Numerical Simulation and Design of Hybrid Underwater Image Restoration and Enhancement with Deep Learning


  • Yogesh Kumar Gupta Assistant Professor, Computer Science, Banasthali Vidyapith Niwai (Raj)
  • Khushboo Saxena Research Scholar, Computer Science, Banasthali Vidyapith Niwai (Raj)


Mathematical model, Underwater Images, Deep Learning, Decision Tree, Convolutional Neural Network


Academicians from all over the world have been researching underwater images and the ability to capture crystal clear images for the past few years. Additionally, restoring the acquired images requires a laborious process in its entirety. The obtained underwater images have some flaws because of the scientific phenomena of absorption and scattering. These images suffer from colour distortion, blurriness, and low contrast effects, which are the main issues. For researchers in the field of image processing, overcoming these deficiencies is a herculean task. When light passes through water, its path is constrained. Pictures become submerged and turn greenish blue as they fall short on certain frequency parts in this case because the larger frequencies are influenced more than the more restricted ones. For instance, a picture taken at a depth of about 4-5 m underwater would require red frequency because the more extended frequency ranges of the apparent range are weaker first. Other frequency segments will start to lose significance with further increase. As a result, the pictures suffer from the negative effects of limited perceivability range, uneven lighting, and the presence of splendid antiquities. The proposed research work uses a deep learning model to improve the underwater images to get around this.


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Y. Rzhanov, L. M. Linnett, and R. Forbes, “Underwater video mosaicing for seabed mapping” in Proceedings 2000 International Conference on Image Processing, 10-13 September 2000.

M. Boudhane and B. Nsiri, “Underwater image processing method for fish localization and detection in submarine environment” in Journal of Visual Communication and Image Representation, vol. 39, pp. 226-238, August 2016.

M. Rajasekar, A. C. Aruldoss and M. Anto Bennet, “A novel method to detect corrosion in underwater infrastructure using an image processing” in ARPN Journal of Engineering and Applied Science, vol 13, Issue 7, pp. 2556-2561, April 2018.

A. Galdran, D. Pardo, A. Picon, & A. Alvarez-Gila, “Automatic red- channel underwater Image Restoration,” Journal of Visual Communication and Image Representation, vol 26, pp. 132-145, January 2015.

Ranadev, M. B. ., V. R. . Sheelavant, and R. L. . Chakrasali. “Predetermination of Performance Parameters of 3-Phase Induction Motor Using Numerical Technique Tools”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 10, no. 6, June 2022, pp. 63-69, doi:10.17762/ijritcc.v10i6.5628.

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, Dec. 2011.

H. Koschmieder “Theorie der horizontalen sichtweite” Beitr. Phys. Freien Atm. vol. 12 pp. 171-181 1924.

K. Iqbal, R. A. Salam, A. Osman & A. Z. Talib, “Underwater image enhancement using an integrated colour model,” IAENG International Journal of Computer Science. 34,2007.

R. Schettini and S. Corchs, “Underwater image processing; state of the art image restoration and image enhancement methods” EURASIP Journal on Advances in Signal Processing, February 2010, Article ID 746052.

P. Sahu, N. Gupta, and N. Sharma, “A survey on underwater image enhancement,” International Journal of Computer Applications, (0975- 48887) vol 87, February 2014.

Sai, M. P. ., V. A. . Rao, K. . Vani, and P. . Poul. “Prediction of Housing Price and Forest Cover Using Mosaics With Uncertain Satellite Imagery”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 10, no. 8, Aug. 2022, pp. 36-46, doi:10.17762/ijritcc.v10i8.5666.

H. Lu, Y. Li, Y. Zhang, M. Chen, S.Serikawa and H. Kim, “Underwater Optical Image Processing: a Comprehensive Review. Mobile Netw Appl 22, 1204–1211, 2011

Y. Schechner and N. Karpel, “Recovery of underwater visibility and structure by polarisation analysis” in IEEE Journal of Oceanic Engineering, vol 30, no. 3, pp. 570-587, July 2005.

C. O. Ancuti and C. Ancuti, “Single image dehazing by multi-scale fusion,” IEEE Transactions on Image Processing, vol. 22, no. 8, pp. 3271-3282, Aug. 2013.

C. O. Ancuti, C. Ancuti, C. De Vleeschouwer, and P. Bekaert, “Color balance and fusion for underwater image enhancement,” in IEEE Transactions on Image Processing, vol. 27, no. 1, pp. 379-393, Jan. 2018.

N. Carlevaris-Bianco, A. Mohan and R. M. Eustice, “Initial results in underwater single image dehazing,” OCEANS 2010 MTS/IEEE SEATTLE, Seattle, WA, 2010, pp. 1-8, December 2010.

C. Li, J. Quo, Y. Pang, S. Chen, and J. Wang, “Single underwater image restoration using blue-green channels dehazing and red channel correction” IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 1731-1735, May 2016.

Ahmed Cherif Megri, Sameer Hamoush, Ismail Zayd Megri, Yao Yu. (2021). Advanced Manufacturing Online STEM Education Pipeline for Early-College and High School Students. Journal of Online Engineering Education, 12(2), 01–06. Retrieved from

K. He, J. Sun, X. Tang, “Guided image filtering,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, pp. 1397-1409, June 2013. Pp 1731-1735, May 2016.

J. Chiang and Y.Chen, “Underwater image enhancement by wavelength compensation and dehazing”, IEEE Transactions on Image Processing, vol. 21, no. 4, pp. 1756-1769, April 2012.

R. Fisher, S. Perkins, A. Walker, E. Wolfart (2003), “Contrast stretching”, rbf/HIPR2/stretch.htm, (last accessed on 12 June 2020)

S. B. Borkar and S. V. Bonde, “Contrast enhancement and visibility restoration of underwater optical images using fusion,” International Journal of Intelligent Engineering and Systems, Vol.10, No.4, pp. 217- 225, 2017.

Chaudhary, D. S. . (2022). Analysis of Concept of Big Data Process, Strategies, Adoption and Implementation. International Journal on Future Revolution in Computer Science &Amp; Communication Engineering, 8(1), 05–08.

S. Borkar and S. V. Bonde, "Underwater image restoration using single color channel prior," 2016 International Conference on Signal and Information Processing (IConSIP), Vishnupuri, 2016, pp. 1-4, doi: 10.1109/ICONSIP.2016.7857488.

Dogiwal, S.R., Shishodia, Y.S., Upadhyaya, A., Ram, H. and Alaria, S.K., 2012. Image Preprocessing Methods in Image Recognition. International Journal of Computers and Distributed Systems, 1(3), pp.96-99.

Alaria, S.K., Sharma, V., Raj, A. and Kumar, V., 2022. Design Simulation and Assessment of Prediction of Mortality in Intensive Care Unit Using Intelligent Algorithms. Mathematical Statistician and Engineering Applications, 71(2), pp.355-367.

Dursun, M., & Goker, N. (2022). Evaluation of Project Management Methodologies Success Factors Using Fuzzy Cognitive Map Method: Waterfall, Agile, And Lean Six Sigma Cases. International Journal of Intelligent Systems and Applications in Engineering, 10(1), 35–43.

Alaria, S. K., Raj, A. ., Sharma, V. and Kumar, V. (2022) “Simulation and Analysis of Hand Gesture Recognition for Indian Sign Language using CNN”, International Journal on Recent and Innovation Trends in Computing and Communication, 10(4), pp. 10–14. doi: 10.17762/ijritcc.v10i4.5556.

Hybrid Model for Deep Learning




How to Cite

Y. K. . Gupta and K. . Saxena, “Numerical Simulation and Design of Hybrid Underwater Image Restoration and Enhancement with Deep Learning”, Int J Intell Syst Appl Eng, vol. 10, no. 1s, pp. 95–101, Oct. 2022.