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 image enhancement based on red channel weighted compensation and gamma correction model
1. Maccarone A, Mccarthy A, Ren X, Warburton RE, Wallace AM et al. Underwater depth imaging using time-correlated sin-gle-photon counting. Opt Express 23, 33911–33926 (2015).
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).
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).
4. Yang M, Sowmya A. An underwater color image quality evaluation metric. IEEE Trans Image Process 24, 6062–6071 (2015).
5. Serikawa S, Lu H M. Underwater image dehazing using joint trilateral filter. Comput Electr Eng 40, 41–50 (2014).
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).
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).
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).
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).
10. Zhang S, Wang T, Dong J Y, Yu H. Underwater image en-hancement via extended multi-scale Retinex. Neurocomputing 245, 1–9 (2017).
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).
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);
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).
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).
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).
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.
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.
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);
19. Chiang J Y, Chen Y C. Underwater image enhancement by wavelength compensation and dehazing. IEEE Trans Image Process 21, 1756–1769 (2012).
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);
21. Provenzi E, Gatta C, Fierro M, Rizzi A. A spatially variant white-patch and gray-world method for color image enhance-ment driven by local contrast. IEEE Trans Pattern Anal Mach Intell 30, 1757–70 (2008).
22. He K M, Sun J, Tang X O. Guided image filtering. IEEE Trans Pattern Anal Mach Intell 35, 1397–1409 (2013).
23. Gould R W Jr, Arnone R A, Martinolich P M. Spectral depend-ence of the scattering coefficient in case 1 and case 2 waters. Appl Opt 38, 2377–2383 (1999).
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).
the National Key Scientific Equipment Development Project of China (ZDYZ2013-2), the National High-Tech R&D Program of China (G128201-G158201, G128603-G158603) and the Natural Science Foundation of China (11704382), Outstanding Youth Fund of Sichuan Province (2012JQ0012)
引用本文： 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-Electronic Advances 1, 180024