<p style="text-align: left;">Underwater image enhancement based on red channel weighted compensation and gamma correction model

Wending Xiang; Ping Yang; Shuai Wang; Bing Xu; Hui Liu

doi:10.29026/oea.2018.180024

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Xiang W D, Yang P, Wang S, Xu B, Liu H. Underwater image enhancement based on red channel weighted compensation and gamma correction model. Opto-Electron Adv 1, 180024 (2018. doi: 10.29026/oea.2018.180024

Citation:

Xiang W D, Yang P, Wang S, Xu B, Liu H. Underwater image enhancement based on red channel weighted compensation and gamma correction model. Opto-Electron Adv 1, 180024 (2018. doi: 10.29026/oea.2018.180024

Original Article Open Access

Underwater image enhancement based on red channel weighted compensation and gamma correction model

1.
Key Laboratory of Adaptive Optics, Chinese Academy of Sciences, Chengdu 610209, China
2.
Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China
3.
University of Chinese Academy of Sciences, Beijing 100049, China
4.
Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China

More Information

Corresponding author: P Yang, E-mail: pingyang2516@163.com

Received Date 20 October 2018

Accepted Date 08 December 2018

Available Online 11 December 2018

Published Date 11 December 2018

Abstract

Abstract

Due to the special characteristics of light in water, the information of the red channel is seriously attenuated in collected image. This causes other colors to dominate the image. This paper proposes an underwater image enhancement algorithm based on red channel weighted compensation and gamma correction model. Firstly, by analyzing the attenuation characteristics of RGB channels, the intensity and the edge information of red channel are compensated by weighting the attenuation coefficient ratio between different channels to correct the chromaticity. Then the gamma correction model is employed to stretch the intensity range to enhance the contrast which makes the image look clearer. The experimental results show that the proposed algorithm can correct the color cast effect and improve the contrast by nearly 2 times for the underwater images with too much red component attenuation.
- underwater imaging /
- gamma correction /
- image enhancement /
- attenuation

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References

[1]	Maccarone A, Mccarthy A, Ren X, Warburton RE, Wallace AM et al. Underwater depth imaging using time-correlated single-photon counting. Opt Express 23, 33911-33926 (2015). doi: 10.1364/OE.23.033911 CrossRef Google Scholar
[2]	Galdran A, Pardo D, Picón A, Alvarez-Gila A. Automatic Red-Channel underwater image restoration. J Vis Commun Image Represent 26, 132-145 (2015). doi: 10.1016/j.jvcir.2014.11.006 CrossRef Google Scholar
[3]	Guan J G, Zhu J P, Tian H, Hou X. Real-time polarization difference underwater imaging based on Stokes vector. Acta Phys Sin 64, 224203 (2015). Google Scholar
[4]	Yang M, Sowmya A. An underwater color image quality evaluation metric. IEEE Trans Image Process 24, 6062-6071 (2015). doi: 10.1109/TIP.2015.2491020 CrossRef Google Scholar
[5]	Serikawa S, Lu H M. Underwater image dehazing using joint trilateral filter. Comput Electr Eng 40, 41-50 (2014). doi: 10.1016/j.compeleceng.2013.10.016 CrossRef Google Scholar
[6]	Peng Y T, Cosman P C. Underwater image restoration based on image blurriness and light absorption. IEEE Trans Image Process 26, 1579-1594 (2017). doi: 10.1109/TIP.2017.2663846 CrossRef Google Scholar
[7]	Li C Y, Guo J C, Cong R M, Pang Y W, Wang B. Underwater image enhancement by dehazing with minimum information loss and histogram distribution prior. IEEE Trans Image Process 25, 5664-5677 (2016). doi: 10.1109/TIP.2016.2612882 CrossRef Google Scholar
[8]	Priyadharsini R, Sree Sharmila T, Rajendran V. A wavelet transform based contrast enhancement method for underwater acoustic images. Multidimens Syst Signal Process, 29, 1845-1859 (2018). doi: 10.1007/s11045-017-0533-5 CrossRef Google Scholar
[9]	He B, Liang Y, Feng X, Nian R, Yan T H et al. AUV SLAM and experiments using a mechanical scanning forward-looking sonar. Sensors 12, 9386-9410 (2012). doi: 10.3390/s120709386 CrossRef Google Scholar
[10]	Zhang S, Wang T, Dong J Y, Yu H. Underwater image enhancement via extended multi-scale Retinex. Neurocomputing 245, 1-9 (2017). doi: 10.1016/j.neucom.2017.03.029 CrossRef Google Scholar
[11]	Iqbal K, Abdul Salam R, Osman A, Talib A Z. Underwater image enhancement using an integrated colour model. IAENG In J of Comput Sci 34, 239-244 (2007). Google Scholar
[12]	Iqbal K, Odetayo M, James A, Abdul Salam R, Talib A Z H. Enhancing the low quality images using unsupervised colour correction method. In Proceedings of 2010 IEEE International Conference on Systems, Man and Cybernetics (IEEE, 2010); http://doi.org/10.1109/ICSMC.2010.5642311. Google Scholar
[13]	Ghani A S A, Isa N A M. Underwater image quality enhancement through integrated color model with Rayleigh distribution. Appl Soft Comput 27, 219-230 (2015). doi: 10.1016/j.asoc.2014.11.020 CrossRef Google Scholar
[14]	Zhao X W, Jin T, Chi H, Qu S. Modeling and simulation of the background light in underwater imaging under different illumination conditions. Acta Phys Sin 64, 104201 (2015). Google Scholar
[15]	He K M, Sun J, Tang X O. Single image haze removal using dark channel prior. IEEE Trans Pattern Anal Mach Intell 33, 2341-2353 (2011). doi: 10.1109/TPAMI.2010.168 CrossRef Google Scholar
[16]	Li C Y, Quo J C, Pang Y W, Chen S J, Wang J. Single underwater image restoration by blue-green channels dehazing and red channel correction. In Proceedings of 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (IEEE, 2016); http://doi.org/10.1109/ICASSP.2016.7471973. Google Scholar
[17]	Yang H Y, Chen P Y, Huang C C, Zhuang Y Z, Shiau Y H. Low complexity underwater image enhancement based on dark channel prior. In Proceedings of Second International Conference on Innovations in Bio-Inspired Computing and Applications (IEEE, 2011); http://doi.org/10.1109/IBICA.2011.9. Google Scholar
[18]	Drews P Jr, do Nascimento E, Moraes F, Botelho S, Campos M. Transmission estimation in underwater single images. In Proceedings of 2013 IEEE International Conference on Computer Vision Workshops (IEEE, 2013); http://doi.org/10.1109/ICCVW.2013.113. Google Scholar
[19]	Chiang J Y, Chen Y C. Underwater image enhancement by wavelength compensation and dehazing. IEEE Trans Image Process 21, 1756-1769 (2012). doi: 10.1109/TIP.2011.2179666 CrossRef Google Scholar
[20]	Yang M, Gong C L. Underwater image restoration by turbulence model based on image gradient distribution. In Proceedings of the 2nd International Conference on Uncertainty Reasoning and Knowledge Engineering (IEEE, 2012); http://doi.org/10.1109/URKE.2012.6319570. Google Scholar
[21]	Provenzi E, Gatta C, Fierro M, Rizzi A. A spatially variant white-patch and gray-world method for color image enhancement driven by local contrast. IEEE Trans Pattern Anal Mach Intell 30, 1757-1770 (2008). doi: 10.1109/TPAMI.2007.70827 CrossRef Google Scholar
[22]	He K M, Sun J, Tang X O. Guided image filtering. IEEE Trans Pattern Anal Mach Intell 35, 1397-1409 (2013). Google Scholar
[23]	Gould R W Jr, Arnone R A, Martinolich P M. Spectral dependence of the scattering coefficient in case 1 and case 2 waters. Appl Opt 38, 2377-2383 (1999). doi: 10.1364/AO.38.002377 CrossRef Google Scholar
[24]	Zhao X W, Jin T, Qu S. Deriving inherent optical properties from background color and underwater image enhancement. Ocean Eng 94, 163-172 (2015). Google Scholar