An Image Synthesis Method Generating Underwater Images


  • Jarina Raihan Ahamed Faculty of Integrated Technologies, Universiti Brunei Darussalam, Brunei Darussalam
  • Pg Emeroylariffion Abas Faculty of Integrated Technologies, Universiti Brunei Darussalam, Brunei Darussalam
  • Liyanage Chandratilak De Silva Faculty of Integrated Technologies, Universiti Brunei Darussalam, Brunei Darussalam



image processing, image synthesis, Jerlov water types, underwater image analysis


The objective of this study is to convert normal aerial images into underwater images based on attenuation values for different water types by utilizing the image formation model (IFM) with Jerlov water types. Firstly, the depth values are derived from RGB-D images. If the depth information is not available, the values between 0.5 m to 10 m are chosen, and the transmission map is estimated by these values. Secondly, the statistical average background light values of Br = 0.6240, Bg = 0.805, and Bb = 0.7651 have been derived by analyzing 890 images using two methods, namely quad-tree decomposition and four-block division. Finally, the conversion of aerial-to-underwater images is done using the derived values, and the images are verified by computer simulation using MATLAB software. The result indicates that this method can easily generate underwater images from aerial images and makes it easier for the availability of ground truth.


D. W. Jung, S. M. Hong, J. H. Lee, H. J. Cho, H. S. Choi, and M. T. Vu, “A Study on Unmanned Surface Vehicle Combined with Remotely Operated Vehicle System,” Proceedings of Engineering and Technology Innovation, vol. 9, pp. 17-24, July 2018.

K. S. Nam, D. G. Lee, J. D. Ryu, and K. N. Ha, “The Basic Study of Underwater Robot Control for Over Actuated Systems,” Proceedings of Engineering and Technology Innovation, vol. 12, pp. 21-25, May 2019.

K. Kim and S. Kim, “Cross Layer Based Cooperative Communication Protocol for Improving Network Performance in Underwater Sensor Networks,” International Journal of Engineering and Technology Innovation, vol. 10, no. 3, pp. 200-210, June 2020.

M. Yang and A. Sowmya, “An Underwater Color Image Quality Evaluation Metric,” IEEE Transactions on Image Processing, vol. 24, no. 12, pp. 6062-6071, October 2015.

K. Panetta, C. Gao, and S. Agaian, “Human-Visual-System-Inspired Underwater Image Quality Measures,” IEEE Journal of Oceanic Engineering, vol. 41, no. 3, pp. 541-551, July 2016.

F. F. Koczy and N. G. Jerlov, Photographic Measurements of Daylight in Deep Water, Göteborg: Elanders boktr, 1951.

R. Hummel, “Image Enhancement by Histogram Transformation,” Computer Graphics and Image Processing, vol. 6, no. 2, pp. 184-195, April 1977.

K. Zuiderveld, “Contrast Limited Adaptive Histogram Equalization,” Graphics Gems IV, pp. 474-485, August 1994.

K. Iqbal, R. A. Salam, A. Osman, and A. Z. Talib, “Underwater Image Enhancement Using an Integrated Colour Model,” IAENG International Journal of Computer Science, vol. 34, no. 2, pp. 1-6, November 2007.

K. Iqbal, M. Odetayo, A. James, R. A. Salam, and A. Z. Talib, “Enhancing the Low Quality Images Using Unsupervised Colour Correction Method,” IEEE International Conference on Systems, Man, and Cybernetics, pp. 1703-1709, October 2010.

D. Huang, Y. Wang, W. Song, J. Sequeira, and S. Mavromatis, “Shallow-Water Image Enhancement Using Relative Global Histogram Stretching Based on Adaptive Parameter Acquisition,” International Conference on Multimedia Modelling, pp. 453-465, February 2018.

A. S. A. Ghani and N. A. M. Isa, “Underwater Image Quality Enhancement through Composition of Dual-Intensity Images and Rayleigh-Stretching,” IEEE 4th Conference on Consumer Electronics Berlin, pp. 757- 767, September 2014.

A. J. Raihan, P. E. Abas, and L. C. D. Silva, “Review of Underwater Image Restoration Algorithms,” IET Image Processing, vol. 13, no. 10, pp. 1587-1596, June 2019.

N. Carlevaris-Bianco, A. Mohan, and R. M. Eustice, “Initial Results in Underwater Single Image Dehazing,” Conference of OCEANS MTS/IEEE Seattle, pp. 1-8, September 2010.

K. He, J. Sun, and X. Tang, “Single Image Haze Removal Using Dark Channel Prior,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 12, pp. 2341-2353, December 2010.

L. Chao and M. Wang, “Removal of Water Scattering,” 2nd International Conference on Computer Engineering and Technology, pp. 1-10, April 2010.

P. Drews, E. Nascimento, F. Moraes, S. Botelho, and M. Campos, “Transmission Estimation in Underwater Single Images,” Proc. of IEEE International Conference on Computer Vision Workshops, pp. 825-830, December 2013.

H. Y. Yang, P. Y. Chen, C. C. Huang, Y. Z. Zhuang, and Y. H. Shiau, “Low Complexity Underwater Image Enhancement Based on Dark Channel Prior,” 2nd International Conference on Innovations in Bio-Inspired Computing and Applications, pp. 17-20, December 2011.

C. Li, J. Quo, Y. Pang, S. Chen, and J. Wang, “Single Underwater Image Restoration by Blue-Green Channels Dehazing and Red Channel Correction,” IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 1731-1735, March 2016.

J. Y. Chiang and Y. C. Chen, “Underwater Image Enhancement by Wavelength Compensation and Dehazing,” IEEE Transactions on Image Processing, vol. 21, no. 4, pp. 1756-1769, April 2012.

Y. T. Peng and P. C. Cosman, “Underwater Image Restoration Based on Image Blurriness and Light Absorption,” IEEE Transactions on Image Processing, vol. 26, no. 4, pp. 1579-1594, April 2017.

W. Song, Y. Wang, D. Huang, and D. Tjondronegoro, “A Rapid Scene Depth Estimation Model Based on Underwater Light Attenuation Prior for Underwater Image Restoration,” 19th Pacific-Rim Conference on Multimedia, pp. 678-688, September 2018.

X. Zhao, T. Jin, and S. Qu, “Deriving Inherent Optical Properties from Background Color and Underwater Image Enhancement,” Ocean Engineering, vol. 94, pp. 163-172, January 2015.

S. Anwar, C. Li, and F. Porikli, “Deep Underwater Image Enhancement,”, 10 July 2018.

X. Liu and B. M. Chen, “A Systematic Approach to Synthesize Underwater Images Benchmark Dataset and Beyond,” IEEE 15th International Conference on Control and Automation, pp. 1517-1522, July 2019.

C. Li, S. Anwar, and F. Porikli, “Underwater Scene Prior Inspired Deep Underwater Image and Video Enhancement,” Pattern Recognition, vol. 98, 107038, February 2020.

D. Akkaynak, T. Treibitz, T. Shlesinger, R. Tamir, Y. Loya, and D. Iluz, “What Is the Space of Attenuation Coefficients in Underwater Computer Vision?” Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4931-4940, November 2017.

M. G. Solonenko and C. D. Mobley, “Inherent Optical Properties of Jerlov Water Types,” Applied Optics, vol. 54, no. 17, pp. 5392-5401, June 2015.

C. Li and X. Zhang, “Underwater Image Restoration Based on Improved Background Light Estimation and Automatic White Balance,” 11th International Conference on Image and Signal Processing, Biomedical Engineering and Informatics (CISP-BMEI), pp. 1-5, October 2018.

C. Ancuti, C. O. Ancuti, T. Haber, and P. Bekaert, “Enhancing Underwater Images and Videos by Fusion,” IEEE Conference on Computer Vision and Pattern Recognition, pp. 81-88, June 2012.

A. J. Raihan, P. E. Abas, and L. C. D. Silva, “ Restoration of Underwater Images Using Depth and Transmission Map Estimation, with Attenuation Priors,” Ocean Systems Engineering, vol. 11, no. 4, pp. 331-351, November 2021.




How to Cite

J. R. Ahamed, P. E. Abas, and L. C. D. Silva, “An Image Synthesis Method Generating Underwater Images”, Adv. technol. innov., vol. 7, no. 3, pp. 195–205, Mar. 2022.