Journal of Jilin University(Engineering and Technology Edition) ›› 2021, Vol. 51 ›› Issue (2): 677-684.doi: 10.13229/j.cnki.jdxbgxb20191064

Previous Articles    

Underwater image restoration based on depth map

Ji-chang GUO(),Shan-shan QIAO   

  1. School of Electrical and Information Engineering，Tianjin University，Tianjin 300072，China
  • Received:2019-11-21 Online:2021-03-01 Published:2021-02-09

RICH HTML

 9   

PDF (PC)

236

Abstract:

The estimated accuracy of the depth maps of underwater images affects the quality of the restored images. In order to obtain more precise depth maps， an algorithm was proposed to calculate the depth map based on attenuated channels and luminance map， and then the depth map was used to recover underwater images. First， the depth map of underwater image is estimated according to the relationship between image pixel and scene depth. Then the depth map is further rectified and refined by using the luminance map. Third， the atmospheric light value and the transmission map of the image is calculated using the refined depth map. Finally， the degraded underwater image is restored by inversely solving the underwater imaging model. The experimental results show that compared with the existing algorithms， the model parameters calculated using the rectified depth map are more accurate， and the restored image has better contrast and can maintain more natural color.

Key words: information procession technology, underwater image restoration, underwater imaging model, depth map

CLC Number: 

  • TP391

Fig.1

Flowchart of proposed method"

Fig.2

Algorithm result comparison 1"

Fig.3

Algorithm result comparison 2"

Fig.4

Algorithm result comparison 3"

Fig.5

Algorithm result comparison 4"

Table 1

Quality evaluation index of restored image in fig.5"

指标文献[6]算法文献[8]算法文献[9]算法文献[11]算法本文 算法
Entropy7.0496.9827.3157.6147.618
UICM8.7415.8417.99611.8348.528
PCQI0.8620.9811.1141.1111.168
UCIQE0.5910.5340.6040.6550.660

Table 2

Quality evaluation index of restored image"

指标文献[6]算法文献 [8]算法文献[9]算法文献[11]算法本文 算法
Entropy6.9366.9457.0546.9247.073
UICM5.0485.5735.6847.5025.923
PCQI1.0841.0771.1241.0451.153
UCIQE0.5030.5710.5420.5220.548
1 Akkaynak D, Treibitz T, Shlesinger T, et al. What is the space of attenuation coefficients in underwater computer vision?[C]∥IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, Hawai, 2017: 568-577.
2 徐岩, 孙美双. 基于卷积神经网络的水下图像增强方法[J]. 吉林大学学报: 工学版, 2018, 48(6): 1895-1903.
Xu Yan,Sun Mei-shuang. Enhancing underwater image based on convolutional neural networks[J]. Journal of Jilin University (Engineering and Technology Edition), 2018, 48(6): 1895-1903.
3 郭继昌, 茹丽, 郭春乐. 基于梯度通道和优化透射率的水下图像复原[J]. 吉林大学学报: 工学版,2020, 50(4): 1435-1442.
Guo Ji-chang, Ru Li, Guo Chun-le. Underwater image restoration based on gradient channel and optimize[J]. Journal of Jilin University (Engineering and Technology Edition), 2020, 50(4): 1435-1442.
4 程艳芬, 姚丽娟, 袁巧, 等. 水下视频图像清晰化方法[J].吉林大学学报: 工学版,2020, 50(2): 668-677.
Cheng Yan-fen, Yao Li-juan, Yuan Qiao, et al. Clearing strategy of underwater video image[J]. Journal of Jilin University (Engineering and Technology Edition), 2020, 50(2): 668-677.
5 Akkaynak D, Treibitz T. A revised underwater image formation[C]∥IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, 2018: 6723-6732.
6 Paulo-Drews J R, Nascimento E, Moraes F, et al. Transmission estimation in underwater single images[C]∥IEEE International Conference on Computer Vision Workshops, Sydney, 2013: 825-830.
7 He K M, Sun J, Tang X O, et al. Single image haze removal using dark channel prior[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2011, 33(12): 2341-2353.
8 Galdran A, Pardo D, Picón A, et al. Automatic red-channel underwater image restoration[J]. Journal of Visual Communication & Image Representation, 2015, 26: 132-145.
9 Peng Y T, Cosman P C. Underwater image restoration based on image blurriness and light absorption[J]. IEEE Transactions on Image Processing, 2017, 26(4): 1579-1594.
10 Li C, Guo J. Underwater image enhancement by dehazing and color correction[J]. Journal of Electronic Imaging, 2015, 24(3): 033023.
11 Peng Y T, Cao K, Cosman P C. Generalization of the dark channel prior for single image restoration[J]. IEEE Transactions on Image Processing, 2018, 27(6): 2856-2868.
12 Tan R T. Visibility in bad weather from a single image[C]∥IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Anchorage, 2008: 1-8.
13 McGlamery B L. A computer model for underwater camera systems[J]. Society of Photo-Optical Instrumentation Engineers, 1980, 208: 221-231.
14 Zhao X, Jin T, Qu S. Deriving inherent optical properties from background color and underwater image enhancement[J]. Ocean Engineering, 2015, 94:163-172.
15 He K, Sun J, Tang X. Guided image filtering[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2013, 35(6): 1397-1409.
16 Mobley C D. Radiative transfer in the ocean[J]. Encyclopedia of Ocean Sciences, 2001, 5: 379-388.
17 Wang S, Ma K, Yeganeh H, et al. A patch-structure representation method for quality assessment of contrast changed images[J]. IEEE Signal Processing Letters, 2015, 22(12): 2387-2390.
18 Panetta K, Gao C, Agaian S. Human-visual-system-inspired underwater image quality measures[J]. IEEE Journal of Oceanic Engineering, 2015, 41(3): 1-11.
19 Yang M, Sowmya A. An underwater color image quality evaluation metric[J]. IEEE Transactions on Image Processing, 2015, 24(12): 6062-6071.
[1] LU Zhi-jun,ZHONG Chao,WU Jing-yu. Small feature segmentation method for Spaceborne SAR images [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1925-1930.
[2] SU Han-song,DAI Zhi-tao,LIU Gao-hua,ZHANG Qian-fang. Saliency region detection combining absorbing Markov chain and manifold ranking [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1887-1894.
[3] LI Ju-peng,ZHANG Zu-cheng,LI Mo-yu,MIAO De-fang. Smooth tracking with a kalman filter algorithm for capacitive touch panels [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1910-1916.
[4] HUANG Yong,YANG De-yun,QIAO Sai,MU Zhen-guo. Target detecting with conjugate CFAR in VHR SAR image [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1904-1909.
[5] XU Yan,SUN Mei-shuang. Enhancing underwater image based on convolutional neural networks [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1895-1903.
[6] SHAN Ze-biao, LIU Xiao-song, WANG Chun-yang, SHI Yi-ran, SHI Yao-wu. Joint estimation of DOA and Doppler frequency for coherence and/or same-direction signals [J]. 吉林大学学报(工学版), 2017, 47(6): 1949-1956.
[7] LYU Chao-hui, SHEN Ying-hua, LI Jing-hua. Depth map inpainting method based on Kinect sensor [J]. 吉林大学学报(工学版), 2016, 46(5): 1697-1703.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Jilin University(Engineering and Technology Edition), All Rights Reserved.
Tel: +86-431-85095297　Fax: +86-431-85094074　E-mail: xbgxb@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd