Study on optical imaging distortion of underwater thermal disturbance

Wang Congzheng; Hu Song; Gao Chunming; Feng Chang

doi:10.12086/oee.2019.180438

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Wang Congzheng, Hu Song, Gao Chunming, et al. Study on optical imaging distortion of underwater thermal disturbance[J]. Opto-Electronic Engineering, 2019, 46(10): 180438. doi: 10.12086/oee.2019.180438

Citation:

Wang Congzheng, Hu Song, Gao Chunming, et al. Study on optical imaging distortion of underwater thermal disturbance[J]. Opto-Electronic Engineering, 2019, 46(10): 180438. doi: 10.12086/oee.2019.180438

Study on optical imaging distortion of underwater thermal disturbance

1.
Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu, Sichuan 610209, China
2.
School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China
3.
University of Chinese Academy of Sciences, Beijing 100049, China

Fund Project: Supported by National Natural Science Foundation of China (61675206)

More Information

^*Corresponding author: Wang Congzheng, E-mail: wangcongzheng@ioe.ac.cn

Received Date 21 August 2018

Revised Date 26 November 2018

Published Date 18 October 2019

Abstract

Abstract

In order to study the influence of underwater thermal disturbance environment on imaging distortion, such as optical imaging distortion or imaging blur, the level of distortion of target image in radial and axial directions was evaluated by using the gray scale distribution, structural similarity image measurement (SSIM), and normalized maximum gray-scale gradient definition evaluation function of underwater images. Furthermore, the laws of underwater thermal disturbance on optical imaging changes were obtained. Experimental results show that with the increase of the axial distance between the imaging system and the target, the level of image distortion and blurring becomes larger and larger. When the axial distance is equal to 500 mm, the SSIM is better than 0.7 and the normalized definition is better than 0.8. When the axial distance reaches 1500 mm, the SSIM is lower than 0.2 and the normalized definition is less than 0.6. In addition, when the axial distance equals 500 mm, the drift of the edges will be greater as the imaging area comes closer the heating source in the radial direction, that is, the imaging distortion is more serious. Finally, under the same axial and radial conditions, the conclusion that the SSIM and normalized definition values of the target images are different at different times can provide a reference for further underwater image restoration.
- underwater imaging /
- thermal disturbance /
- image distortion /
- image blurring /
- image evaluation

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References

[1]	Gu K, Zhai G T, Yang X K, et al. Automatic contrast enhancement technology with saliency preservation[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2015, 25(9): 1480-1494. doi: 10.1109/TCSVT.2014.2372392 CrossRef Google Scholar
[2]	张赫, 徐玉如, 万磊, 等.水下退化图像处理方法[J].天津大学学报, 2010, 43(9): 827-833. doi: 10.3969/j.issn.0493-2137.2010.09.012 CrossRef Google Scholar Zhang H, Xu Y R, Wan L, et al. Processing method for underwater degenerative Image[J]. Journal of Tianjin University, 2010, 43(9): 827-833. doi: 10.3969/j.issn.0493-2137.2010.09.012 CrossRef Google Scholar
[3]	黄文有, 徐向民, 吴凤岐, 等.核环境水下双目视觉立体定位技术研究[J].光电工程, 2016, 43(12): 28-33. doi: 10.3969/j.issn.1003-501X.2016.12.005 CrossRef Google Scholar Huang W Y, Xu X M, Wu F Q, et al. Research of underwater binocular vision stereo positioning technology in nuclear condition[J]. Opto-Electronic Engineering, 2016, 43(12): 28-33. doi: 10.3969/j.issn.1003-501X.2016.12.005 CrossRef Google Scholar
[4]	谌雨章, 叶婷, 程超杰, 等.水下湍流成像退化及优化恢复研究[J].光电工程, 2018, 45(12): 180233. doi: 10.12086/oee.2018.180233 CrossRef Google Scholar Chen Y Z, Ye T, Cheng C J, et al. Degradation and optimal recovery of underwater turbulent imaging[J]. Opto-Electronic Engineering, 2018, 45(12): 180233. doi: 10.12086/oee.2018.180233 CrossRef Google Scholar
[5]	郭继昌, 李重仪, 郭春乐, 等.水下图像增强和复原方法研究进展[J].中国图象图形学报, 2017, 22(3): 273-287. Google Scholar Guo J C, Li C Y, Guo C L, et al. Research progress of underwater image enhancement and restoration methods[J]. Journal of Image and Graphics, 2017, 22(3): 273-287. Google Scholar
[6]	Galdran A, Pardo D, Picón A, et al. Automatic Red-Channel underwater image restoration[J]. Journal of Visual Communication and Image Representation, 2015, 26: 132-145. doi: 10.1016/j.jvcir.2014.11.006 CrossRef Google Scholar
[7]	Trucco E, Olmos-Antillon A T. Self-tuning underwater image restoration[J]. IEEE Journal of Oceanic Engineering, 2006, 31(2): 511-519. doi: 10.1109/JOE.2004.836395 CrossRef Google Scholar
[8]	Zhao X W, Jin T, Qu S. Deriving inherent optical properties from background color and underwater image enhancement[J]. Ocean Engineering, 2015, 94: 163-172. doi: 10.1016/j.oceaneng.2014.11.036 CrossRef Google Scholar
[9]	Chiang J Y, Chen Y C. Underwater image enhancement by wavelength compensation and dehazing[J]. IEEE Transactions on Image Processing, 2012, 21(4): 1756-1769. doi: 10.1109/TIP.2011.2179666 CrossRef Google Scholar
[10]	Serikawa S, Lu H M. Underwater image dehazing using joint trilateral filter[J]. Computers & Electrical Engineering, 2014, 40(1): 41-50. Google Scholar
[11]	Wen Z Y, Lambert A, Fraser A, et al. Bispectral analysis and recovery of images distorted by a moving water surface[J]. Applied Optics, 2010, 49(33): 6376-6384. doi: 10.1364/AO.49.006376 CrossRef Google Scholar
[12]	王马华, 赵正敏, 王士湖, 等.基于改进湍流模型和偏振成像技术的水下退化图像复原方法[J].农业工程学报, 2013, 29(S1): 203-209. Google Scholar Wang M H, Zhao Z M, Wang S H, et al. Restoring method for underwater degraded images based on improved turbulence model and polarization imaging[J]. Transactions of the Chinese Society of Agricultural Engineering, 2013, 29(S1): 203-209. Google Scholar
[13]	李磊, 王庆, 肖照林.一种基于视频的水下场景复原算法[J].系统仿真学报, 2012, 24(1): 188-191, 196. Google Scholar Li L, Wang Q, Xiao Z L. Underwater image restoration algorithm from distorted video[J]. Journal of System Simulation, 2012, 24(1): 188-191, 196. Google Scholar
[14]	吴军, 王志军, 徐海涛, 等.视觉测量中轴向热干扰成像问题研究[J].应用光学, 2018, 39(2): 235-239. Google Scholar Wu J, Wang Z J, Xu H T, et al. Study on axial thermal disturbance imaging in vision measurement[J]. Journal of Applied Optics, 2018, 39(2): 235-239. Google Scholar
[15]	黄战华, 王蓓, 陈嘉佳, 等.变折射率介质对成像变形的影响[J].天津大学学报, 2006, 39(6): 708-711. doi: 10.3969/j.issn.0493-2137.2006.06.013 CrossRef Google Scholar Huang Z H, Wang B, Chen J J, et al. Impact of variable-refractive-index medium on imaging distortion[J]. Journal of Tianjin University, 2006, 39(6): 708-711. doi: 10.3969/j.issn.0493-2137.2006.06.013 CrossRef Google Scholar
[16]	Hou W L, Woods S, Jarosz E, et al. Optical turbulence on underwater image degradation in natural environments[J]. Applied Optics, 2012, 51(14): 2678-2686. doi: 10.1364/AO.51.002678 CrossRef Google Scholar
[17]	Wang C Z, Hu S, Gao C M, et al. Nuclear radiation degradation study on HD camera based on CMOS image sensor at different dose rates[J]. Sensors, 2018, 18(2): 514. doi: 10.3390/s18020514 CrossRef Google Scholar
[18]	Kanaev A V, Hou W, Restaino S R, et al. Restoration of images degraded by underwater turbulence using structure tensor oriented image quality (STOIQ) metric[J]. Optics Express, 2015, 23(13): 17077-17090. doi: 10.1364/OE.23.017077 CrossRef Google Scholar
[19]	洪裕珍, 任国强, 孙健.离焦模糊图像清晰度评价函数的分析与改进[J].光学精密工程, 2014, 22(12): 3401-3408. Google Scholar Hong Y Z, Ren G Q, Sun J. Analysis and improvement on sharpness evaluation function of defocused image[J]. Optics and Precision Engineering, 2014, 22(12): 3401-3408. Google Scholar

Overview

Overview

Overview: Underwater optical imaging is widely used in industry, agriculture, scientific research, and other fields. When there are heat sources in the imaging light path, the target itself is a heat source, or there are disturbances caused by other reasons in the water environment, due to the non-uniformity of the imaging light field, image distortion and defocusing will occur in the underwater image. Therefore, it is very necessary to study the problem of imaging distortion under the condition of underwater thermal disturbance.
In order to study the influence of underwater thermal disturbance, an experimental platform with heat sources and thermal convection is designed. The underwater imaging platform can be adjusted along the axis to change the distance between the camera and the target to be L₁(500 mm), L₂(1000 mm) and L₃(1500 mm), respectively. The distortion level of target image is evaluated through the gray scale distribution, structural similarity image measurement (SSIM), and normalized maximum gray-scale gradient definition evaluation function of underwater images. For gray scale distribution, the trend of disturbance influence is analyzed based on statistics, which provides a basis for image sequence restoration and correction under thermal disturbance environment. For SSIM, it is an effective method for image distortion analysis, and its evaluation index includes the brightness, contrast, as well as structure of the image. For the evaluation of the fuzzy index, the normalized maximum gray gradient clarity evaluation function based on edge information is adopted, that is, the closer the gray value of the whole pixel of the image is to that of the reference image, the smaller the ambiguity degree of the image is.
Experimental results show that with the increase of the axial distance between the imaging system and the target, the level of image distortion and blurring becomes larger and larger. When the axial distance L₁=500 mm, the SSIM is better than 0.7 and the normalized definition is better than 0.8. When the axial distance L₃=1500 mm, the SSIM is lower than 0.2 and the normalized definition is less than 0.6. In addition, when the axial distance is L₁, the drift of the edges will be greater as the imaging area comes closer the heating source in the radial direction, that is, the imaging distortion is more serious. Furthermore, under the same axial and radial conditions, the conclusion that the SSIM and normalized definition values of the target images are different at different times can provide a reference for further underwater image restoration.