|
|
|
| Recognition of images for underwater vehicle based on improved γ-CLAHE algorithm |
Hong-da CHENG1( ),Hai-ming LUO1,Qing-chao XIA1,2,*(),Can-jun YANG1,2 |
1. Ningbo Research Institute, Zhejiang University, Ningbo 315100, China
2. College of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China |
|
|
|
Abstract The absorption, refraction and reflection of light by water and suspended particles lead to low contrast and blurred details of underwater images. Therefore, it is difficult to apply a single image enhancement algorithm to the recognition of complex underwater environments. An enhancement algorithm based on wavelet transform and an improved γ-CLAHE algorithm was proposed to solve this problem. Firstly, fast median filter was used to remove the noise in the image, and adaptive gamma transform was added to CLAHE to solve the problems of color distortion and loss of details information such as isolated points, thin lines and sudden changes in the underwater image. Secondly, the improved γ-CLAHE method was used to process the low frequency part after wavelet transform decomposition to enhance the image and speed up the algorithm. Then, the wavelet inverse transform was used to get the final enhanced image by fusing the low-frequency part processed by the γ-CLAHE algorithm and the high-frequency part processed by bilateral filtering. Finally, the final image was compared with the images processed by traditional CLAHE, Retinex, and Singh’s fusion algorithm, verifying the effectiveness and superiority of the proposed algorithm in the underwater image processing.
|
|
Received: 23 August 2021
Published: 30 August 2022
|
|
|
| Fund: 国家自然科学基金资助项目(52071292)；浙江省自然科学基金资助项目(LQ20E090008)；宁波市“科技创新2025”重大专项(2021E008) |
|
Corresponding Authors:
Qing-chao XIA
E-mail: ysdxysu7046@163.com;mynameisxia@zju.edu.cn
|
基于改进γ-CLAHE算法的水下机器人图像识别
水体及悬浮粒子对光的吸收、折射及反射导致水下图像对比度低及细节模糊，单一图像增强算法难以适用于水下复杂环境识别.为了解决该问题，提出基于小波变换和改进的γ-CLAHE相融合的图像增强算法.通过快速中值滤波去除图像中噪声，向CLAHE算法中加入自适应伽马变换，解决CLAHE算法处理水下图像色彩失真，丢失孤立点、细线，画面突变等问题. 利用改进的γ-CLAHE算法处理小波变换分解后的低频部分，增强图像并加快运行速度. 通过小波逆变换将γ-CLAHE算法处理后的低频部分和双边滤波处理后的高频部分相融合，得到最终的增强图像. 将实验图像同传统CLAHE、Retinex、Singh融合算法的处理图像进行对比，验证本研究算法在水下图像处理方面的有效性和优越性.
关键词：
水下机器人,
图像增强,
小波变换,
自适应伽马变换,
CLAHE算法
|
|
| [1] |
KANG W, XIWU G Design of optimal scheme for industrial network monitoring of ocean energy power generation system[J]. IOP Conference Series: Earth and Environmental Science, 2021, 647 (1): 012120
doi: 10.1088/1755-1315/647/1/012120
|
|
|
| [2] |
彭飞, 富宁宁, 胡伟, 等 国内外海洋资源研究知识图谱解析及启示[J]. 资源科学, 2020, 42 (11): 2047- 2061
PENG Fei, Fu Ning-ning, HU Wei, et al Analysis and enlightenment of knowledge map of marine resources research at home and abroad[J]. Resources Science, 2020, 42 (11): 2047- 2061
doi: 10.18402/resci.2020.11.01
|
|
|
| [3] |
陈永华, 于非, 张林林, 等 深海综合观测浮标研制及其在热带西太平洋的应用[J]. 海洋科学, 2020, 44 (8): 215- 222
CHEN Yong-hua, YU Fei, ZHANG Lin-lin, et al Design and development of deep-sea buoys and their applications in the tropical western Pacific[J]. Marine Sciences, 2020, 44 (8): 215- 222
|
|
|
| [4] |
王庆琳, 陈水浩, 陈冬妮, 等 中国南海海洋真菌资源及其活性次级代谢产物研究评述[J]. 生物资源, 2020, 42 (5): 505- 514
WANG Qing-lin, CHEN Shui-hao, CHEN Dong-ni, et al Review on the research of marine fungus resources and their bioactive secondary metabolites from the South China Sea[J]. Biotic Resources, 2020, 42 (5): 505- 514
|
|
|
| [5] |
宋泽明, 宁凌 基于 DPSIR-TOPSIS 模型的我国沿海省份海洋资源环境承载力评价及障碍因素研究[J]. 生态经济, 2020, 36 (8): 154- 160
SONG Ze-ming, NING Ling Evaluation and obstacle factors of marine resources and environment carrying capacity of coastal provinces and cities in China based on DPSIR-TOPSIS model[J]. Ecological Economy, 2020, 36 (8): 154- 160
|
|
|
| [6] |
朱光文 我国海洋探测技术五十年发展的回顾与展望(三)[J]. 海洋技术, 2000, (1): 23- 31
ZHU Guang-wen Review and prospect for the marine survey technology development in the past 50 years of china[J]. Journal of Ocean Technology, 2000, (1): 23- 31
doi: 10.3969/j.issn.1003-2029.2000.01.003
|
|
|
| [7] |
BOUDHANE M, NSIRI B Underwater image processing method for fish localization and detection in submarine environment[J]. Journal of Visual Communication and Image Representation, 2016, 39: 226- 238
doi: 10.1016/j.jvcir.2016.05.017
|
|
|
| [8] |
董鹏, 周烽, 赵悰悰, 等 基于双目视觉的水下海参尺寸自动测量方法[J]. 计算机工程与应用, 2021, 57: 271- 278
DONG Peng, ZHOU Feng, ZHAO Cong-cong, et al Automatic measurement of underwater sea cucumber size based on binocular vision[J]. Computer Engineering and Applications, 2021, 57: 271- 278
doi: 10.3778/j.issn.1002-8331.2005-0096
|
|
|
| [9] |
CHEN H, CHUANG W, WANG C Vision-based line detection for underwater inspection of breakwater construction using an ROV[J]. Ocean Engineering, 2015, 109: 20- 33
doi: 10.1016/j.oceaneng.2015.09.007
|
|
|
| [10] |
郭银景, 吴琪, 苑娇娇, 等 水下光学图像处理研究进展[J]. 电子与信息学报, 2021, 43 (2): 1- 10
GUO Yin-jing, WU Qi, YUAN Jiao-jiao, et al Research progress on underwater optical image processing[J]. Journal of Electronics and Information Technology, 2021, 43 (2): 1- 10
doi: 10.11999/JEIT190803
|
|
|
| [11] |
方明, 刘小晗, 付飞蚺 基于注意力的多尺度水下图像增强网络[J]. 电子与信息学报, 2021, 43 (12): 1- 9
FANG Ming, LIU Xiao-han, FU Fei-ran Multi-scale underwater image enhancement network based on attention mechanism[J]. Journal of Electronics and Information Technology, 2021, 43 (12): 1- 9
doi: 10.11999/JEIT200311
|
|
|
| [12] |
SCHARSTEIN D, SZELISKI R A taxonomy and evaluation of dense two-frame stereo correspondence algorithms[J]. International Journal of Computer Vision, 2002, 47 (1): 7- 42
|
|
|
| [13] |
袁利毫, 昝英飞, 钟声华, 等 基于YOLOv3的水下小目标自主识别[J]. 海洋工程装备与技术, 2018, 5 (Suppl.1): 118- 123
YUAN Li-hao, ZAN Ying-fei, ZHONG Sheng-hua, et al Small underwater target recognition based on YOLOv3[J]. Ocean Engineering Equipment and Technology, 2018, 5 (Suppl.1): 118- 123
|
|
|
| [14] |
徐萌. 基于机器视觉的水下海参图像识别技术研究[D]. 济南: 山东大学, 2020: 1-10.
XU Meng. Research on underwater sea cucumber image recognition technology based on machine vision [D]. Jinan: Shandong University, 2020: 1-10.
|
|
|
| [15] |
王若谦. 基于图像融合的水下图像增强算法研究[D]. 大连: 大连海事大学, 2016: 7−24.
WANG Ruo-qian. Underwater image enhancement algorithm based on image fusion [D]. Dalian: Dalian Maritime University, 2016: 7−24.
|
|
|
| [16] |
HAN M, LYU Z, QIU T, et al A review on intelligence dehazing and color restoration for underwater images[J]. IEEE Transactions on Systems Man Cybernetics-Systems, 2020, 50 (5): 1820- 1832
doi: 10.1109/TSMC.2017.2788902
|
|
|
| [17] |
颜阳, 王颖, 丁雪妍, 等 基于图像融合的自适应水下图像增强[J]. 计算机工程与设计, 2021, 42 (1): 1- 6
YAN Yang, WANG Ying, DING Xue-yan, et al Adaptive underwater image enhancement method via image fusion[J]. Computer Engineering and Design, 2021, 42 (1): 1- 6
|
|
|
| [18] |
SINGH G, JAGGI N, VASAMSETTI S, et al. Underwater image/video enhancement using wavelet based color correction (WBCC) method [C]// IEEE Underwater Technology (UT). Chennai: IEEE, 2015: 1−5.
|
|
|
| [19] |
KASHIF I, SALAM R A, AZAM O, et al Underwater image enhancement using an integrated colour model[J]. IAENG International Journal of Computer Science, 2007, 34 (2): 239- 244
|
|
|
| [20] |
邹沛煜, 张卫东, 史金余, 等 基于高低频分量融合的水下图像增强算法[J]. 激光与光电子学进展, 2020, 1- 18
ZOU Pei-yu, ZHANG Wei-dong, SHI Jin-yu, et al Underwater image enhancement algorithm based on fusion of high and low frequency components[J]. Laser and Optoelectronics Progress, 2020, 1- 18
|
|
|
| [21] |
刘奕晖. 基于AUV的水下管道检测及位置估算方法研究[D]. 哈尔滨: 哈尔滨工程大学, 2017: 1−10.
LIU Yi-hui. Research on method of underwater pipeline detection and location estimation based on AUV [D]. Harbin: Harbin Engineering University, 2017: 1−10.
|
|
|
| [22] |
SHI Z, FENG Y, ZHAO M, et al Normalised gamma transformation-based contrast-limited adaptive histogram equalisation with colour correction for sand-dust image enhancement[J]. IET Image Processing, 2020, 14 (4): 747- 756
doi: 10.1049/iet-ipr.2019.0992
|
|
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
| |
Shared |
|
|
|
|
| |
Discussed |
|
|
|
|
