DOI QR코드

DOI QR Code

Blending of Contrast Enhancement Techniques for Underwater Images

  • Abin, Deepa (Pimpri Chinchwad College of Engineering) ;
  • Thepade, Sudeep D. (Pimpri Chinchwad College of Engineering) ;
  • Maitre, Amulya R. (Pimpri Chinchwad College of Engineering)
  • Received : 2021.12.05
  • Published : 2022.01.30

Abstract

Exploration has always been an instinct of humans, and underwater life is as fascinating as it seems. So, for studying flora and fauna below water, there is a need for high-quality images. However, the underwater images tend to be of impaired quality due to various factors, which calls for improved and enhanced underwater images. There are various Histogram Equalization (HE) based techniques which could aid in solving these issues. Classifying the HE methods broadly, there is Global Histogram Equalization (GHE), Mean Brightness Preserving HE (MBPHE), Bin Modified HE (BMHE), and Local HE (LHE). Each of these HE extensions have their own pros and cons and thus, by considering them we have considered BBHE, CLAHE, BPDHE, BPDFHE, and DSIHE enhancement algorithms, which are based on Mean Brightness Preserving HE and Local HE, for this study. The performance is evaluated with non-reference performance measures like Entropy, UCIQE, UICM, and UIQM. In this study, we apply the enhancement algorithms on 300 images from the UIEB benchmark dataset and then apply the techniques of cascading fusion on the best-performing algorithms.

Keywords

Acknowledgement

We thank the research community and our well wishers.

References

  1. R. C. Gonzalez and R. E. Woods, Digital image processing, 2nd ed. Boston, MA, USA: Prentice-Hall of India, 2002.
  2. Nicholas Sia Pik Kong, Haidi Ibrahim, and Seng Chun Hoo, " A Literature Review on Histogram Equalization and Its Variations for Digital Image Enhancement," International Journal of Innovation, Management and Technology vol. 4, no. 4, pp. 386-389, 2013.
  3. C. Li, C. Guo, W. Ren, R. Cong, J. Hou, S. Kwong, D. Tao, "An Underwater Image Enhancement Benchmark Dataset and Beyond," IEEE Trans. Image Process., vol. 29, pp.4376-4389, 2019. https://doi.org/10.1109/tip.2019.2955241
  4. Mohan, Sangeetha, and Philomina Simon. "Underwater Image Enhancement Based on HISTOGRAM Manipulation AND Multiscale Fusion." Procedia Computer Science, vol. 171, 2020, pp. 941-950., doi: 10.1016/j.procs.2020.04.102.
  5. Zhang, Yue, et al. "An Approach for Underwater Image Enhancement Based on Color Correction and Dehazing." International Journal of Advanced Robotic Systems, vol. 17, no. 5, Sept. 2020, p. 1729881420961643. SAGE Journals, doi:10.1177/1729881420961643.
  6. C. Cai, Y. Zhang, and T. Liu, "Underwater Image Processing System for Image Enhancement and Restoration," 2019 IEEE 11th International Conference on Communication Software and Networks (ICCSN), 2019, pp. 381-387, doi: 10.1109/ICCSN.2019.8905310.
  7. Chen, Xuelei, et al. "Underwater Image Enhancement Based on Deep Learning and Image Formation Model." ArXiv:2101.00991 [Eess], Jan. 2021. arXiv.org, http://arxiv.org/abs/2101.00991.
  8. D. Abin, B. Gulabani, C. Joshi, S. Damle and S. Gengaje, "Fusion based approach for Underwater Image Enhancement," 2021 International Conference on Communication information and Computing Technology (ICCICT), 2021, pp. 1-5, doi: 10.1109/ICCICT50803.2021.9510127.
  9. Abin, Deepa and Thepade, Sudeep D. and Gadde, Varun and Upganlawar, Vedant and Karpe, Sanskruti and Jadon, Vaishali Singh, Weighted Blending Fusion for Low Illumination Imagery Enhancement (July 9, 2021). Proceedings of the International Conference on IoT Based Control Networks & Intelligent Systems - ICICNIS 2021, Available at SSRN: https://ssrn.com/abstract=3883452 or http://dx.doi.org/10.2139/ssrn.3883452
  10. T. Treibitz and Y. Schechner, "Active polarization descattering," IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 3, pp. 385-399, 2009. https://doi.org/10.1109/TPAMI.2008.85
  11. C. Ancuti, C. O. Ancuti, and P. Bekaert, "Enhancing underwater images and videos by fusion," in Proc. of IEEE Int. Conf. Comput. Vis. Pattern Rec. (CVPR), 2012, pp. 81-88.
  12. X. Fu, Z. Fan, and M. Ling, "Two-step approach for single underwater image enhancement," in Symposium. of IEEE Intell. Signal Process. Commun. Syst., 2017, pp. 789-794.
  13. X. Fu, P. Zhang, Y. Huang, et al., "A retinex-based enhancing approach for single underwater image," in Proc. of IEEE Int. Conf. Image Process. (ICIP), 2014, pp. 4572-4576.
  14. A. Galdran, D. Pardo, and A. Picn, "Automatic Red-Channel underwater image restoration," J. Vis. Commu. and Image Repre., vol. 26, pp. 132- 145, 2015. https://doi.org/10.1016/j.jvcir.2014.11.006
  15. Yeong-Taeg Kim, "Contrast enhancement using brightness preserving bi-histogram equalization," in IEEE Transactions on Consumer Electronics, vol. 43, no. 1, pp. 1-8, Feb. 1997, doi: 10.1109/30.580378.
  16. Zuiderveld, Karel. "Contrast Limited Adaptive Histograph Equalization." Graphic Gems IV. San Diego: Academic Press Professional, 1994. 474-485.
  17. H. Ibrahim and N. S. Pik Kong, "Brightness Preserving Dynamic Histogram Equalization for Image Contrast Enhancement," in IEEE Transactions on Consumer Electronics, vol. 53, no. 4, pp. 1752-1758, Nov. 2007, doi: 10.1109/TCE.2007.4429280.
  18. D. Sheet, H. Garud, A. Suveer, M. Mahadevappa and J. Chatterjee, "Brightness preserving dynamic fuzzy histogram equalization," in IEEE Transactions on Consumer Electronics, vol. 56, no. 4, pp. 2475-2480, November 2010, doi: 10.1109/TCE.2010.5681130.
  19. Y. Wan, Q. Chen, B.M. Zhang, "Image enhancement based on equal area dualistic sub-image histogram equalization method," IEEE Trans. Consumer Electronics, Vol.45, pp.68-75, 1999. https://doi.org/10.1109/30.754419