Nondestructive Crack Testing via Infrared Thermal Imaging Using Halogen Lamp Excitation
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摘要: 钢轨安全状态的监测对保证列车的安全运行至关重要,针对钢轨裂纹的检测,本文阐述了几种不同的裂纹检测技术。重点分析了红外热成像检测技术在钢轨裂纹检测中的应用,该检测技术包括外激励加热、红外图像采集以及图像处理三部分。本文将常用激励方式进行了介绍和对比,详细阐述了卤素灯作为激励在裂纹检测中的应用;其次,搭建了基于卤素灯激励的红外热成像检测实验平台;然后,对采集到的红外图像进行增强处理,并提出改进图像处理算法;最后,本文对该技术未来的应用前景做出展望。Abstract: The monitoring of rail safety status is crucial to ensure the safe operation of trains. Aiming at rail crack detection, this study quantitatively compares different crack detection technologies and analyzes the application of infrared thermal imaging technology in rail crack detection. The proposed detection technology comprises three parts: external excitation heating, infrared image acquisition and image processing. Firstly, the common excitation methods are introduced and compared. The application of halogen lamps as excitation sources in crack detection is described in detail. Secondly, a halogen lamp excitation based infrared thermal imaging detection experimental platform is developed. Thirdly, an improved image processing algorithm is proposed to enhance the collected infrared image. Finally, this study discusses the prospects of applying the proposed technology in the future.
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Keywords:
- rail crack /
- infrared thermal imaging /
- excitation /
- image processing
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0. 引言
近年来,鉴于衍射光学元件能够为系统提供一定的设计自由度,被广泛地应用于各种光学系统中,如成像系统、光束整形系统、复眼和3D显示等[1-5]。衍射效率和带宽积分平均衍射效率(polychromatic integral diffraction efficiency,PIDE)是决定DOE工作波段的重要参数。DOE的衍射效率对入射角度具有一定的依赖性。入射角度的增大会降低其衍射效率,进一步影响折衍混合光学系统的成像质量。
目前,成像光学系统中的DOE都是利用标量衍射理论(scalar diffraction theory,SDT)进行设计的,但该理论没有考虑入射角度对微结构高度的影响[6]。随着微结构表面入射角的增大,DOE的衍射效率会不断下降[7-8]。当微结构高度和波长处于同一数量级时,采用SDT计算衍射效率的准确度会大幅度下降,此时可以利用矢量衍射理论(vector diffraction theory,VDT)进行分析计算[9-12]。但是,VDT很难通过优化设计微结构高度等参数实现衍射效率的最大化。扩展标量衍射理论(extended scalar diffraction theory,ESDT)考虑了入射角度这一参数,其计算结果要比SDT更加精确,能够简化VDT的计算时间,并实现DOE的优化设计[13]。对于工作在可见光波段的双层DOE,文献[14]讨论了基于SDT和VDT计算的入射角度对衍射效率的影响。文献[15]基于ESDT讨论了周期宽度对微结构高度和PIDE的影响,但并没有给出DOE结构参数的优化设计。对于工作在一定入射角度范围内的DOE,基于ESDT对PIDE的优化设计未见报道。
本文基于ESDT,提出了工作在一定入射角度范围内,基于复合带宽积分平均衍射效率(comprehensive PIDE,CPIDE)最大化实现DOE设计波长、微结构高度等结构参数的优化设计方法。以工作在红外波段的DOE为例进行了分析与讨论。该方法可以实现工作在一定入射角度范围内的DOE结构参数的优化设计,特别是在相对周期宽度不是很大的情况下。
1. 理论模型
当光线以入射角θ传播到DOE的微结构上时,如图 1所示,根据衍射光栅公式,得到DOE的衍射光栅方程为:
$$ T\left(n_{\mathrm{r}} \sin \theta_{\mathrm{d}}-n_{\mathrm{i}} \sin \theta\right)=m \lambda $$ (1) 式中:T为光栅周期;ni与nr分别为相应介质材料在波长λ时的折射率;θd为衍射角;m为衍射级次。依据Snell折射定律,考虑衍射微结构对光线传播的影响,有:
$$ n_{\mathrm{r}} \sin (\theta+\alpha)=n_{\mathrm{r}} \sin \left(\theta_{\mathrm{r}}+\alpha\right) $$ (2) 式中:α为微结构表面的倾角,tanα=d/T,θr为折射角。当衍射角等于折射角,即θr=θd时,第m衍射级次的衍射效率最大。利用公式(1)和(2),得到DOE的表面微结构高度d为:
$$ d\left( {\lambda , \theta , T} \right) = \frac{{m\lambda }}{{{n_{\text{i}}}\left( \lambda \right)\cos \theta - \sqrt {{n_{\text{r}}}^2\left( \lambda \right) - {{(\frac{{m\lambda }}{T} + {n_{\text{i}}}\left( \lambda \right)\sin \theta )}^2}} }} $$ (3) 由公式(3)可知,当衍射面两端介质材料确定后,DOE的微结构高度与波长、入射角度和周期宽度有关。当DOE工作在正入射的状态下,微结构高度可以表示为:
$$ d\left( {\lambda , T} \right) = \frac{{m\lambda }}{{{n_{\text{i}}}\left( \lambda \right) - {n_{\text{r}}}\left( \lambda \right)\sqrt {1 - {{\left( {\frac{{m\lambda }}{{{n_{\text{r}}}\left( \lambda \right)T}}} \right)}^2}} }} $$ (4) 当周期宽度远大于波长并且光线正入射时,得到基于SDT的DOE的微结构高度为:
$$ {d_{{\text{SDT}}0}} = \frac{{m{\lambda _0}}}{{{n_{\text{i}}}\left( {{\lambda _0}} \right) - {n_{\text{r}}}\left( {{\lambda _0}} \right)}} $$ (5) 可见,基于SDT,当介质材料和衍射级次确定后,微结构高度仅由设计波长λ0决定。
基于ESDT,斜入射时DOE的衍射效率为:
$$ \begin{array}{l} {\eta _m}\left( {\lambda , \theta , T} \right) = \sin {{\text{c}}^2}\{ m - \hfill \\ \frac{d}{\lambda }\left[ {\sqrt {n_{\text{r}}^2\left( \lambda \right) - n_{\text{i}}^2\left( \lambda \right){{\sin }^2}\theta } - {n_{\text{i}}}\left( \lambda \right)\cos \theta } \right]\} \hfill \\ \end{array} $$ (6) 利用公式(3)和(6)可以计算周期宽度和入射角度对DOE衍射效率的影响。若把公式(6)中的d换成dSDT0即得到SDT的计算结果。
若DOE工作在λmin~λmax波段范围时,其PIDE为:
$$ {\overline \eta _m}\left( \lambda \right) = \frac{1}{{{\lambda _{\max }} - {\lambda _{\min }}}}\int_{{\lambda _{\min }}}^{{\lambda _{\max }}} {{\eta _m}{\text{d}}\lambda } $$ (7) 要基于PIDE的最大化确定微结构高度的大小,需要利用公式(7),在周期宽度已知的前提下,确定公式(3)中的设计波长λ0和设计入射角度θ0,即可计算得到微结构高度d0,即:
$$ {d_0} = \frac{{m{\lambda _0}}}{{{n_{\text{i}}}\left( {{\lambda _0}} \right)\cos {\theta _0} - \sqrt {{n_{\text{r}}}^2\left( {{\lambda _0}} \right) - {{(\frac{{m{\lambda _0}}}{T} + {n_{\text{i}}}\left( {{\lambda _0}} \right)\sin {\theta _0})}^2}} }} $$ (8) 若工作在成像光学系统中的DOE,其入射角度范围为θmin~θmax,则DOE在整个工作入射角度范围内的CPIDE为:
$$ \begin{array}{l} {\overline \eta _{\text{c}}}(\lambda , \theta , T) = \frac{1}{{{\theta _{\max }} - {\theta _{\min }}}}\int_{{\theta _{\min }}}^{{\theta _{\max }}} {{{\overline \eta }_m}{\text{d}}\theta } {\text{ = }} \hfill \\ \quad \frac{1}{{{\theta _{\max }} - {\theta _{\min }}}} \cdot \frac{1}{{{\lambda _{\max }} - {\lambda _{\min }}}}\int_{{\theta _{\min }}}^{{\theta _{\max }}} {\int_{{\lambda _{\min }}}^{{\lambda _{\max }}} {{\eta _m}{\text{d}}\lambda } {\text{d}}\theta } \hfill \\ \end{array} $$ (9) 若DOE工作在几个分离的入射角度范围内,如变焦系统,则其CPIDE为:
$$ \begin{array}{l} {\overline \eta _{\text{c}}}(\lambda , \theta , T) = \sum\limits_{z = 1}^N {\frac{{{\omega _z}}}{{{\theta _{z\max }} - {\theta _{z\min }}}}\int_{{\theta _{z\min }}}^{{\theta _{z\max }}} {{{\overline \eta }_{\text{m}}}{\text{d}}\theta } } {\text{ = }} \hfill \\ \quad \quad \frac{{{\omega _1}}}{{{\theta _{1\max }} - {\theta _{1\min }}}} \cdot \int_{{\theta _{1\min }}}^{{\theta _{1\max }}} {{{\overline \eta }_{\text{m}}}{\text{d}}\theta } + \frac{{{\omega _2}}}{{{\theta _{2\max }} - {\theta _{2\min }}}} \cdot \hfill \\ \quad \quad \int_{{\theta _{2\min }}}^{{\theta _{2\max }}} {{{\overline \eta }_{\text{m}}}{\text{d}}\theta } + \cdots + \frac{{{\omega _N}}}{{{\theta _{N\max }} - {\theta _{N\min }}}} \cdot \int_{{\theta _{N\min }}}^{{\theta _{N\max }}} {{{\overline \eta }_{\text{m}}}{\text{d}}\theta } \hfill \\ \end{array} $$ (10) 式中:θzmin和θzmax分别表示第z个入射角范围的最小和最大入射角;ωz为第z个入射角范围的权重;N表示总的入射角范围数量。
2. 基于SDT的仿真与分析
以工作在红外波段1.4~2.2 μm的DOE为例,基底材料采用硫化锌,衍射级次m=1。假设光束从空气介质入射到衍射基底,如图 2所示为基于SDT计算得到的正入射时DOE的PIDE与波长的关系。在整个波段范围内,PIDE最高为94.47%,此时对应的峰值波长为设计波长,即1.7410 μm,利用公式(5)计算得到微结构高度dSDT0为1.3726 μm。
当入射角度分别为20°和40°时,DOE的衍射效率与波长的关系如图 3所示。正入射时,在设计波长处的衍射效率为100%,最低衍射效率为81.07%。随着入射角度的增大,100%衍射效率对应的设计波长向长波方向移动;而且在工作波段范围内的最低衍射效率呈现快速下降的变化趋势。
图 4给出了PIDE与入射角度的关系。当入射角度分别为20°、40°和60°时,DOE在整个波段的PIDE分别为94.27%、91.13%%和78.05%。假设各个视场的权重相同,当DOE分别工作在0°~20°、0°~40°和0°~60°入射角度范围内时,利用公式(10)计算得到CPIDE分别为94.43%、93.81%和91.13%。可见,基于正入射计算得到的微结构高度,随着入射角度或入射角度范围的增大,对应的PIDE或CPIDE逐渐减小。
3. 基于ESDT的仿真与分析
基于ESDT,首先分析周期宽度和入射角度对DOE微结构高度的影响;然后,分析一定的周期宽度和不同入射角度时DOE的衍射效率;最后,基于CPIDE最大化实现微结构高度等结构参数的优化设计。
3.1 微结构高度的分析
由公式(3)可知,DOE的微结构高度的大小与周期宽度和入射角度有关。当入射角度分别为0°、20°、40°和60°时,DOE的微结构高度与相对周期宽度(周期宽度与波长1.7410 μm的比值)的关系如图 5所示。正入射时,当相对周期宽度为5时,衍射微结构高度为1.3758 μm,与dSDT0相比,增大了0.0032 μm。如表 1所示,当入射角度为40°时,相对周期宽度分别为10或无穷大时,对应的微结构高度分别为1.2511 μm和1.2355 μm,与dSDT0相比,分别减小了8.85%和9.99%。如图 5和表 1所示,当入射角度或者相对周期宽度改变时,基于ESDT计算得到的微结构高度与SDT的偏差不同。所以,当入射角度较大时,需要考虑入射角度和相对周期宽度对微结构高度的影响。
表 1 微结构高度与周期宽度的关系Table 1. Relationship between microstructure height and period widthIncident angle/(°) Period width /λ 5 10 20 30 ∞ 0 1.3758 1.3734 1.3728 1.3727 1.3726 20 1.3579 1.3463 1.3411 1.3395 1.3365 40 1.2689 1.2511 1.2430 1.2404 1.2355 60 1.1266 1.1076 1.0988 1.0960 1.0907 3.2 斜入射时的仿真与分析
当相对周期宽度确定为20时,在上述4个入射角度情况下,DOE的PIDE与波长的关系如图 6所示。随着入射角度的增大,PIDE最大值对应的峰值设计波长向短波方向移动。利用公式(4)计算得到微结构高度如表 2所示。伴随着设计波长的减小,微结构高度随入射角度的增大也减小。
表 2 基于带宽积分平均衍射效率最大化确定的结构参数Table 2. Structural parameters determined by maximum PIDEParameters Incident angle/° 0 20 40 60 Maximum PIDE/% 94.47 94.47 94.48 94.50 Design wavelength/μm 1.7399 1.7298 1.7220 1.7182 Microstructure height/μm 1.3724 1.3360 1.2350 1.0904 利用上述计算得到的设计波长和微结构高度,计算DOE的衍射效率如图 7所示。入射角度为20°时,采用SDT和ESDT设计时的DOE在整个工作波段范围内的衍射效率图 7(a)所示,衍射效率最小值分别为76.18%和81.65%,提高了5.47%。当入射角度分别增大到40°和60°时,如图 7(b)、(c)所示,衍射效率最小值分别从59.25%增大到81.17%,从29.39%增大到81.21%,分别提高了21.92%和51.82%。可见,当入射角度偏离正入射时,利用基于ESDT计算得到的设计波长和微结构高度能够显著提高衍射效率。
3.3 一定入射角度范围工作时的仿真与分析
同样假设DOE的相对周期宽度确定为20,当DOE工作时的入射角度范围为0°~20°,并假设各个视场的权重因子相同,DOE的PIDE与波长和入射角度的关系如图 8所示,基于CPIDE的最大化(94.45%),得到设计波长为1.73 μm,设计角度为8.25°,进一步计算得到DOE的微结构高度为1.3615 μm。若DOE工作的入射角度范围增大到0°~40°或0°~60°时,其微结构高度和CPIDE等参数如表 3所示。可见,随着DOE衍射面入射角度范围的增大,其微结构高度和CPIDE都逐渐减小。
表 3 基于带宽积分平均衍射效率最大化确定的结构参数Table 3. Structural parameters determined by maximum CPIDEParameters Incident angle range/° 0-20 0-40 0-60 Design wavelength/μm 1.73 1.72 1.72 Design angle/° 8.25 16 24 Microstructure height/μm 1.3615 1.3396 1.3142 CPIDE/% 94.45 94.15 92.67 4. 结论
本文基于ESDT,建立了DOE的微结构高度与入射角度、周期宽度的数学关系模型,提出了工作在一定入射角度范围内,基于CPIDE最大化实现设计波长和微结构高度等结构参数的优化设计方法。对工作在红外波段的DOE进行仿真分析。当入射角度为40°时,对比SDT,基于ESDT计算得到的在工作波段范围内衍射效率的最小值提高了29.39%;当DOE工作在0°~40°范围内时,通过优化设计得到微结构高度为1.3396 μm,CPIDE为94.15%。随着DOE在各类光学系统中的广泛应用,该方法为工作在较大入射角度范围内DOE的优化设计提供了理论依据。
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表 1 红外检测常用激励方式
Table 1 Common excitation methods of infrared detection
Excitation modes Advantages Disadvantages Scope of application Ultrasonic excitation It is not limited by the shape of the tested object, has the characteristics of selective heating for closed crack defects, and only produces temperature rise in the crack defect area. It belongs to internal excitation and can detect internal micro cracks[13] The excitation effect is greatly affected by the coupling effect and excitation direction, and the mechanical wave vibration may damage the internal interface of the material Defects such as closed cracks on the surface or sub surface of parts with complex shape Laser excitation High energy density, high-intensity energy input to tiny areas Low efficiency and small single excitation area; high energy will lead to thermal stress on the local surface of the material Defect detection of small parts or small areas Halogen lamp excitation It can operate at a higher temperature, with larger excitation area and higher efficiency. In addition, the halogen lamp has the advantages of low cost, long service life, good seismic resistance and easy heat control The detection depth is shallow Rapid detection of surface defects in large areas Pulse excitation It can quickly obtain the original thermal image, and is not sensitive to uneven illumination. Simultaneously, there is no need for reference points Due to the uneven distribution of heat flow on the surface of the test piece, and greatly affected by the reflectivity of the surface of the test piece and the surrounding environmental noise, it is difficult to accurately judge the defects according to the original thermal image of the surface of the test piece [14] Can be used for composite material with defects inside Electromagnetic excitation It is not limited by the shape of the detection image, does not produce mechanical vibration, and will not damage the internal structure of the material Affected by the surface skin effect of induced current, the excitation depth is shallow Surface and subsurface defect detection of with high-conductivity materials -
[1] 郭火明, 王文健, 刘腾飞, 等. 重载铁路钢轨损伤行为分析[J]. 中国机械工程, 2014, 25(2): 267-272. DOI: 10.3969/j.issn.1004-132X.2014.02.025 GUO Huoming, WANG Wenjian, LIU Tengfei, et al. Analysis of Damage Behavior of Heavy-haul Railway Rails[J]. China Mechanical Engineering, 2014, 25(2): 267-272. DOI: 10.3969/j.issn.1004-132X.2014.02.025
[2] 田贵云, 高斌, 高运来, 等. 铁路钢轨缺陷伤损巡检与监测技术综述[J]. 仪器仪表学报, 2016, 37(8): 1763-1780. DOI: 10.3969/j.issn.0254-3087.2016.08.008 TIAN Guiyun, GAO Bin, GAO Yunlai, et al. Review of railway rail defect non-destructive testing and monitoring[J]. Chinese Journal of Scientific Instrument, 2016, 37(8): 1763-1780. DOI: 10.3969/j.issn.0254-3087.2016.08.008
[3] Kim G, Seo M K, Kim Y I, et al. Development of phased array ultrasonic system for detecting rail cracks[J]. Sensors and Actuators A Physical, 2020, 311: 112086. DOI: 10.1016/j.sna.2020.112086
[4] JIANG Yi, WANG Haitao, CHEN Shuai, et al. Visual quantitative detection of rail surface crack based on laser ultrasonic technology[J]. Optik, 2021, 237: 166732. DOI: 10.1016/j.ijleo.2021.166732
[5] 李浩然, 高斌, 张喜源, 等. 电磁热多物理耦合成像检测方法研究[J]. 中国测试, 2020, 46(12): 99-104. DOI: 10.11857/j.issn.1674-5124.2020090090 LI Haoran, GAO Bin, ZHANG Xiyuan, Research on imaging detection method of thermo-electromagnetic multi-physical coupling effects[J]. China Measurement & Test, 2020, 46(12): 99-104. DOI: 10.11857/j.issn.1674-5124.2020090090
[6] YUAN F, YU Y, LIU B, et al. Investigation on velocity effect in pulsed eddy current technique for detection cracks in ferromagnetic material [C]//IEEE Transactions on Magnetics, 2020, 56(9): 3012341.
[7] 杨理践, 耿浩, 高松巍. 基于多级磁化的高速漏磁检测技术研究[J]. 仪器仪表学报, 2018, 39(6): 148-156. https://www.cnki.com.cn/Article/CJFDTOTAL-YQXB201806019.htm YANG Lijian, GENG Hao, GAO Songwei. Study on high-speed magnetic flux leakage testing technology based on multistage magnetization[J]. Chinese Journal of Scientific Instrument, 2018, 39(6): 148-156. https://www.cnki.com.cn/Article/CJFDTOTAL-YQXB201806019.htm
[8] XU Changhang, XIE Jing, CHEN Guoming, et al. An infrared thermal image processing framework based on superpixel algorithm to detect cracks on metal surface[J]. Infrared Physics and Technology, 2014, 67: 266-272. DOI: 10.1016/j.infrared.2014.08.002
[9] 郑凯, 江海军, 陈力. 红外热波无损检测技术的研究现状与进展[J]. 红外技术, 2018, 40(5): 401-411. http://hwjs.nvir.cn/article/id/hwjs201805001 ZHENG Kai, JIANG Haijun. CHEN Li. Infrared thermography NDT and its development[J]. Infrared Technology, 2018, 40(5): 401-411 http://hwjs.nvir.cn/article/id/hwjs201805001
[10] YUAN L, ZHU X, HONG K. Detection of material surface cracks by infrared non-destructive testing[C]//2020 11th International Conference on Prognostics and System Health Management (PHM-2020 Jinan), 2020: DOI: 10.1109/PHM-Jinan48558.2020.00114.
[11] YANG J, WANG W, LIN G, et al. Infrared thermal imaging-based crack detection using deep learning[J]. IEEE Access, 2019, 7: 182060-182077. DOI: 10.1109/ACCESS.2019.2958264
[12] 沈功田, 王尊祥. 红外检测技术的研究与发展现状[J]. 无损检测, 2020, 42(4): 1-9, 14. https://www.cnki.com.cn/Article/CJFDTOTAL-WSJC202004003.htm SHEN Gongtian, WANG Zunxiang, Progress of infrared testing technology[J]. Nondestructive Testing, 2020, 42(4): 1-9, 14. https://www.cnki.com.cn/Article/CJFDTOTAL-WSJC202004003.htm
[13] 徐欢, 殷晨波, 李向东, 等. 超声红外检测中裂纹微观界面生热的数值模拟[J]. 南京工业大学学报: 自然科学版, 2019, 41(4): 493-500 DOI: 10.3969/j.issn.1671-7627.2019.04.015 XU Huan, YIN Chenbo, LI Xiangdong, et al. Numerical simulation of the heat generated by the microcosmic interface of cracks in ultrasonic infrared detection[J]. Journal of Nanjing Tech University: Natural Science Edition, 2019, 41(4): 493-500. DOI: 10.3969/j.issn.1671-7627.2019.04.015
[14] CHI Wubu, ZHAO Bo, LIU Tao, et al. Infrared thermal imaging detection of debonding defects in carbon fiber reinforced polymer based on pulsed thermal wave excitation[J]. Thermal Science, 2020, 24(6B): 3887 - 3892.
[15] 顾桂梅, 贾文晶. 钢轨轨底裂纹红外热波无损检测数值模拟分析[J]. 红外技术, 2018, 40(3): 294-299. http://hwjs.nvir.cn/article/id/hwjs201803016 GU Guimei, JIA Wenjing. Numerical simulation analysis of infrared thermal wave nondestructive testing of rail bottom crack[J]. Infrared Technology, 2018, 40(3): 294-299. http://hwjs.nvir.cn/article/id/hwjs201803016
[16] 李玉杰, 李科, 钟安彪, 等. 卤素灯加热红外成像检测技术仿真研究[J]. 激光与红外, 2016, 46(12): 1477-1480. DOI: 10.3969/j.issn.1001-5078.2016.12.008 LI Yujie, LI Ke, ZHONG Anbiao, et al. Simulation research of infrared image detection technology for halogen lamp heating[J]. Laser & Infrared, 2016, 46(12): 1477-1480. DOI: 10.3969/j.issn.1001-5078.2016.12.008
[17] ZHOU Zhenggan, HE Pengfei, ZHAO Hanxue, et al. Detection of skin desoldering defect in Ti-alloy honeycomb structure using lock-in infrared thermography test[J]. Journal of Beijing University of Aeronautics and Astronautics. 2016, 42(9): 1795-1802. https://www.cnki.com.cn/Article/CJFDTOTAL-BJGD202103011.htm [18] 黄涛. 基于红外热波技术的钢轨疲劳裂纹深度定量检测研究[D]. 兰州: 兰州交通大学, 2015. HUANG Tao. Rail Fatigue Crack Depth Quantitative Detection Based on Infrared Thermal Wave Technology[D]. Lanzhou: Lanzhou Jiaotong University, 2015.
[19] 李科, 钟安彪, 李玉杰, 等. 基于热风激励的红外成像检测技术研究[J]. 激光与红外, 2016, 46(7): 823-826. DOI: 10.3969/j.issn.1001-5078.2016.07.010 LI Ke, ZHONG Anbiao, LI Yujie. Research on infrared imaging detection based on hot wind heating[J]. Laser & Infrared, 2016, 46(7): 823-826. DOI: 10.3969/j.issn.1001-5078.2016.07.010
[20] 王加, 周永康, 李泽民, 等. 非制冷红外图像降噪算法综述[J]. 红外技术, 2021, 43(6): 557-565. http://hwjs.nvir.cn/article/id/380dcf6e-de3d-4411-ab70-e246d5c8ea27 WANG Jia, ZHOU Yongkang, LI Zemin, et al. A survey of uncooled infrared image denoising algorithms[J]. Infrared Technology, 2021, 43(6): 557-565 http://hwjs.nvir.cn/article/id/380dcf6e-de3d-4411-ab70-e246d5c8ea27
[21] 王浩, 张叶, 沈宏海, 等. 图像增强算法综述[J]. 中国光学, 2017, 10(4): 438-448. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGA201704005.htm WANG Hao, ZHANG Ye, SHEN Honghai, et al. Review of image enhancement algorithms[J]. Chinese Optics, 2017, 10(4): 438-448. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGA201704005.htm
[22] 李贤阳, 阳建中, 杨竣辉, 等. 基于改进的直方图均衡化与边缘保持平滑滤波的红外图像增强算法[J]. 计算机应用与软件, 2019, 36(3): 96-103. https://www.cnki.com.cn/Article/CJFDTOTAL-JYRJ201903020.htm LI Xianyang, YANG Jianzhong, YANG Junhui, et al. Infrared image enhancement algorithm based on improved histogram equalization and edge preserving smooth filtering[J]. Computer Applications and Software, 2019, 36(3): 96-103. https://www.cnki.com.cn/Article/CJFDTOTAL-JYRJ201903020.htm
[23] 陈明, 谭涛. 基于形态学和高斯滤波的图像快速去雾算法[J]. 计算机应用与软件, 2019, 36(12): 209-213. https://www.cnki.com.cn/Article/CJFDTOTAL-JYRJ201912034.htm CHEN Ming, TAN Tao. A fast image denoising algorithm based on morphology and Gaussian filter[J]. Computer Applications and Software, 2019, 36(12): 209-213. https://www.cnki.com.cn/Article/CJFDTOTAL-JYRJ201912034.htm
[24] 宋人杰, 刘超, 王保军. 一种自适应的Canny边缘检测算法[J]. 南京邮电大学学报: 自然科学版, 2018, 38(3): 72-76. https://www.cnki.com.cn/Article/CJFDTOTAL-NJYD201803012.htm SONG Renjie, LIU Chao, WANG Baojun. Adaptive Canny edge detection algorithm[J]. Journal of Nanjing University of Posts and Telecommunications: Natural Science Edition. 2018, 38(3): 72-76. https://www.cnki.com.cn/Article/CJFDTOTAL-NJYD201803012.htm
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