Research Progress of Silicon-based BIB Infrared Detector
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摘要: 以锗基和硅基为主的阻挡杂质带(blocked impurity band,BIB)红外探测器的兴起有力推进了红外天文学的快速发展,其中硅基BIB红外探测器在特定波长的航天航空领域有着不可替代的地位。国外对硅基BIB红外探测器的研究已有40多年,以美国航空航天局(NASA)为主的科研机构已经实现了硅基BIB红外探测器在天文领域的诸多应用,而国内对硅基BIB红外探测器的研究尚处于起步阶段。本文首先阐述了硅基BIB红外探测器的工作原理,然后简单概述了器件结构和制备工艺,并对不同类型的硅基BIB探测器的性能进行了对比分析,之后介绍了其在天文探测中的应用,最后对硅基BIB红外探测器未来的发展进行了展望。Abstract: The rise of blocked impurity band (BIB) infrared detectors based on germanium and silicon has promoted the rapid development of infrared astronomy, among which silicon-based BIB infrared detectors with specific wavelengths play an irreplaceable role in the aerospace field. Research on silicon-based BIB infrared detectors has been conducted abroad for more than 40 years, and many of its applications in the astronomical field have been realized by NASA and its related research institutes. However, domestic research on silicon-based BIB infrared detectors is still in its infancy. In this paper, the working principle of silicon BIB infrared detectors is described first; then, the structure and fabrication process of the device are briefly summarized, the performance of different types of silicon BIB detectors is compared and analyzed, and its application in astronomical detection is described. Finally, the future development of silicon BIB infrared detectors is discussed.
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Keywords:
- silicon-based BIB /
- infrared detector /
- astronomical detection
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0. 引言
地球上海洋面积广阔且具有丰富的资源,如今随着经济的发展,每个国家对资源的需求不断增长,然而相比海洋资源,陆地上的资源随着人类大量开采而不断减少,因此,扩大对海洋资源的开发利用是未来人类发展的方向。不同于陆地环境,海洋环境存在着许多复杂的问题,在海洋环境下成像问题是最基础的问题同时也是最大的问题,这直接影响着是否能够探索到海洋资源。通常直接获取的水下图像会出现严重的噪声干扰、颜色衰退、图像信息的丢失等问题,这直接造成无法提取到有用的信息,无法进行后续工作的开展,因此进行水下图像增强具有重要的意义。
水下图像增强一直是当前很多专家学者研究的方向。2018年,Mishra等[1]对CLAHE进行了改进并应用于水下图像增强,这种方法在很大程度上将水下图像的对比度得到提高,但是经过这种方法处理过的水下图像会出现部分细节模糊的现象。2019年,Sun等人[2]提出了一种暗通道先验结合MSRCR水下图像增强算法,该算法有效地解决了低照度水下图像颜色衰退的问题,但是增强后的图像会出现雾化的问题。2020年,Wang等人[3]提出了一种低照度多尺度Retinex水下图像增强算法。该方法可以有效避免图像在输出时产生光影,并且可以解决水下图像不清晰、对比度较差等问题。但是,实验结果表明水下图像在图像颜色衰退部分还是有待改进。同年,Dhanya等人[4]提出L-CLAHE增强滤波图像算法,该算法由L-CLAHE、增强和滤波三个模块组成。L-CLAHE模块的输出经过Gamma校正、直方图均衡化和双边滤波等强化和滤波阶段处理,结果表明,输出图像对比度得到改善,但是图像局部边缘细节视觉效果差。2021年,朱佳琦等人[5]提出一种红通道先验与CLAHE融合的水下增强算法,该算法首先利用红通道先验理论计算出预估透射率,然后在CLAHE算法增强图像前加入Gamma校正,实验结果表明,图像整体对比度得到改善,但是图像局部对比度差距较小时增强效果较差。范新南等人[6]提出一种MSRCR与多尺度融合的水下图像增强算法,该算法首先将图像进行色偏校正并转换到Lab颜色空间对亮度分量进行增强,然后对MSRCR色彩校正图像和Lab空间亮度分量进行多尺度分解并融合,增强后的水下图像色彩丰富,但是图像对比度没有得到有效改善。张薇等人[7]提出基于白平衡和相对全变分的低照度水下图像增强,算法采用灰度世界先验校正水下图像颜色,依据引导滤波的保边平滑性构造新的相对全变分约束来估计照度图,水下图像的颜色得到了校正,但是图像雾化严重对比度低,同时部分水下图像颜色容易校正过度。
针对图像颜色衰退和图像结构复杂会导致局部区域过度增强色彩不真实和对比度低边缘细节模糊的问题,本文针对水下图像颜色衰退的问题提出用融合导向滤波的MSRCR算法,针对水下图像对比度低的问题提出融合Gamma校正的CLAHE算法,同时针对水下图像边缘细节模糊采用多尺度融合来增强图像中的边缘细节信息。最后通过对比实验,以验证本文所提出方法的有效性。
1. 算法原理
1.1 改进的MSRCR
多尺度Retinex(MSR)[8]在水下图像增强中通常会出现图像颜色衰退严重,噪声没有得到抑制,引起增强后的图像整体视觉效果不佳。Jobson等[9]提出的带色彩恢复因子的MSR算法(MSRCR)可以有效地解决图像颜色失真的问题,MSRCR的计算公式如下:
$$ {R_{{\text{MSRCR}}}}(x, y) = {C_i}(x, y)\sum\limits_{n = 1}^N {{\omega _n}} \{ \ln I(x, y) - \ln [I(x, y)*{G_n}(x, y)]\} $$ (1) 式中:I(x, y)为输入图像;Ci(x, y)表示图像第i个通道的颜色恢复参数,用来调节不同颜色通道之间的比例关系;N表示尺度数目,通常为3;ωn为第n个尺度的加权系数;Gn(x, y)为高斯滤波函数。
高斯滤波通常易产生图像被过度光滑,导致图像缺乏边缘细节。针对此问题,本文在MSRCR中采用导向滤波[10]代替高斯低通滤波,以有效解决水下图像颜色失真的问题和MSRCR算法增强水下图像时易造成边缘细节丢失的问题。
导向滤波的基本原理为对于输出图像和导向图在滤波窗口存在局部线性关系,其公式如下:
$$ \begin{gathered} {q_i} = {p_i} - {n_i} \hfill \\ {q_i} = {a_k}{I_i} + {b_k}, \forall i \in {\omega _k} \hfill \\ \end{gathered} $$ (2) 式中:pi为输入图像;Ii为导向图;qi为输出图像;ni为噪声;ak, bk为局部线性函数系数;ωk为滤波窗口。
对于在确定的窗口ωk中,ak, bk将会是唯一的常量系数,这就保证了在局部区域里,如果导向图Ii有一个边缘的时候,输出图像qi也保持边缘不变。因此只要求得了系数ak, bk也就得到了输出图像qi。为使得输入图像和输出图像的差别特别小,而且还可以保持局部线性模型,利用带正则项的岭回归计算滤波窗口内的损失函数E(ak, bk),计算过程如下:
$$ E({a_k}, {b_k}) = \sum\limits_{i \in {\omega _k}}^{} {({{({a_k}{I_i} + {b_k} - {p_i})}^2} + \varepsilon {a_k}^2)} $$ (3) $$ \begin{gathered} {a_k} = \frac{{\frac{1}{{|\omega |}}\sum\limits_{i \in {\omega _k}}^{} {{I_i}{p_i} - {\mu _k}{{\overline p }_k}} }}{{{\sigma _k}^2 + \varepsilon }}, \quad {b_k} = {\overline p _k} - {a_k}{\mu _k} \hfill \\ {q_i} = \frac{1}{{|\omega |}}\sum\limits_{k|i \in {\omega _k}}^{} {({a_k}{I_i} + {b_k})} \hfill \\ \end{gathered} $$ (4) 式中:μk, σk2分别是导向图在ωk窗口大小的均值与方差;ε为正则化参数;|ω|表示窗口内像素总数$\overline {{p_k}} = \frac{1}{{|\omega |}}\sum\limits_{i \in {\omega _k}}^{} {{p_i}} $表示在ωk窗口内输入图像的像素均值。
使用线性相关参数(ak, bk),滤波输出图像就可以通过qi=akIi+bk线性模型得到。针对不同的窗口大小我们就会得到不同的导向滤波输出图像qi值。
本文在MSRCR算法中用导向滤波函数代替高斯低通滤波函数,具体计算如下:
$$ {R^*}_{{\text{MSRCR}}}(x, y) = {C_i}(x, y)\sum\limits_{n = 1}^N {{\omega _n}} \{ \ln I(x, y) - \ln [I(x, y)*D(x, y)]\} $$ (5) 式中:RMSRCR*(x, y)表示改进的MSRCR算法输出图像;D(x, y)为导向滤波函数。
图 1(a)为水下拍摄的原图,图像整体呈现出颜色失真的状态;图 1(b)为经过MSR处理后的图像,图像对比度有所提高,但是颜色矫正不佳,出现了色偏;图 1(c)为经过MSRCR处理后的图像,图像对比度明显提高,但是图像局部细节较为模糊;图 1(d)为改进的MSRCR处理后的图像相较于图 1(c)图像边缘细节清晰。
图 2(a)为经过MSRCR处理后选取局部区域放大后的图像,图 2(b)为经过改进MSRCR处理后选取局部区域放大后的图像。对比可得,图 2(b)的边缘细节更加清晰,改进后的MSRCR算法解决了MSRCR算法增强图像带来的部分边缘细节模糊的问题。
1.2 改进的CLAHE
常用的对比度增强算法[11-13]有HE(Histogram Equalization)、AHE(Adaptive Histogram Equalization)、CLAHE。HE算法主要是用来对图像的整体对比度进行增强,适用于图像的背景和前景接近的情况下。但是该方法在对较暗的区域均衡处理后由于亮度被拉得太高而出现噪点,并最终弱化了图像细节。AHE算法是在HE算法的基础上,将图像划分为几块分别处理,这样有利于处理图像数据的局部细节。但是该算法的复杂度较高,降低了图像的处理效率,同时图像块与块之间的过渡处理欠佳。CLAHE算法是在AHE算法的基础上进行改进,通过加入阈值对图像噪声进行抑制,同时通过使用线性插值的方法对图像区域块连接处进行优化,使图像整体变得平滑。但是CLAHE算法只提高了图像的对比度,然而并没有对图像的边缘细节信息进行增强。
综上,本文在CLAHE算法中引入Gamma校正[14],在图像经过CLAHE处理前、后都加入Gamma校正,以增强图像整体对比度同时提高图像局部对比度,尤其对于相邻区域之间相差较小时增强效果明显。具体步骤如下所示:
1)对图像进行Gamma校正并将图像分割成连续,非重叠的M×N的区域块,每个区域块含有的像素为n,区域块的大小与图像对比度的增强有着紧密的联系,区域块越大图像对比度增强越大,但是图像细节信息丢失的越多。
2)获取每个区域块的直方图,根据每个区域块的直方图分布规律计算裁剪幅值T。
$$ T = {C_{{\text{clip}}}} \times \frac{{{N_x} \times {N_y}}}{H} $$ (6) 式中:Cclip是裁剪系数;Nx, Ny为在每个子块x, y方向上的像素个数;H为灰度级数。
3)计算出图像区域块的分布直方图并设置阈值,将高于阈值的直方图部分进行切除,同时将该部分平均分布在直方图的下方,如图 3所示。
4)在重新分配后的直方图上,对每个区域块进行直方图均衡化,同时对区域块的位置进行像素重构。
5)对图像进行Gamma校正。如式(7)所示,当γ<1时,如果图像区域块输入灰度值低,那么图像区域块输出灰度值变化将变大,图像对比度将会增强;如果图像区域块输入灰度值高,那么图像区域块输出灰度值变化将变小,图像对比度将会降低。当γ>1时,如果图像区域块输入灰度值低,那么图像区域块输出灰度值变化将变小,图像对比度将会降低;如果图像区域块输入灰度值高,那么图像区域块输出灰度值变化将变大,图像对比度将会增强。
$$ s = c{\left( {r + \varepsilon } \right)^\gamma } $$ (7) 式中:c和ε为常量;γ为Gamma校正参数,该参数决定校正效果;r为输入灰度级;s为输出灰度级。
图 4是对图 1(d)所示的水下图像运用不同对比度增强算法得到结果。
由图 4可以看出,图 4(a)是HE算法处理后的图像,易看出图像对比度增强过度,许多不重要的背景噪声同时也被增强,图像部分细节没有得到增强反而变得模糊不清;图 4(b)是AHE算法处理后的图像,易看出图像块与块之间没有做过渡处理并且出现图像背景噪声被过度增强;图 4(c)是CLAHE算法处理后的图像,易看出图像整体对比度得到了改善,背景噪声没有出现增强过度,但是图像局部对比度增强不足,部分细节不清晰;图 4(d)是改进的CLAHE算法处理后的图像,该算法有效地增强了图像整体对比度的同时也增强了图像局部对比度。
1.3 图像多尺度融合
图像融合通常的做法就是对不同图像赋予不同的值,然后通过叠加得到最终的结果图,但是这种做法往往会出现图像细节不清晰,图像出现重影晕环。为了解决这个问题,本文采用多尺度图像金字塔来融合图像,多尺度图像融合指的是图像在不同尺度下进行融合,通常情况下在单一尺度很难获取图像特征然而在另外一种尺度下就很容易获取,为了极大可能地保留图像结构特征,采用多尺度图像融合是一种较好的方式之一。
1.3.1 图像权重的计算
单一的图像权重[15]不能完整地反映图像各个基本特征。因此,要想完整地反映图像各个基本特征需要融合多个图像权重,本文选取了拉普拉斯对比度权重、亮度权重、饱和度权重、显著性权重。
拉普拉斯对比度权重可以清楚地显示出图像的边缘特征信息,通过使用拉普拉斯滤波器可以得到图像的全局对比度,这样可以保证图像的边缘和纹理具有较高的值。亮度权重负责为具有良好可见性的像素分配高值,该权重图是通过观察输入的R、G、B三通道与亮度通道L(给定位置的像素强度的平均值)之间的偏差来计算;显著性权重为了突出显示水下图像具有更高显著性的区域,可以通过输入的平均值减去其高斯平滑后得到结果。饱和度权重用于调整图像中的饱和区域,以获得饱和度均匀的融合图像。归一化权重是对上述权重进行归一化处理。
图 5分别是颜色校正图像,颜色校正图像的拉普拉斯对比度、亮度、饱和度、显著性权重图以及归一化权重图。图 6分别是对比度增强图像,对比度增强图像的拉普拉斯对比度、亮度、饱和度、显著性权重图以及归一化权重图。
1.3.2 多尺度图像金字塔融合
图像金字塔技术是以不同角度展示图像细节的一种方式,高斯图像金字塔和拉普拉斯图像金字塔是最常见的图像金字塔技术,通常会结合这两种技术综合使用。对颜色校正图像和对比度增强图像的归一化权重图进行高斯金字塔分解,得到不同尺度的权重图;对颜色校正图像和对比度增强图像采用拉普拉斯金字塔分解,得到不同尺度的图像,最后将不同尺度的图像进行重建,得到最终的增强图。
高斯图像金字塔可以保持图像的结构纹理信息,首先会对图像进行高斯滤波并且进行连续下采样,从而得到多种分辨率的图像。
$$ \begin{gathered} {G_l}(i, j) = \sum\limits_{m = - 2}^2 {\sum\limits_{n = - 2}^2 {\omega (m, n){G_{l - 1}}(2i + m, 2j + n)} } \hfill \\ (1 \leqslant L \leqslant N, 1 \leqslant i \leqslant {R_L}, 1 \leqslant j \leqslant {C_L}) \hfill \\ \end{gathered} $$ (8) 式中:N表示图像金字塔不同的层数;RL, CL表示第L层输入图像的行和列;ω(m, n)表示高斯核函数。
拉普拉斯金字塔是用来重新构造出一幅图像,通过高斯金字塔得到的不同分辨率的图像,然后将每一层与上一层进行作差,同时进行上采样并且做高斯卷积,最终会得到不同的差值图像,通常称这些差值图像为拉普拉斯图像金字塔。
$$ {L_l} = {K_l} - {\text{Up}}({\text{Down}}({K_l})) $$ (9) 式中:Kl为原始输入图像;Ll为拉普拉斯金字塔分解图像;Up, Down分别为向上采样,向下采样。
多尺度金字塔融合计算公式为:
$$ \begin{gathered} {F_l}(x) = \sum\limits_k^{} {{G_l}[{{\overline W }_k}(x)]{L_l}[{I_k}(x)]} \hfill \\ F(x) = \sum\limits_l {{F_l}(x){ \uparrow ^d}} \hfill \\ \end{gathered} $$ (10) 式中:l为金字塔的不同层数;k为输入图像金字塔的索引;Gl为高斯金字塔的分解;L为拉普拉斯金字塔的分解;${\bar W_k}$表示权重值归一化;Ik表示输入图像;Fl(x)为多尺度融合图像;F(x)为最后的融合结果;↑d表示该过程采用上采样方式。
2. 算法流程
本文针对水下图像颜色衰退严重、对比度低及细节特征模糊等问题,提出一种改进的MSRCR与CLAHE多尺度融合的图像增强算法。图 7为多尺度图像融合的原理图及最终效果图,其中图 7(a)为水下图像经过颜色校正和对比度校正后的高斯金字塔图像和拉普拉斯图像,图 7(b)为水下图像经过颜色校正和对比度校正多尺度融合后的图像。为图 8为本文算法流程图,该算法首先将采集到的水下图像运用带导向滤波的MSRCR算法进行颜色校正;同时将颜色校正后的图像运用带有Gamma校正的CLAHE算法增强图像对比度;最后对经过颜色校正和对比度增强的水下图像进行多尺度图像融合得到最终水下增强图像。
3. 实验结果与分析
实验的硬件系统为CPU i7-10875H,16GB DDR4;软件仿真环境是Matlab2016a,Win10操作系统。为了求证本文算法的可行性,将本文算法和文献[3]、文献[7]、文献[13]、文献[16]的算法进行对比,同时从主观和客观方面进行对比分析。
3.1 主观评价
本文实验选择10种不同水下环境下的图像进行对比仿真实验,处理结果如图 9所示。
从图 9中可以看出,文献[3]算法整体上对图像细节清晰度和对比度有一定的提升,但是没有解决水下图像颜色衰退的问题;文献[7]算法增强后的水下图像颜色校正明显,但是对于偏蓝色的水下场景并没有很好的校正,并且图像对比度低,图像细节模糊;文献[13]算法基本解决了水下图像颜色衰退的问题,图像细节和对比度同时也得到了提升,但是对于偏蓝色的水下场景颜色校正效果差(如Picture 10);文献[16]算法整体上图像对比度提升明显,图像细节较为清晰,但是颜色校正效果稍显不足(如Picture 2,Picture 4,Picture 8);本文算法整体上解决了不同水下环境的颜色衰退问题,对比度大幅度的提高,图像局部细节清晰明显,符合自然光照下的图像。
3.2 客观评价
本文采用3种性能指标来评估水下图像质量,即PSNR、SSIM和UIQE[17-18]。PSNR是基于对应像素点间的误差计算,主要计算最大值信号与背景噪声之间的比值,其数值越大则表示失真越小,其计算公式为:
$$ {\text{MSE}} = \frac{1}{{H*W}}\sum\limits_{i = 1}^H {\sum\limits_{j = 1}^W {(X(i, j)} - Y(i, j){)^2}} $$ $$ {\text{PSNR}} = 10\lg (\frac{{{{({2^n} - 1)}^2}}}{{{\text{MSE}}}}) $$ (11) 式中:MSE表示图像的均方误差;H、W表示图像的宽,高;n表示图像像素的比特数。
SSIM是衡量两幅图像相似度的指标,其计算公式为:
$$ {\text{SSIM}} = \frac{{(2{x_1}{x_2} + {C_1})(2{y_{1, 2}} + {C_2})}}{{({x_1}^2 + {x_2}^2 + {C_1})({y_1}^2 + {y_2}^2 + {C_2})}} $$ (12) 式中:x1、y1表示输入图像的均值,标准差;y1、y2表示增强后图像的均值,标准差;y1, 2表示输入图像和增强后图像的协方差;C1、C2为常数。SSIM数值越大表示输入原图的结构损失越小。
UIQE是专门用来评价水下图像质量的指标,通常对评价颜色保真度、对比度、清晰度3个分量根据水下环境微调不同的权重参数,3个权重参数的确定需要通过多元的线性回归计算,最后线性相加不同的分量得出最终指标。
$$ {\text{UIQE}} = {c_1}*\alpha + {c_2}*\beta + {c_3}*\chi $$ (13) 式中:c1、c2、c3是不同分量的权重;α表示颜色保真度的测量指标;β表示对比度测量指标;χ表示清晰度的测量指标。
表 1 不同算法PSNR性能比较Table 1. PSNR performance comparison of different algorithmsPNSR Original Reference[3] Reference[7] Reference[13] Reference[16] Ours Picture 1 - 13.8014 14.2261 16.3929 21.0436 24.2896 Picture 2 - 6.2876 6.7461 6.2519 14.0142 18.9873 Picture 3 - 15.9585 12.5442 13.0328 20.9045 22.3212 Picture 4 - 7.3254 7.9521 8.1265 13.2158 19.9914 Picture 5 - 12.9871 14.8561 15.8516 17.3258 20.5563 Picture 6 - 15.6243 15.9985 16.2546 18.2319 24.7963 Picture 7 - 11.8274 13.2873 14.7931 19.2291 19.9639 Picture 8 - 10.2034 11.2544 15.2698 16.3245 19.3312 Picture 9 - 14.5758 15.9152 18.3223 20.5513 24.3698 Picture10 - 11.4522 12.3756 13.4851 16.6334 19.3497 表 2 不同算法SSIM性能比较Table 2. Performance comparison of different SSIM algorithmsSSIM Original Reference[3] Reference[7] Reference[13] Reference[16] Ours Picture 1 - 0.5609+ 0.5943 0.8321 0.8384 0.9611 Picture 2 - 0.5223 0.5081 0.6869 0.8612 0.8874 Picture 3 - 0.6186 0.7926 0.8031 0.8299 0.9212 Picture 4 - 0.5743 0.5178 0.8163 0.8752 0.9649 Picture 5 - 0.7121 0.7963 0.8263 0.8998 0.9088 Picture 6 - 0.6933 0.7432 0.7966 0.8364 0.8997 Picture 7 - 0.6074 0.5927 0.6988 0.7411 0.8796 Picture 8 - 0.5871 0.5988 0.6355 0.7843 0.8894 Picture 9 - 0.6121 0.6028 0.7123 0.7652 0.9126 Picture 10 - 0.5386 0.6103 0.7521 0.8419 0.8696 表 3 不同算法UIQE性能比较Table 3. UIQE performance comparison of different algorithmsUIQE Original Reference[3] Reference[7] Reference[13] Reference[16] Ours Picture 1 2.6449 4.9242 4.6436 5.0271 3.6167 5.2238 Picture 2 1.7252 4.0814 0.2696 3.9252 2.1831 4.9121 Picture 3 1.9542 3.0251 1.3447 3.5738 2.2406 4.4633 Picture 4 1.5241 4.5296 1.0328 4.2153 3.6574 5.9685 Picture 5 1.7551 3.1221 3.0217 3.9746 4.9962 6.2312 Picture 6 1.9978 2.2173 2.1179 4.5023 4.8785 6.0178 Picture 7 1.2212 1.3258 2.3647 4.2589 4.5565 6.9872 Picture 8 2.0121 2.2365 3.4562 4.2199 4.8456 5.5463 Picture 9 2.7853 2.8742 3.9893 4.5631 4.7987 6.2971 Picture 10 0.6721 2.9255 3.2372 3.4801 1.5899 3.6943 从表 1中可以看出本文算法的PSNR基本上高于其他文献算法的值,除了Picture 3的PSNR数值略低于CLAHE算法,然而PSNR的数值并不能完全代表图像的质量,所以要结合图像的主观比较结果,从图中可以清晰看出Picture 3的颜色校正过度,红色分量过多出现颜色偏差,因此结合主观视觉来看,PSNR指标还是最好的。从表 2中可以看出,本文算法的SSIM数值和其他算法相比是最优的,说明本文算法保留了更多的图像的原始信息。从表 3中,本文算法的UIQE数值远远大于其他算法数值,其中,UIQE的权重系数c1=0.0351,c2=0.3128,c3=3.5792,UIQE数值越大表明图像的颜色保真度、清晰度、对比度越佳。
主观上,从图 8中不同图像增强后的结果可以看出,本文算法可以有效地解决不同环境下的水下图像共同存在的问题,呈现出优良的视觉效果;客观上,从表 1~3中可以看出本文算法的不同指标数值几乎是最优的。因此,本文算法可以从不同方向有效的增强水下图像。
4. 结束语
本文研究了水下图像增强几种具有代表性的算法。针对水下图像存在图像颜色衰退严重,对比度低,细节特征模糊等问题,提出了一种具有导向滤波的MSRCR算法,该算法解决了水下图像颜色衰退的问题同时又保留了图像边缘细节;提出了一种具有Gamma校正的CLAHE算法,该算法有效地增强了水下图像整体对比度的同时也增强了图像局部对比度;最后结合多尺度图像融合,将两种算法增强后的图像逐层提取融合,保留了大量的图像特征信息,最终增强后的水下图像有效地解决了图像颜色衰退严重,对比度低,细节特征模糊的问题。对比实验结果显示,本文算法在主客观方面优于其他几种经典的水下图像增强算法。
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图 2 硅基BIB红外探测器的结构和工作原理:(a) 非本征硅光电导探测器的工作原理示意图[10];(b) 硅基BIB红外探测器的工作原理图[11];(c) Si: As BIB红外探测器结构示意图[13];(d) Si: Sb BIB红外探测器的器件结构图[14];(e) 背照射式Si: Sb BIB探测器的结构示意图,其中Nd为中性施主的密度,Nd+为电离施主的浓度,Na-为电离受主的浓度[15];(f) Si: Sb BIB探测器的红外吸收层在正的反偏电压下的平衡电荷分布图[15]
Figure 2. Structures and working mechanisms of silicon-based BIB infrared detectors: (a) Schematic diagram of the working principle of the ESPC detector[10]; (b) Schematic diagram of the working principle of the silicon-based BIB infrared detector[11]; (c) Structure diagram of the Si: As BIB infrared detector[13]; (d) Structure diagram of the Si: Sb BIB infrared detector[14]; (e) Schematic diagram of the back-illuminated Si: Sb BIB, where Nd is the density of neutral donors, Nd+ is the ionized donor density, and Na- is the density of ionized acceptors[15]; (f) Equilibrium charge distributions for the positive reverse-biased operation for the Si: Sb BIB infrared detector[15]
图 3 硅基BIB红外探测器的性能:(a) 用于Si: As IBC探测器辐射测试的低温杜瓦装置[38];(b) 测试及计算得到的Si: As IBC探测器的响应量子效率曲线[38];(c) Si: As IBC探测器的I-V测试曲线[38];(d) 金属管壳封装的Si: Sb BIB探测器[14];(e) Si: Sb BIB探测器的光谱量子效率曲线[14];(f) Si: Sb BIB探测器的暗电流与温度的关系[14];(g) Si: P BIB器件的PC光谱与远红外背景光谱,以及响应峰的指定[39];(h) Si: Ga BIB探测器的光谱量子效率[40];(i) Si: Ga BIB探测器与长波碲镉汞探测器的暗电流对比[40]
Figure 3. Performances of the silicon-based BIB infrared detectors: (a) Dewar configuration for Si: As IBC detector radiation testing[38]; (b) Responsive quantum efficiency curves of Si: As IBC detector[38]; (c) I-V testing curves of Si: As IBC detector[38]; (d) Metal shell packed Si: Sb BIB detector[14]; (e) Spectral quantum efficiency curve of Si: Sb BIB detector[14]; (f) Dark current as a function of temperature of Si: Sb BIB detector, measured at 1.5 V bias voltage[14]; (g) PC spectrum of the Si: P BIB device versus far-infrared background spectrum, and the designations of the response peak[39]; (h) Spectral QE of Si: Ga BIB detector[40]; (i) Dark current performance comparison of Si: Ga BIB detector with LWMCT detector[40]
图 5 国外硅基BIB红外探测器的研究进展:(a) 空间红外望远镜设备(SIRTF)上的128×128长波长红外焦平面组件[29];(b) DRS公司的HF1024焦平面阵列,封装在84针无铅芯片载体上[40];(c) 百万像素中红外阵列裸多路复用器[54];(d) 无掺杂单晶衬底晶圆[54];(e) Si: As BIB焦平面阵列的封装[55];(f) 256×256 Si: As IBC阵列及其航天封装[57];(g) 1024×1024 Si: As IBC阵列的红外传感器芯片[53];(h) 1024×1024 Si: As IBC阵列的读出电路[58];(i) 由双侧可粘扣的HF1024 Si: As和Si: Sb焦平面阵列组成的2048×2048焦平面阵列,像元间距为18 μm[40]
Figure 5. Research progresses of overseas silicon-based BIB infrared detectors: (a) SIRTF 128×128 long wavelength infrared focal plane array assembly[29]; (b) DRS HF1024 FPA packaged in 84-pin leadless chip carrier[40]; (c) A Mega pixel MIR bare multiplexer[54]; (d) Undoped single-crystal substrate wafer[54]; (e) Packaging of the BIB focal plane arrays[55]; (f) 256×256 Si: As IBC array in flight mount[57]; (g) Photo of a 1024×1024 Si: As IBC SCA[53]; (h) SB-291 ROIC for 1024×1024 Si: As IBC array[58]; (i) 2048×2048 FPA with 18-micron pixel pitch composed of 2-side buttable HF1024 Si: As and Si: Sb FPAs[40]
图 6 国内硅基BIB红外探测器的研究进展:(a) 平面型Si: P BIB探测器结构示意图[65];(b) 垂直型Si: P BIB探测器模型[58];(c) Si: P BIB探测器在2 V偏压和不同温度下的响应光谱[58];(d) 等离子体调谐太赫兹探测器横截面示意图[59];(e) 不同周期性孔结构(PHSs)的Si: P BIB探测器的归一化光电流谱[59];(f) Si: Ga BIB探测器在不同功能区上的层状材料结构示意图[60];(g) Si: Ga BIB探测器不同温度下的响应谱[60];(h) 金属光栅/硅基BIB太赫兹探测器的工作原理图[61];(i) 有金属光栅的器件(参数:p=7 μm,d=5 μm,DR=2/7)与无金属光栅的器件的实验光谱响应对比[61]
Figure 6. Research progresses of domestic silicon-based BIB infrared detectors: (a) Schematic diagram of the planar type Si: P BIB detector structure[65]; (b) Vertical type Si: P BIB detector model[58]; (c) Response spectrum of the Si: P BIB detector at 2 V bias voltage with different temperatures[58]; (d) Schematic representation of the cross section of the plasma-tuning THz detector[59]; (e) The normalized photocurrent spectrum of the Si: P BIB detectors for different periodic pore structures (PHSs)[59]; (f) Schematic diagram of the layered material structure of the Si: Ga BIB detector in different functional areas[60]; (g) Response spectrum of the Si: Ga BIB detector at different temperatures[60]; (h) Mechanism of the metal-grating/silicon-based BIB THz detector[61]; (i) Comparison of the experimental spectral response of devices with metal gratings (parameters: p=7 μm, d=5 μm, DR=2/7) with devices with metal-free gratings[61]
图 7 硅基BIB红外探测器的天文应用[72]:(a) 斯皮策太空望远镜;(b) 斯皮策太空望远镜观测到的“红蝴蝶”星系;(c) WISE捕捉的最古老的超新星RCW 86的图像;(d) 水瓶座/SAC-D航天探测器;(e) 平流层天文台;(f)平流层天文台捕捉的恒星合并的快照;(g) 詹姆斯·韦伯空间望远镜(JWST);(h) JWST的近红外照相机捕捉的第一张全彩图像;(i) COBE在太空中运行的示意图
Figure 7. Astronomical applications of the silicon-based BIB infrared detectors[72]: The spitzer space telescope; (b) The "red butterfly" galaxy was observed by the spitzer space telescope; (c) An image of the oldest supernova RCW 86 captured by WISE; (d) The aquarius/SAC-D space probe; (e) Stratospheric observatory for infrared astronomy; (f) Snapshot of stellar mergers captured by SOFIA; (g) The James Webb Space Telescope; (h)The first full color image captured by the near-infrared camera of the JWST; (i) Schematic representation of the cosmic background explorer operating in space
表 1 硅基BIB红外探测器的部分工艺参数
Table 1 Partial process parameters of the silicon-based BIB infrared detector
Year Material Thickness of IRAL/μm Thickness of blocking layer/μm Doping concentration of IRAL/cm-3 Fabrication method of epitaxial layer Institution Ref. 1979 Si: As 6−10 1−4 7×1017 CVD Rockwell [11] 1992 Si: Sb 17 3.5 1−8×1017 CVD Rockwell [15] 1999 Si: B 4.5 3 1×1018 - - [16] 2007 Si: As 10 - 4×1018 - DRS [17] 2007 Si: P - - 4×1018 - DRS [17] 2018 Si: As 15 - 1×1018 - NIST [18] 表 2 国外公司生产的硅基BIB红外探测器的性能参数
Table 2 Performance parameters of silicon-based BIB infrared detectors produced by foreign companies
Year Material Technology FPA format Pixel size/μm2 Pixel pitch/μm Operating temperature range/K Wavelength
range/μmDark current Quantum efficiency/% Institution Applications Ref. 2012 Si: Sb BIB 1024×1024 18 - 5-12 14-38 0.1 e/s 60 DRS Wide-field infrared survey explorer [14] 1992 Si: Sb BIB 128×128 - - 7 2-40 - - Rockwell Space infrared telescope facility [15] 2018 Si: As BIB - - - 7-10 2-30 10-12 A/mm2 60 NIST Missile defense transfer radiometer [18] 1986 Si: As BIB 10×50 - - 12 - 12.3 pA - Rockwell - [19] 1991 Si: As BIB 128×128 75 - 11 - < 0.1 nA - Rockwell Space infrared telescope facility [20] 1993 Si: As BIB 256×256 30 - 12 - 18 e-/s 57 HTC Space infrared telescope facility [21] 1995 Si: Ga ESPC 128×192 75 - ≤10 5-17 0 30 LETI/LIR European transonic windtunnel [22] 1998 Si: As BIB 256×256 30 - 6-7 - < 100 e-/s 40 RVS Infrared imaging surveyor [23] 1998 Si: As BIB 320×240 - 50 6 2-28 100 e-/s 40-55 SBRC SUBARU [24] 2000 Si: As BIB 256×256 - 25 6 5-28 3.8 e-/s 84 RVS Space infrared telescope facility [25] 2001 Si: As BIB 320×240 - 50 6-12 2-28 ≤100 e-/s > 40 RVS Mid-infrared spectrometer and imager [26] 2001 Si: As BIB 1024×1024 - 27 6-8 5-30 0.3 e-/s 45 RVS Next generation space telescope [27] 2001 Si: As BIB 1024×1024 - 27 6 5-30 < 1 e-/s 50 RVS Stratospheric observatory for infrared astronomy [28] 2003 Si: As BIB 128×128 - 75 - - 0.49-2.9 e-/s 84 DRS Wide-field infrared explorer [29] 2003 Si: Sb BIB 128×128 - 75 - - 5.3-12.9 e-/s 51 DRS Wide-field infrared explorer [29] 2003 Si: As BIB 256×256 50 - 4.7 5-25 - 56 DRS Stratospheric observatory for infrared astronomy [30] 2004 Si: As BIB 256×256 25 - 6.7-7.1 5-28 0.1 e-/s > 50 RVS James Webb space telescope [31] 2005 Si: As BIB 1024×1024 - 18 6 5-28 < 10 e-/s > 57 DRS Wide-field infrared survey explorer/James Webb space telescope [32] 2006 Si: As BIB 1024×1024 - 18 7.8 7.5-28 < 100 e-/s > 60 DRS Wide-field infrared survey explorer [33] 2008 Si: As BIB 1024×1024 30 - 7-9 3-28 1 e-/s > 40 RVS AQUARIUS [34] -
[1] McCreight C R, McKelvey M E, Goebel J H, et al. Detector arrays for low-background space infrared astronomy[C]//Infrared detectors, Sensors, and Focal Plane Arrays of SPIE, 1986, 686: 66-75.
[2] Szmulowicz F, Madarasz F L. Blocked impurity band detectors—an analytical model: figures of merit[J]. Journal of Applied Physics, 1987, 62(6): 2533-2540. DOI: 10.1063/1.339466
[3] Battersby C, Armus L, Bergin E, et al. The origins space telescope[J]. Nature Astronomy, 2018, 2(8): 596-599. DOI: 10.1038/s41550-018-0540-y
[4] 刘恩科, 朱秉升, 罗晋生. 半导体物理学: 7版[M]. 北京: 电子工业出版社, 2017. LIU Enke, ZHU Bingsheng, LUO Jingsheng. The Physics of Semiconductors: 7th Edition[M]. Beijing: Publishing House of Electronics Industry, 2017.
[5] CHEN H C, LIN C C, HAN H W, et al. Enhanced efficiency for c-Si solar cell with nanopillar array via quantum dots layers[J]. Optics Express, 2011, 19(105): A1141-A1147.
[6] JUANG J Y, ZHOU K, BANG J H, et al. Improved photovoltaic performance of Si nanowire solar cells integrated with ZnSe quantum dots[J]. The Journal of Physical Chemistry C, 2012, 116(23): 12409-12414. DOI: 10.1021/jp301683q
[7] WU C, Crouch C H, ZHAO L, et al. Near-unity below-band-gap absorption by microstructured silicon[J]. Applied Physics Letters, 2001, 78(13): 1850-1852. DOI: 10.1063/1.1358846
[8] Crouch C, Carey J, Shen M, et al. Infrared absorption by sulfur-doped silicon formed by femtosecond laser irradiation[J]. Appl Phys A, 2004, 79: 1635-1641. DOI: 10.1007/s00339-004-2676-0
[9] ZHANG T, Ahmad W, LIU B, et al. Broadband infrared response of sulfur hyperdoped silicon under femtosecond laser irradiation[J]. Materials Letters, 2017, 196: 16-19. DOI: 10.1016/j.matlet.2017.03.011
[10] 王占国, 郑有炓. 半导体材料研究进展[M]. 北京: 高等教育出版社, 2012. WANG Zhanguo, ZHENG Youliao. Research Progress in Semiconductor Materials[M]. Beijing: Publishing House of Higher Education, 2018.
[11] Petroff M D, Stapelbroek M G. Blocked Impurity Band Detectors: 4568 960[P]. U.S. Patent, 1986-02-04.
[12] Petroff M D, Stapelbroek M G. Responsivity and noise models of blocked impurity band detectors[C]//Proc. IRIS Specialty Group on Infrared Detectors, 1984, 2.
[13] Rieke G H. Infrared detector arrays for astronomy[J]. Annu. Rev. Astron. Astrophys. , 2007, 45: 77-115. DOI: 10.1146/annurev.astro.44.051905.092436
[14] Khalap V, Hogue H. Antimony-doped silicon blocked impurity band (BIB) arrays for low flux applications[C]//Infrared Sensors, Devices, and Applications Ⅱ. International Society for Optics and Photonics, 2012, 8512: 85120O.
[15] Huffman J E, Crouse A G, Halleck B L, et al. Si: Sb blocked impurity band detectors for infrared astronomy[J]. Journal of Applied Physics, 1992, 72(1): 273-275. DOI: 10.1063/1.352127
[16] Asadauskas L, Brazis R, Leotin J. Optical phonon line in boron-doped silicon BIB structures[C]//Materials Science Forum. Trans Tech Publications Ltd., 1999, 297: 361-364.
[17] Hogue H H, Guptill M T, Monson J C, et al. Far-infrared blocked impurity band detector development[C]//Infrared Spaceborne Remote Sensing and Instrumentation XV of SPIE, 2007, 6678: 63-73.
[18] Woods S I, Proctor J E, Jung T M, et al. Wideband infrared trap detector based upon doped silicon photocurrent devices[J]. Applied Optics, 2018, 57(18): D82-D89. DOI: 10.1364/AO.57.000D82
[19] Stetson S B, Reynolds D B, Stapelbroek M G, et al. Design and performance of blocked-impurity-band detector focal plane arrays[C]//Infrared Detectors, Sensors, and Focal Plane Arrays of SPIE, 1986, 686: 48-65.
[20] Noel R A. Large-area blocked-impurity-band focal plane array development[C]//Infrared Detectors and Focal Plane Arrays Ⅱ of SPIE, 1992, 1685: 250-259.
[21] Lum N A, Asbrock J F, White R, et al. Low-noise, low-temperature 256 ×256 Si: As IBC staring FPA[C]//Infrared Detectors and Instrumentation of SPIE, 1993, 1946: 100-109.
[22] Suffis S, Caes M, Deliot P, et al. Characterization of 128×192 Si: Ga focal plane arrays: study of nonuniformity, stability of its correction, and application for the CRYSTAL camera[C]//Infrared Detectors and Focal Plane Arrays Ⅴ of SPIE, 1998, 3379: 235-248.
[23] Matsuhara H. IRC: an infrared camera on board the IRIS[C]//Infrared Astronomical Instrumentation of SPIE, 1998, 3354: 915-921.
[24] Sohn E, Schneider E R, Cruz-Gonzales I, et al. Mid-infrared camera/spectrograph for OAN/SPM[C]//Infrared Astronomical Instrumentation, 1998, 3354: 822-824.
[25] McMurray Jr R E, Johnson R R, McCreight C R, et al. Si: As IBC array performance for SIRTF/IRAC[C]//Infrared Spaceborne Remote Sensing Ⅷ of SPIE, 2000, 4131: 62-69.
[26] Deutsch L K, Hora J L, Adams J D, et al. MIRSI: a mid-infrared spectrometer and imager[C]//Instrument Design and Performance for Optical/Infrared Ground-based Telescopes of SPIE, 2003, 4841: 106-116.
[27] Ennico K A, McKelvey M E, McCreight C R, et al. Large format Si: As IBC array performance for NGST and future IR space telescope applications[C]//IR Space Telescopes and Instruments of SPIE, 2003, 4850: 890-901.
[28] Ennico K A, Greene T P, McCreight C R, et al. Development and testing of a 1024×1024 pixel Si: As IBC detector for SOFIA-like applications[C]//Airborne Telescope Systems Ⅱ of SPIE, 2003, 4857: 155-165.
[29] Hogue H H, Guptill M L, Reynolds D, et al. Space mid-IR detectors from DRS[C]//IR Space Telescopes and Instruments, 2003, 4850: 880-889.
[30] Adams J D, Herter T L, Keller L D, et al. Testing of mid-infrared detector arrays for FORCAST[C]//Optical and Infrared Detectors for Astronomy of SPIE, 2004, 5499: 442-451.
[31] Love P J, Hoffman A W, Lum N A, et al. 1024×1024 Si: As IBC detector arrays for JWST MIRI[C]//Focal Plane Arrays for Space Telescopes Ⅱ of SPIE, 2005, 5902: 58-66.
[32] Mainzer A K, Hong J, Stapelbroek M G, et al. A new large-well 1024×1024 Si: As detector for the mid-infrared[C]//Infrared and Photoelectronic Imagers and Detector Devices of SPIE, 2005, 5881: 253-260.
[33] Mainzer A, Larsen M, Stapelbroek M G, et al. Characterization of flight detector arrays for the wide-field infrared survey explorer[C]//High Energy, Optical, and Infrared Detectors for Astronomy Ⅲ of SPIE, 2008, 7021: 302-313.
[34] Ives D, Finger G, Jakob G, et al. AQUARIUS: the next generation mid-IR detector for ground-based astronomy[C]//High Energy, Optical, and Infrared Detectors for Astronomy Ⅴ of SPIE, 2012, 8453: 296-308.
[35] Reynolds D B, Seib D H, Stetson S B, et al. Blocked impurity band hybrid infrared focal plane arrays for astronomy[J]. IEEE Transactions on Nuclear Science, 1989, 36(1): 857-862. DOI: 10.1109/23.34565
[36] Petroff M D, Stapelbroek M G. Blocked Impurity Band Detectors, Radiation Hard, High Performance LWIR Detectors[C]//Proceedings, IRIS Specialty Group on Infrared Detectors, 1980: 48-62.
[37] Mainzer A, Larsen M, Stapelbroek M G, et al. Characterization of flight detector arrays for the wide-field infrared survey explorer[C]//High Energy, Optical, and Infrared Detectors for Astronomy Ⅲ of SPIE, 2008, 7021: 302-313.
[38] Ando K J, Hoffman A W, Love P J, et al. Development of Si: As impurity band conduction (IBC) detectors for mid-infrared applications[C]//Infrared Technology and Applications XXIX, 2003, 5074: 648-657.
[39] LIAO K, LI N, LIU X, et al. Ion-implanted Si: P blocked-impurity-band photodetectors for far-infrared and terahertz radiation detection[C]//International Symposium on Photoelectronic Detection and Imaging on Terahertz Technologies and Applications of SPIE, 2013, 8909: 257-265.
[40] Hogue H, Atkins E, Reynolds D, et al. Update on blocked impurity band detector technology from DRS[C]//Detectors and Imaging Devices: Infrared, Focal Plane, Single Photon. International Society for Optics and Photonics, 2010, 7780: 778004.
[41] Sclar N. Properties of doped silicon and germanium infrared detectors[J]. Progress in Quantum Electronics, 1984, 9(3): 149-257. DOI: 10.1016/0079-6727(84)90001-6
[42] Kleinhans W A, Petroff M D, Stapelbroek M G. Hybrid Si: As BIBIB Detector Arrays[C/OL]//Proc. of IRIS Specialty Group on Infrared Detectors, 1984: https://www.researchgate.net/profile/George-Gull/publication/234236444_Improved_SiAs_BIBIB_Back-Illuminated_Blocked-Impurity-Band_hybrid_arrays/links/0f317537a14b676810000000/Improved-SiAs-BIBIB-Back-Illuminated-Blocked-Impurity-Band-hybrid-arrays.pdf.
[43] Fowler A M, Joyce R R. Status of the NOAO evaluation of the Hughes 20x64 Si: As impurity band conduction array[C]//Instrumentation in Astronomy VⅡ, 1990, 1235: 151-159.
[44] Larsen M F, Sargent S D, Tansock Jr J J. On-orbit goniometric calibration for the SPIRIT Ⅲ radiometer[C]//Signal and Data Processing of Small Targets of SPIE, 1998, 3373: 32-43.
[45] Hoffman A W, Love P J, Ando K J, et al. Large infrared and visible arrays for low-background applications: an overview of current developments at Raytheon[C]//Optical and Infrared Detectors for Astronomy, 2004, 5499: 240-249.
[46] Mainzer A K, Hogue H, Stapelbroek M, et al. Characterization of a megapixel mid-infrared array for high background applications[C]//High Energy, Optical, and Infrared Detectors for Astronomy Ⅲ, 2008, 7021: 70210T.
[47] Hogue H H, Mattson R B, Stapelbroek M G, et al. Focal plane detectors for the WISE 12-and 23-µm bands[C]//Infrared Systems and Photoelectronic Technology Ⅱ of SPIE, 2007, 6660: 194-202.
[48] Mainzer A K, Hong J, Stapelbroek M G, et al. A new large-well 1024×1024 Si: As detector for the mid-infrared[C]//Infrared and Photoelectronic Imagers and Detector Devices, 2005, 5881: 58810Y.
[49] McMurray Jr R E, Johnson R R, McCreight C R, et al. Si: As IBC array performance for SIRTF/IRAC[C]//Infrared Spaceborne Remote Sensing Ⅷ of SPIE, 2000, 4131: 62-69.
[50] Ando K J, Hoffman A W, Love P J, et al. Development of Si: As impurity band conduction (IBC) detectors for mid-infrared applications[C]//Infrared Technology and Applications XXIX, 2003, 5074: 648-657.
[51] Starr B, Mears L, Fulk C, et al. RVS large format arrays for astronomy[C]//High Energy, Optical, and Infrared Detectors for Astronomy Ⅶ of SPIE, 2016, 9915: 929-942.
[52] Miyata T, Sako S, Nakamura T, et al. Development of a new mid-infrared instrument for the TAO 6.5-m Telescope[C]//Ground-based and Airborne Instrumentation for Astronomy Ⅲ, 2010, 7735: 77353P.
[53] Stacey G J, Hayward T L, Latvakoski H M, et al. KWIC: a widefield mid-infrared array camera/spectrometer for the KAO[C]//Infrared Detectors and Instrumentation, 1993, 1946: 238-248.
[54] Van Cleve J E, Herter T L, Butturini R, et al. Evaluation of Si: As and Si: Sb blocked-impurity-band detectors for SIRTF and WIRE[C]//Infrared Spaceborne Remote Sensing Ⅲ of SPIE, 1995, 2553: 502-513.
[55] Dotson J L, McKelvey M, McMurray Jr R, et al. Cryogenic testing of a 1024×1024 Si: As array for WISE[C]//Focal Plane Arrays for Space Telescopes Ⅲ, 2007, 6690: 66900F.
[56] WANG C, LI N, DAI N, et al. High performance infrared detectors compatible with CMOS-circuit process[J]. Chinese Physics B, 2021, 30(5): 050702. DOI: 10.1088/1674-1056/abd6fb
[57] ZHU H, ZHU J, HU W, et al. Temperature-sensitive mechanism for silicon blocked-impurity-band photodetectors[J]. Applied Physics Letters, 2021, 119(19): 191104. DOI: 10.1063/5.0065468
[58] ZHU H, ZHU J, XU H, et al. Design and fabrication of plasmonic tuned THz detectors by periodic hole structures[J]. Infrared Physics & Technology, 2019, 99: 45-48.
[59] DENG K, ZHANG K, LI Q, et al. High-operating temperature far-infrared Si: Ga blocked-impurity-band detectors[J]. Applied Physics Letters, 2022, 120(21): 211103. DOI: 10.1063/5.0092774
[60] CHEN Y, TONG W, WANG B, et al. The absorption enhancement effect of metal gratings integrated Silicon-based Blocked-Impurity-Band (BIB) terahertz detectors[C]//2021 International Conference on Numerical Simulation of Optoelectronic Devices (NUSOD) of IEEE, 2021: 43-44.
[61] XIAO Y, ZHU H, DENG K, et al. Progress and challenges in blocked impurity band infrared detectors for space-based astronomy[J]. Science China Physics, Mechanics & Astronomy, 2022, 65(8): 1-17.
[62] Herter T L, Hayward T L, Houck J R, et al. Mid-and far-infrared hybrid focal plane arrays for astronomy[C]//Infrared Astronomical Instrumentation of SPIE, 1998, 3354: 109-115.
[63] Stapelbroek M G, Hogue H H, Atkins E W, et al. Silicon for visible-to-VLWIR photon detection[C]//Infrared Technology and Applications XXIX, 2003, 5074: 166-172.
[64] Bamberg J A, Zaun N H. Design and performance of the cryogenic focal plane optics assembly for the Infrared Astronomical Satellite (IRAS)[C]//Cryogenic Optical Systems and Instruments Ⅰ of SPIE, 1985, 509: 94-102.
[65] Werner M W, Roellig T L, Low F J, et al. The Spitzer space telescope mission[J]. The Astrophysical Journal Supplement Series, 2004, 154(1): 1. DOI: 10.1086/422992
[66] Mainzer A K, Eisenhardt P, Wright E L, et al. Preliminary design of the wide-field infrared survey explorer (WISE)[C]//UV/Optical/IR Space Telescopes: Innovative Technologies and Concepts Ⅱ Of SPIE, 2005, 5899: 262-273.
[67] Gardner, J.P., Mather, J.C., Clampin, M., et al. The James Webb Space telescope[J]. Space Science Reviews, 2006, 123(4): 485-606. DOI: 10.1007/s11214-006-8315-7
[68] Huffman J E. Infrared detectors for 2-to 220-um astronomy[C]//Infrared Detectors: State of the Art Ⅱ, 1994, 2274: 157-169.
[69] Herter T, Stacey G, Gull G, et al. FORCAST: a WIDE-field infrared camera for SOFIA[C]//American Astronomical Society Meeting Abstracts, 1997, 191: 09.02.
[70] Mather J C, Cheng E S, Eplee Jr R E, et al. A preliminary measurement of the cosmic microwave background spectrum by the Cosmic Background Explorer (COBE) satellite[J]. The Astrophysical Journal, 1990, 354: L37-L40. DOI: 10.1086/185717
[71] Rauter P, Fromherz T, Winnerl S, et al. Terahertz Si: B blocked-impurity-band detectors defined by nonepitaxial methods[J]. Applied Physics Letters, 2008, 93(26): 261104. DOI: 10.1063/1.3059559
[72] Mary W. Jackson NASA Headquarters. NASA Missions[DB/OL]. https://www.nasa.gov/missions.
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