Research Progress in Ultraviolet Enhanced Image Sensors
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摘要: 近年来图像传感器在紫外成像的应用越来越广泛,尤其是以CCD(charge coupled device)和CMOS(complementary metal oxide semiconductor)为主的紫外图像传感器受到了研究人员的广泛关注。半导体技术的进步和纳米材料的发展进一步推动了紫外图像传感器的研究。本文综述了国内外紫外增强图像传感器的研究进展,介绍了几种增强器件紫外响应的材料,另外还简要概述了紫外图像传感器在生化分析、大气监测、天文探测等方面的应用,并讨论了CCD/CMOS图像传感器在紫外探测方面所面临的挑战。Abstract: In recent years, image sensors are more and more widely used in ultraviolet imaging, especially the ultraviolet image sensors based on CCD and CMOS have attracted intensive attention of researchers. The progress of semiconductor technology and the development of nanomaterials further promote the research of ultraviolet image sensor. In this review, the research progress of ultraviolet enhanced image sensor at home and abroad is reviewed, and several materials enhancing the ultraviolet response of the device are introduced. In addition, the applications of ultraviolet image sensor in biochemical analysis, atmospheric monitoring and astronomical detection are briefly summarized, and the challenges faced by CCD/CMOS image sensors in ultraviolet detection are discussed.
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Keywords:
- ultraviolet enhancement /
- CMOS image sensor /
- CCD
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0. 引言
随着国家对检测精度和检测损伤程度的要求越来越高,红外热波无损检测技术逐渐出现在人们的视野中[1],其主要通过热源对损伤部位进行主动加热,缺陷部位会因为比其他部位的导热能力差或隔热在红外热像仪中显示出不同的热波序列图。它拥有检测速度快、对试件几乎没有损害、检测结果直观明了等优点,目前已经普遍应用于交通、军工和新能源领域。然而,红外热像仪的读取、传输和储存容易受外界条件和试件本身不均匀性的干扰,这使得原始热波图像信噪比低、噪声构成复杂,对后续的主观分析评价造成了巨大的影响,使得检测效果和效率都大幅度降低。因此,如何对原始热波图像进行去噪,提高其信噪比,突出图像细节,提高检测效率是红外热波无损检测技术的关键研究内容[2]。
红外图像中的混合噪声(随机和条纹噪声)构成复杂,很难被传统去噪方法去除[3]。与传统去噪方法相比较,利用小波分析去噪方法则显得相对更能抓住噪声的本质特性。在利用小波分析的许多去噪方法当中,比较常用的是小波阈值去噪法,这是由于它的多分辨率分析的特性,能够较好地保留图像边缘信息,算法运算量相对较小,运行的速度较快,并且所具有的多尺度、多方向、时频局部化的特点,可以精确对信号定位,对噪声进行抑制,从而使图像的质量提高。
近些年来,学者们逐渐将小波阈值去噪应用至红外图像去噪[3],主要工作有:对阈值进行改进,使其能够自适应分解尺度,对阈值函数进行改进,使其获取的小波系数连续且保真。这些改进均在处理已知噪声方差的白噪声上取得了不错的效果,却缺乏对复杂噪声的适应性,本论文通过建立红外图像噪声模型,分析其噪声特性,建立随机噪声与固定噪声协方差矩阵获取噪声方差,改进阈值与阈值函数,通过软件实现对阈值函数控制因子最佳值的获取,最后通过仿真模拟与传统方法进行去噪效果比较,综合对处理后图像的主观分析和客观评价指标,得到改进后的阈值与阈值函数对复杂噪声具有更好去噪效果的结论。
1. 噪声建模
建立红外图像噪声模型是去除红外图像噪声的关键。红外图像的信息与噪声的比例比传统图像低,并且噪声构成比较复杂,外界环境的干扰和内部元件的成像特性都能产生多种噪声,由此使噪声建模变得更加困难。本文将红外图像噪声分为两类:随机噪声和固定噪声。
通过随机噪声和固定噪声的特性,将噪声模型建立为3D模式,将噪声的信息通过三维形式表现出来,三维信息分别代表空间坐标和时间坐标。这种处理方式将复杂的三维噪声分解成了若干个简单的一维或者二维噪声的集合,简化了噪声模型[4]。
3D噪声模型表达式为:
$$ {U_{{\rm{TVH}}}}=S+{N_{{\rm{TVH}}}}+{N_{{\rm{VH}}}}+{N_{{\rm{TH}}}}+{N_{{\rm{TV}}}}+{N_{\rm{H}}}+{N_{\rm{V}}}+{N_{\rm{T}}} $$ (1) 式中:T代表时间;V代表垂直方向的信息;H代表水平方向的信息;S是所有像素点的平均值。
1.1 红外图像随机噪声
随机噪声,顾名思义其空域和时域坐标均为随机的噪声。表现为位置不固定的噪点,它主要由红外图像本身背景辐射的光子起伏,读取和转换电路以及输出电路的附加噪声构成。我们认为高斯噪声和泊松噪声组成了随机噪声。这两种信号的强度与信号本身的平均强度相关。
由3D噪声模型可知,如果客观条件不变,即红外热像仪和拍摄背景不变,空间分量就不会产生变化,那么通过对1s内视频中的25帧进行帧相减、平均,余下的部分就是这1s内的随机噪声。
通过软件编程,对某红外视频进行差分,进行帧相减获取随机噪声的结果见图 1。
1.2 红外图像固定噪声
固定噪声,表示在同一个位置和多个位置固定出现的噪声。它是由红外热像仪检测元件本身的响应程度不统一、成像缺陷和其他波段影响造成的。红外图像固定噪声由图像的非均匀性和图像盲元组成,非均匀性就是使图像模糊不清的条纹噪声,盲元即是椒盐噪声。
1.2.1 非均匀性模型
物体通过红外热像仪探测元件映射至内部的响应模式可以分为线性和非线性,通常情况下我们认为其呈线性[5]。因此,本论文的响应模式表示如下:
$$ {R_j}_{, k}={a_j} \times {E_j}_{, k}+v $$ (2) 式中:j, k表示像素的空域坐标,j=1~W,k=1~H,W和H分别表示图像的行宽和列宽;Ej,k和Rj,k分别表示实际辐射值和经探测元映射后的辐射值。aj和bj分别为此模型的线性指数和常数偏移,aj服从均值为1,方差为σa的高斯分布。偏移bj服从均值为0,方差为σb的高斯分布。
通过软件生成满足上述条件的线性系数和偏移值,然后根据线性响应的非均匀性模型使清晰图像产生非均匀性。添加非均匀性后的图像如图 2所示:其中图(a)为红外图像,图(b)为产生非均匀性后的红外图像,σa=0.01, σb=5。
1.2.2 椒盐噪声
目前普遍采用的基础理论认为盲元在红外图像中的表现形式与普通图像中的椒盐噪声类似。因此将红外图像中的盲元当作椒盐噪声处理生成,其概率分布密度为:
$$P\left( z \right) = \left\{ {\begin{array}{*{20}{l}} {{P_a}\quad z = a} \\ {{P_b}\quad z = b} \\ {0\quad {\rm{other}}} \end{array}} \right.$$ (3) 若a>b,灰度值为a的点在图像中将显示为一个亮点,灰度值为b的点在图像中将显示为一个暗点。通常将a,b设置为图像的极大极小值。Pa和Pb为椒盐噪点的概率密度分布。
由于红外热像仪中自带了盲元算法,其采集到的红外图像中几乎不存在盲元,因此本论文忽略了盲元对图像的影响,最终生成的红外图像噪声模型如图 3所示。
2. 小波阈值
小波阈值去噪方法原理简单、容易实现、去噪效果良好,已经在各去噪处理中得到了广泛应用。但是传统的小波阈值去噪方法具有一定的缺陷,传统阈值不能够自适应小波分解的尺度,使得每层的阈值单一,去噪效果受到限制。传统阈值函数获得的小波系数不够保真或者不连续,这增加了小波去噪和重构的难度,降低了去噪效果[6]。近些年来,学者们对如何改进阈值,使其能够自适应分解尺度,如何改进阈值函数,使其平滑连续且保真,进行了大量的研究,也获得了去噪效果良好的阈值及阈值函数,却忽略了影响小波阈值去噪效果的关键因素:噪声方差。本论文在改进阈值及阈值函数的基础上,进一步针对前文构造的红外图像噪声模型进行噪声方差估计。
2.1 小波阈值去噪原理
对含噪声的信号使用选用小波进行分解,噪声信号的小波系数偏小,信号的小波系数偏大,因此设定一个阈值来筛选信号,滤除噪声,而后再通过小波逆变换重构信号图像[7]。
设信号模型为f(t)=s(t)+n(t),其中,s(t)是原信号,n(t)是方差为σ2的高斯白噪声。
以上述信号为例。平均采取信号的若干点,f(n),n=0, 1, …, N-1,则f(t)小波变换系数为:
$${W_f}\left( {j, k} \right) = {2^{\frac{j}{2}}}\sum\limits_{n = 0}^{N - 1} {f\left( n \right)} \psi \left( {{2^j}n - k} \right)$$ (4) 在实际应用中,难以直接对式(4)进行实现,并且在大多数情况下Ψ(t)没有表达式。但是它可以通过下面两个公式来递归实现:
$$ {S_j}(j+1, k)={S_f}\left( {j, k} \right) \times h\left( {j, k} \right) $$ (5) $$ {W_f}(j+1, k)={S_f}\left( {j, k} \right) \times g\left( {j, k} \right) $$ (6) 式中:h和g为滤波器,用于滤除低频和高频信号;Sf(0, k)为原始信号f(k),Sf(j, k)为近似j的函数;Wf(j, k)则为小波系数。那么,经变换重构后的小波可以表示为如下形式:
$${S_j}\left( {j - 1, k} \right) = {S_f}\left( {j, k} \right) \times \tilde h\left( {j, k} \right) + {W_f}\left( {j, k} \right) \times \tilde g\left( {j, k} \right)$$ (7) 将小波系数Wf(j, k)简记为ωj,k,对f(k)=s(k)+n(k)作小波分解后,小波系数ωj,k仍然由两部分组成,分别是真实信号s(k)对应的小波系数Ws(j, k),记为uj,k,白噪声n(k)对应的小波系数Wn(j, k),记为vj,k,那么有:
$$ {\omega _j}_{, k}={u_j}_{, k}+{v_j}_{, k} $$ (8) 小波阈值去噪方法过程如图 4。
2.2 噪声的方差估计
噪声的方差估计决定了阈值的准确性,它决定了去噪效果的好坏,传统的噪声方差估计适用于单一的加性白噪声,对于噪声复杂的红外图像噪声难以适应,本论文建立非均匀性与随机噪声的混合模型。对于W×H的图像块,将其整理为(W×H)×1的列向量yj,k:
$$ {y_j}_{, k}={x_j}_{, k}+{n_j}_{, k} $$ (9) 式中:xj,k为干净的图像块;nj,k为混合噪声,其均值为零向量。协方差矩阵为:
$$ {\mathit{\boldsymbol{C}}_n}={\sigma _{\rm{r}}}^2\mathit{\boldsymbol{I}}+{\sigma _{\rm{s}}}^2\mathit{\boldsymbol{O}} $$ (10) 式中:I为W2×H2单位矩阵;O为W2×H2的特殊对角矩阵:
$$\boldsymbol O = \left( {\begin{array}{*{20}{c}} \mathbf{1}& \cdots &\mathbf{0} \\ \vdots & \ddots & \vdots \\ \mathbf{0}& \cdots &\mathbf{1} \end{array}} \right)$$ 式中:1为W×H元素均为1的方阵;0为W×H元素均为0的方阵。
此方法构造了混合噪声的协方差矩阵,分别求得随机噪声强度σr和非均匀性导致的条纹噪声强度σs即可获取混合噪声强度:
$$ {\sigma _{\rm{r}}}={\rm{MAD}}\left( {{\rm{HH}}} \right) $$ (11) 式中:HH为图像一阶小波分解中的对角高频分量。MAD为绝对中位差,其表达式为:
$$ {\rm{MAD}}\left( a \right)=1.4726 \times {\rm{median}}(|a-{\rm{median}}\left( a \right)|) $$ (12) 经过小波分解后的对角高频分量中不包含有着垂直或者水平特性的条纹噪声,因此使用此方法很好地消除了固定噪声对随机噪声方差估计的影响。
本论文采用文献[8]的方法,利用各方向的差分图来消除固定噪声的方向特性。对含噪声图像y分别做不同方向的一阶差分,得到两幅梯度图▽hy和▽vy。固定噪声方差的估计为:
$${\sigma _{\rm{s}}} = \sqrt {\left( {{\rm{MAD}}{{\left( {{\nabla _h}y} \right)}^2} - {\rm{MAD}}{{\left( {{\nabla _v}y} \right)}^2}} \right)/2} $$ (13) 2.3 小波阈值改进
2.3.1 阈值改进
针对通用阈值不能自适应分解尺度的不足,本论文在通用阈值的基础上添加尺度控制因子j,改进后的阈值能够自适应分解尺度,随着分解尺度增大阈值逐渐减小,更符合小波系数在各层分解的情况:
$$\lambda {\rm{ = }}\sigma \sqrt {2\ln N} /\ln \left( {j + e - 1} \right)$$ (14) 式中:σ是噪声的总方差;N是信号的尺寸。
经改进后的阈值克服了传统固定阈值不能随分解尺度改变的缺点,更贴合了每层噪声信号经小波分解后的临界值,减轻了对信号小波系数的过度扼杀,增强了小波阈值去噪方法的效果。
2.3.2 阈值函数改进
传统的硬阈值和软阈值存在着获取的小波系数失真、不连续等缺点[9],为了降低这些缺点对去噪效果造成的影响本论文设计了如下阈值函数,表达式:
$${\hat \omega _{j, k}} = \left\{ {\begin{array}{*{20}{l}} {{\omega _{j, k}} - \operatorname{sgn} \left( {{\omega _{j, k}}} \right) \cdot m \cdot \frac{\lambda }{{{{\rm{e}}^{{{\left| {\frac{{{\omega _{j, k}}}}{\lambda }} \right|}^n}}} + 1 - {\rm{e}}}}, \quad \left| {{\omega _{j, k}}} \right| \geqslant \lambda } \\ {\frac{{{{\rm{e}}^{{{\left| {\frac{{{\omega _{j, k}}}}{\lambda }} \right|}^n}}} - {\rm{e}}}}{\lambda }\quad \quad \quad \quad \quad \quad \quad \quad \quad \left| {{\omega _{j, k}}} \right| \leqslant \lambda } \end{array}} \right.$$ (15) 式中:ωj,k为添加噪声后的图像经小波分解后的系数;sgn(ωj,k)取ωj,k的符号;λ为阈值;${\hat \omega _{j, k}}$为经过改进阈值函数获取的小波系数;m, n为调节因子;由公式可以看出当∣ωj,k∣≥λ时,随着绝对值的增大,缩减后的小波系数绝对值的减小幅度逐渐变小,这样也就避免了在以上阈值处理过程中因传统阈值函数处理大于阈值的小波系数存在固定偏差的现象,形成了动态地对偏差进行填补,所以可以看出$\frac{1}{{{{\rm{e}}^{{{\left| {\frac{{{\omega _{j, k}}}}{\lambda }} \right|}^n}}} + 1 - {\rm{e}}}}$是一个具有很好调节偏差动态的因子。对于绝对值小于阈值的部分,本论文的函数不是一味地置零,而是给出了一个特殊地阈值函数$\frac{{{{\rm{e}}^{{{\left| {\frac{{{\omega _{j, k}}}}{\lambda }} \right|}^n}}} - {\rm{e}}}}{\lambda }$,从而能够有效地缓解软、硬阈值函数对那些系数都置为零所产生的截断效应,更好地逼近原图像信号中的真实信息。并且,改进的阈值函数中引入了m, n,从而能够更加灵活和有效地对图像信号的小波系数进行阈值处理。
3. 仿真模拟
3.1 参数设置
本论文基于软件平台,对图像添加随机噪声和固定噪声,对加噪后的图像进行多尺度的分解,提取各尺度上的高频系数,对其进行相应的阈值处理,最后将处理后的新小波系数进行重构后得到去噪后的图像。
以0.1为间隔,在软件平台上从0到10对m、n进行赋值,以峰值信噪比为评价标准,选取了m=0.3和n=2.5作为这两个参数的值。
由于小波函数众多,分解层数也是自由选取的,所以本论文所期望的实验效果也会不尽相同。理论上讲,小波分解层数越高,重构得到的图像就会越清晰,但是相应的重构图像的难度就会越大,本论文选取sym4小波,3层分解尺度为实验基础。
3.2 结果分析
对添加噪声后的图像使用不同方法进行处理后的结果如图 5所示。
从效果比较图中可以看出,硬阈值函数去噪方法处理后图像的视觉效果是其中最差的,不管是其细节部分还是其相对平滑部分都显得有些模糊,软阈值函数去噪方法处理后的图像虽然整体显得没那么多噪声存在,但是图像的细节信息也没能保留,传统中值滤波去噪方法并不能很好地去除噪声,虽然较好地保留了图像的细节信息,但是对于图像的非均匀性和图像的随机噪声没有得到有效的处理。改造后阈值函数去噪方法较好地解决了图像的非均匀性,图像的随机噪声和椒盐噪声也得到了较好的滤除。
3.3 客观指标评价
均方误差(mean square error,MSE)、峰值信噪比(peak signal to noise ratio,PSNR)和结构相似性(structural similarity,SSIM)是传统的客观评价标准。去噪后求得的均方误差越小、峰值信噪比越大,说明图像的清晰度越高。SSIM取值范围是[0, 1],SSIM的值越大,表示去噪图像与原图像越相似。其表达式分别为[10]:
$${\rm{MSE}} = \frac{1}{{W \times H}}\sum\limits_{i = 1}^W {\sum\limits_{j = 1}^H {{{\left( {I\left( {i, j} \right) - K\left( {i, j} \right)} \right)}^2}} } $$ (16) $${\rm{PSNR}} = 10 \times \log \frac{{{{255}^2}}}{{{\rm{MSE}}}}$$ (17) 式中:I(i, j)为原始图像在(i, j)坐标上的像素大小;K(i, j)为去噪后的图像在(i, j)位置上的像素值;W代表图像的行数;H代表图像的列数。
结构相似性的值度量了处理后的图像与原图像的相似程度,它分别从亮度l、对比度c和结构三个方面来对图像进行考量。
$$l\left( {I, K} \right) = \frac{{2{\mu _I}{\mu _K} + {C_1}}}{{\mu _I^2 + \mu _K^2 + {C_1}}}$$ (18) $$c\left( {I, K} \right) = \frac{{2{\sigma _I}{\sigma _K} + {C_2}}}{{\sigma _I^2 + \sigma _K^2 + {C_2}}}$$ (19) $$s\left( {I, K} \right) = \frac{{{\sigma _{IK}} + {C_3}}}{{\sigma _I^{}\sigma _K^{} + {C_3}}}$$ (20) 式中:μI和μK分别表示图像I和图像K的均值;σI和σK分别表示图像I和图像K的方差;σIK表示图像I和图像K的协方差;C1,C2和C3是常数。
为了更好地对比改进阈值函数去噪方法与传统方法的去噪效果,本论文采用了上述指标作为客观评价标准,结果如表 1所示。
表 1 仿真模拟图像几种去噪方法评价指标对比Table 1. Comparison of evaluation indexes of several image denoising merhodsDenoising method MSE PSNR SSIM Noisy image 0.2866 2.4322 0.5512 Hard threshold function 0.2712 2.5127 0.2896 Soft threshold function 0.2755 2.6893 0.5924 Median filter 0.2781 2.7667 0.4612 Improved threshold function 0.2877 3.5389 0.6415 从表 1中的数据可以看出,上文提到的几种阈值函数和传统中值滤波和本论文改进的阈值函数在对图像进行去噪后,均方误差大致相同,用硬阈值函数进行去噪时,其PSNR最小,所以可得出硬阈值的去噪效果最差;改进阈值函数的PSNR最大,去噪效果最好,结构相似性方面,改进后的阈值函数也脱颖而出,经改进阈值函数处理后的图像更接近原始图像,因此具有更好的去噪效果。
3.4 真实图像处理
为了验证仿真模拟的结果,本论文采用几种成熟的滤波去噪方法和改进小波阈值去噪方法对真实非制冷红外热像仪进行了去噪处理,处理结果如图 6所示。
与现在较为成熟的几种滤波去噪[11]方法相比。双边滤波和保边滤波没有能够很好地滤除条纹噪声,超级滤波虽然很好地滤除了条纹噪声,但细节模糊严重。复合引导滤波效果最好,在滤除噪声的同时较好地保留了图像细节,本论文的改进小波阈值去噪方法相对于处理仿真图像,对真实非制冷红外热像仪图像的处理有更好的效果,真实图像由非均匀性产生的固定条纹噪声相对于仿真模拟的条纹噪声更好地被去除,图像细节部分保留较为完整。
综合对去噪后仿真模拟图像与真实图像的直观评价和去噪后仿真模拟图像评价指标数据对比,能够得出结论,本论文改进后的阈值函数对于非制冷红外热像仪采集到的红外图像噪声相对于传统阈值方法及部分成熟应用于去噪的滤波方法具有更好的去噪效果。
4. 结语
在红外热波无损检测中,非制冷红外热像仪采集的原始热波图像的信噪比低、噪声构成复杂是影响人们对结果进行分析判断的主要原因,对红外图像进行去噪是红外热波无损检测的重要工作。如何将小波阈值去噪方法合理地应用至红外图像去噪是近年来学者们研究的热点,本论文不同于前人单一的处理高斯白噪声,建立了更完整的红外图像噪声模型,增添了非均匀性引起的条纹噪声,基于噪声模型对噪声方差进行了估计,改进了阈值和阈值函数,通过软件编程,获取了阈值函数的最佳参数,最后通过仿真模拟和真实数据实验对不同方法的去噪效果进行了评价,结果表明,改进后的阈值和阈值函数对加噪图像有更好的去噪效果,图像细节也更加还原。
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图 1 图像传感器工作原理和结构示意图:(a),(b),(c)和(d)分别为CCD、CMOS、前照式图像传感器结构和背照式图像传感器结构[12];(e) 堆栈式CMOS图像传感器;(f) 具有Cu-Cu杂化键合的新型堆栈式背照CMOS图像传感器及器件截面图[13]
Figure 1. Schematic diagrams of imaging sensor working principles and structures: (a), (b), (c) and (d) are CCD, CMOS, structure of front-illuminated image sensor cross-section, and structure of back-illuminated image sensor cross-section, respectively[12]; (e) Stacked CMOS image sensor; (f) New stacked BI-CIS with Cu-Cu hybrid bonding and cross-sectional view of the device[13]
图 2 有机、无机稀土掺杂化合物增强紫外图像传感器:(a) Lumogen结构;(b) Lumogen薄膜紫外-可见吸收光谱[37];(c) 镀膜前(i)和镀膜后(ii)的CCD汞灯谱线[40];(d) 不同方法制备的晕苯薄膜的反射和透射光谱图[42];(e) 不同膜层厚度下的CMOS图像传感器在紫外波段范围内的量子效率[44];(f) LiSr(1-3x/2)VO4: xTb3+的荧光激发和发射光谱[45]
Figure 2. Ultraviolet image sensor enhanced by organic and inorganic rare earth doped compounds: (a) Structure of Lumogen; (b) UV-vis absorption spectrum of Lumogen film[37]; (c) CCD mercury lamp spectra before (i)and after(ii) coating[40]; (d) Reflectance and transmittance spectra of Coronene film prepared by different methods[42]; (e)Quantum efficiency of CMOS image sensors in the ultraviolet band rang with different film thickness[44]; (f) Photoluminescence excitation and emission spectra of LiSr(1-3x/2)VO4: xTb3+[45]
图 3 量子点增强紫外CMOS器件:(a) 纳米复合薄膜在紫外光和可见光照射下的示意图[57];(b) CdSe/ZnS量子点和硅基量子点纳米复合物的吸收和荧光光谱图[58];(c) 在可见光(i)和紫外光(ii)照射下的量子点涂层CID86器件[59];(d) CdSe/ZnS量子点示意图;(e) 不同膜层的CdSe/ZnS量子点薄膜的荧光发射光谱[61];(f) 量子点涂覆器件的结构图[60]
Figure 3. Quantum dot enhanced UV CMOS devices: (a) A schematic representation of the nanocomposites film illuminated by UV and visible light[57]; (b) Absorption and PL spectra of CdSe/ZnS QDs and QD/silica nanocomposites[58]; (c) Photoes of CID86 devicecoated by QD under visible (i) and UV (ii) light illumination[59]; (d) Diagram of CdSe/ZnS quantum dot; (e) PL emission spectra of CdSe/ZnS QD films with different layers[61]; (f) Schematic of a QD coated device[60]
图 4 钙钛矿量子点增强紫外CCD器件:(a) 钙钛矿结构示意图;(b) CsPbX3胶体量子点溶液的荧光成像图和相应的荧光光谱[62];(c) MAPbBr3量子点的紫外-可见吸收光谱和透射电镜图像[66];(d) PQDCF紫外增强硅光电二极管结构示意图;(e) PQDCF旋涂前后的EMCCD成像传感器的外量子效率;(f) PQDCF的荧光光谱及在室内日光(上)和365 nm紫外灯下(下)的照片[68]
Figure 4. Perovskite quantum dots enhanced ultraviolet CCD devices: (a) Structure diagram of perovskite; (b) Photoes of CsPbX3 colloidal QDs solutions and corresponding PL spectra[61]; (c) UV-Vis absorption spectra and TEM image of MAPbBr3 QDs[65]; (d) Structure diagram of the PQDCF UV enhanced EMCCD; (e) The EQE of EMCCD image sensor before and after coating PQDCF, (f) PL spectrum of PQDCF with the corresponding photographs under ambient daylight (up) and under a 365 nm UV lamp (down) shown in inset[67]
图 5 图像传感器在紫外成像方面的应用:(a) 盐酸二甲双胍可见透射和紫外吸收图像[4];(b) 片剂的可见光和紫外图像[69];(c)电站烟囱校准后的SO2图像[70];(d) 高分辨率极紫外相机模型[72];(e) 哈勃望远镜第三代相机的CCD探测器封装图[71];(f) SUIT所有子系统的有效载荷[73]
Figure 5. Applications of image sensor in ultraviolet imaging: (a) UV and visible absorbance maps obtained for Glucophage SR[4]; (b) Visible and ultraviolet images of tablets[69]; (c) The resulting calibrated SO2 image of Drax power station stack[70]; (d) HRIEUV camera flight model[72]; (e) Peckaging image of CCD detector of Hubble telescope third generation camera[71]; (f) SUIT (Solar Ultraviolet Imaging Telescope) payload with all the subsystems[73]
表 1 CMOS与CCD图像传感器参数对比
Table 1 Comparison of CMOS and CCD image sensor parameters
Parameter CMOS CCD Signal to noise ratio Low High Sensitivity High Higher Size Small Large Power consumption High to mode rate High System complexity Low High Cost Low High Signal from pixel Voltage Electron packet Signal from chip Bits(digital) Analog voltage 表 2 紫外增强CMOS/CCD图像传感器
Table 2 UV-enhanced CMOS/CCD image sensor
Year Sensor QE Wavelength range Number of pixels Ref. 1987 CCD 22%@250 nm 10-300 nm - [14] 1997 CCD 50% 200-400 nm - [15] 2007 CMOS 15%@300 nm 300 nm 4k×3k [16-17] 2008 CCD 45%@400 nm 250-900 nm 1k×1k [18] 2009 CMOS 52%@400 nm 400-1000 nm - [19] 2012 CCD 50% 180-200 nm 1024×512 [20] 2012 CMOS 50% 5-20 nm 1k×1k [21] 2013 CMOS - 200-1000 nm - [22] 2014 CMOS - - 3k×3k [23] 2015 CMOS 190-1000 nm 1k×1k [24] 2016 EMCCD 80%@205 nm 170-320 nm 1k×2k [25] 2016 CMOS - 200-1100 nm - [26] 2019 CMOS 46%@300 nm 190-1000 nm - [27] 2019 CMOS - 200-1000 nm 640×480 [28] -
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