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Modified Restoration Technique for Improved Visual Perception of Shallow Underwater Imagery


Affiliations
1 Underwater Acoustic Research Laboratory, Department of Electronics and Communication Engineering, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, Chennai 603 110, India
 

Images captured underwater often suffer from quality degradation such as low contrast, non-uniform illumi-nation, etc. due to the attenuation and backscattering of light by suspended underwater particles. To over-come this, restoration-cum-enhancement techniques are necessary. Here we present the modified under-water light attenuation prior (MULAP) model using supervised linear regression model to restore the degraded image. The image formation model (IFM)-based restoration depends on dual factors: back-ground light and transmission map.Initially, datasets are collected on the close-range point-of-interest. Then, experimental analyses are carried out for those images using the IFM-based methods. For the above techniques, both subjective analysis and objective analysis are done by considering dual metrics such as universal quality index and visual information fidelity factor. Finally, the proposed MULAP shows over-whelming qualitative and quantitative results among other state-of-the-art techniques.

Keywords

Image Formation Model, Restoration Techniques, Underwater Imagery, Visual Perception.
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  • Mangeruga, M., Cozza, M. and Bruno, F., Evaluation of underwater image enhancement algorithms under different environmental conditions. J. Mar. Sci. Eng., 2018, 6, 10.

  • Huang, D., Wang, Y., Song, W., Sequeira, J. and Mavromatis, S., Shallow-water image enhancement using relative global histogram stretching based on adaptive parameter acquisition. In Proceedings of International Conference on Multimedia Modeling, Springer, 2018.

  • Wei, S., Wang, Y., Huang, D., Liotta, A. and Perra, C., Enhancement of underwater images with statistical model of background light and optimization of transmission map. IEEE Trans. Broadcast., 2020, 66, 153–169.

  • Miao, Y., Hu, J., Li, C., Rohde, G., Du, Y. and Hu, K., An in-depth survey of underwater image enhancement and restoration. IEEE Access, 2019, 7, 123638–123657.

  • Islam, Md J., Xia, Y. and Sattar, J., Fast Underwater Image enhancement for improved visual perception. IEEE Robot. Automat. Lett., 2020, 5, 3227–3234.

  • Li, C., Guo, J. and Guo, C., Emerging from water: underwater image color correction based on weakly supervised color transfer. EEE Signal Proc. Lett., 2018, 25, 323–327.

  • Banerjee, J., Ray, R., Vadali, S. R. K., Shome, S. N. and Nandy, S., Real-time underwater image enhancement: an improved approach for imaging with AUV-150. Sadhana, 2016, 41, 225–238.

  • Wang, Y., Song, W., Fortino, G., Qi, L.-Z., Zhang, W. and Liotta, A., An experimental-based review of image enhancement and image restoration methods for underwater imaging. IEEE Access, 2019, 7, 140233–140251.

  • Dorothy, R., Joany, R. M., Joseph Rathish, R., Santhana Prabha, S. and Rajendran, S., Image enhancement by histogram equalization. Int. J. Nano. Corr. Sci. Eng., 2015, 2, 21-30.

  • Ma, J., Fan, X., Yang, S. X., Zhang, X. and Zhu, X., Contrast limited adaptive histogram equalization based fusion for underwater image enhancement. Int. J. Pattern Recognit. Artif. Intell., 2018, 32, 1854018.

  • Iqbal, K., Salam, R. A., Osman, A. and Talib, A. Z., Underwater image enhancement using an integrated colour model. IAENG Int. J. Comput. Sci., 2007, 34.

  • Iqbal, K., Odetayo, M., James, A., Salam, R. A. and 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.

  • Ghani, A., Abdul, S. and Isa, N. A. M., Underwater image quality enhancement through composition of dual-intensity images and Rayleigh-stretching. SpringerPlus, 2014, 3(1), 757.

  • Drews, P., Nascimento, E., Moraes, F., Botelho, S. and Campos, M., Transmission estimation in underwater single images. In Proceedings of the IEEE International Conference on Computer Vision Workshops, 2013.

  • Wen, H., Tian, Y., Huang, T. and Gao, W., Single underwater image enhancement with a new optical model. In 2013 IEEE International Symposium on Circuits and Systems (ISCAS2013), IEEE, 2013.

  • Li, C., Quo, J., Pang, Y., Chen, S. and 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 (ICASSP), IEEE, 2016.

  • Ebner, M., Color constancy based on local space average color. Mach. Vision Appl., 2009, 20, 283–301.

  • Peng, Y.-T. and Cosman, P. C., Underwater image restoration based on image blurriness and light absorption. IEEE Trans. Image Proc., 2017, 26, 1579–1594.

  • Song, W., Wang, Y., Huang, D. and Tjondronegoro, D., A rapid scene depth estimation model based on underwater light attenuation prior for underwater image restoration. In Proceedings of Pacific Rim Conference on Multimedia, Springer, 2018.

  • Parthasarathy, S. and Sankaran, P., An automated multi scale retinex with color restoration for image enhancement. In Proceedings of 2012 National Conference on Communications (NCC), IEEE, 2012.

  • Xiao, J., Hays, J., Ehinger, K. A., Oliva, A. and Torralba, A., Sun database: Large-scale scene recognition from abbey to zoo. In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, 2010.

  • Wang, Z. and Bovik, A. C., A universal image quality index. IEEE Signal Proc. Lett., 2002, 9, 81–84.

  • Wang, Z., Alan, B., Hamid, S. and Eero, S., Image quality assessment: From error visibility to structural similarity. IEEE Trans. Image Proc., 2004, 13, 600–612.


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  • Modified Restoration Technique for Improved Visual Perception of Shallow Underwater Imagery

Abstract Views: 225  |  PDF Views: 74

Authors

M. Dhana Lakshmi
Underwater Acoustic Research Laboratory, Department of Electronics and Communication Engineering, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, Chennai 603 110, India
S. Sakthivel Murugan
Underwater Acoustic Research Laboratory, Department of Electronics and Communication Engineering, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, Chennai 603 110, India

Abstract


Images captured underwater often suffer from quality degradation such as low contrast, non-uniform illumi-nation, etc. due to the attenuation and backscattering of light by suspended underwater particles. To over-come this, restoration-cum-enhancement techniques are necessary. Here we present the modified under-water light attenuation prior (MULAP) model using supervised linear regression model to restore the degraded image. The image formation model (IFM)-based restoration depends on dual factors: back-ground light and transmission map.Initially, datasets are collected on the close-range point-of-interest. Then, experimental analyses are carried out for those images using the IFM-based methods. For the above techniques, both subjective analysis and objective analysis are done by considering dual metrics such as universal quality index and visual information fidelity factor. Finally, the proposed MULAP shows over-whelming qualitative and quantitative results among other state-of-the-art techniques.

Keywords


Image Formation Model, Restoration Techniques, Underwater Imagery, Visual Perception.

References





DOI: https://doi.org/10.18520/cs%2Fv121%2Fi1%2F103-108