Good Single photon detector High Common Common High Good Low-light-level image intensifier High Low Fast High Good CCD/CMOS Common Common Common Common Good
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