Presentation of an optimal method to increase the quality of underwater imaging

Subject Areas : Journal of Radar and Optical Remote Sensing and GIS

Abbas Bashiri ¹ , Hasan HasaniMoghaddam ² , Adel Tabeshkar ³

1 - Instructor of Electronics Department of Imam Hossein University
2 - Remote sensing researcher of Imam Hossein University
3 - Master of Telecommunications, non-profit higher education institutions of Qom

Received: 2022-02-01 Accepted : 2022-06-02 Published : 2022-06-20

Keywords: algorithm, Underwater imaging, Spectral, Underwater target,

Abstract :

Most underwater intelligent vehicles and marine remote-control vehicles are equipped with optical cameras for underwater imaging. However, due to the properties of water and its impurity, the quality of the images taken by these imaging devices is not good enough, because the water weakens the light, and the deeper the water, the more the intensity of the light will decreases. Since different wavelengths show different behavior in the collision with the water column, processing and study of these wavelengths is very important to obtain the desired image. The spectral signature can be used for underwater applications. In this research, to increase the quality of underwater images, a new method has been introduced to improve image contrast. In this method, first, with structured lighting, different wavelengths are irradiated to the underwater target in a laboratory environment, then underwater images are processed by the proposed algorithm, and finally, a multispectral image is achieved by stacking images with different wavelengths. The results showed the relative superiority of the proposed method over other methods.

References:

Amer, K. O., Elbouz, M., Alfalou, A., Brosseau, C., & Hajjami, J. (2019). Enhancing underwater optical imaging by using a low-pass polarization filter. Optics Express, 27(2), 621. https://doi.org/10.1364/oe.27.000621

Bostater Jr, C. R., & Aziz, S. (2019). Water wave glint corrections, water depth, light attenuation, and WorldView-3 remote sensing algorithms for Indian River lagoon. In Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2019 (Vol. 11150, pp. 70-80. SPIE. https://doi.org/10.1117/12.2534093

Chen, Z., Yu, J., Zhang, A., & Song, S. (2015). Control system for long-range survey hybrid-driven underwater glider. In OCEANS 2015-Genova (pp. 1-16). IEEE. https://doi.org/10.1109/oceans-genova.2015.7271706

Doron, M., Babin, M., Mangin, A., & Hembise, O. (2007). Estimation of light penetration, and horizontal and vertical visibility in oceanic and coastal waters from surface reflectance. Journal of Geophysical Research, 112(C6). https://doi.org/10.1029/2006jc004007

Ghane Ezabadi, N., Azhdar, S., & Jamali, A. A. (2021). Analysis of dust changes using satellite images in Giovanni NASA and Sentinel in Google Earth Engine in western Iran. Journal of Nature and Spatial Sciences (JONASS), 1(1), 17-26.

Grasso, R. J., Odhner, J. E., Wikman, J. C., Skaluba, F. W., Dippel, G. F., McDaniel, R. V., ... & Seibel, W. (2005, October). A novel low-cost targeting system (LCTS) based upon a high-resolution 2D imaging laser radar. In Electro-Optical Remote Sensing (Vol. 5988, pp. 183-188). SPIE.

HasaniMoghaddam, H., Torahi, A. A., & Zeaiean, P. (2021). Fusion of Hyperspectral and High resolution imagery based on different level of HAAR DWT. Application of Geography information system and remote sensing in planning, 11(4), 7-17.

Lin, X. (2018). Adaptive Underwater Optical Wireless Sensor Network Using LED-Based Visible Light Communications. The Second International Conference on Materials Chemistry and Environmental Protection. https://doi.org/10.5220/0008187902220227

Liu, J., Guan, W., Wang, X., & Liu, J. (2018). Optical imaging study of underwater acousto-optical fusion imaging systems. In Proceedings of the 13th International Conference on Underwater Networks & Systems (pp. 1-5). https://doi.org/10.1145/3291940.3291982

Liu, B., Men, S., Ding, Z., Li, D., Zhao, Z., He, J., Ju, H., Shen, M., Yu, Q., & Liu, Z. (2023). Underwater Hyperspectral Imaging System with Liquid Lenses. Remote Sensing, 15(3), 544. https://doi.org/10.3390/rs15030544

Lee, K. E., Barber, L. B., & Schoenfuss, H. L. (2014). Spatial and Temporal Patterns of Endocrine Active Chemicals in Small Streams Indicate Differential Exposure to Aquatic Organisms. JAWRA Journal of the American Water Resources Association, 50(2), 401–419. Portico. https://doi.org/10.1111/jawr.12162

Ma, Y., Feng, X., Chao, L., Huang, D., Xia, Z., & Jiang, X. (2018). A New Database for Evaluating Underwater Image Processing Methods. In 2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA) (pp. 1-6). https://doi.org/10.1109/ipta.2018.8608131

Masoumi, H., Jamali, A. A., & Khabazi, M. (2014). Investigation of role of slope, aspect and geological formations of landslide occurrence using statistical methods and GIS in some watersheds in Chahar Mahal and Bakhtiari Province. J. Appl. Environ. Biol. Sci, 4(9), 121-129.

Moghimi, M. K., & Mohanna, F. (2021). Real-time underwater image enhancement: a systematic review. Journal of Real-Time Image Processing, 18(5), 1509–1525. https://doi.org/10.1007/s11554-020-01052-0

Monks, J. N. (2022). The Physical Principles of Light Propagation and Light–Matter Interactions. In Principles of Light Microscopy: From Basic to Advanced (pp. 1–16). https://doi.org/10.1007/978-3-031-04477-9_1

Shen, Y., Zhao, C., Liu, Y., Wang, S., & Huang, F. (2021). Underwater Optical Imaging: Key Technologies and Applications Review. IEEE Access, 9, 85500–85514. https://doi.org/10.1109/access.2021.3086820

Torahi, A. A., & Hasani Moghaddam, H. (2019). Determination of Flood extent using OLI data (case study: Dezful 2016 flooding). Environment and Water Engineering, 5(1), 24-35. https://doi.org/10.22034/jewe.2019.154927.1289

Vodopivec, M., Mandeljc, R., Makovec, T., Malej, A., & Kristan, M. (2018). Towards automated scyphistoma census in underwater imagery: A useful research and monitoring tool. Journal of Sea Research, 142, 147–156. https://doi.org/10.1016/j.seares.2018.09.014

VanMiddlesworth, M. M. A. (2014). Toward autonomous underwater mapping in partially structured 3D environments (Doctoral dissertation, Massachusetts Institute of Technology). https://doi.org/10.1575/1912/7136

Yin, X., Cheng, H., Yang, K., & Xia, M. (2020). Bayesian reconstruction method for underwater 3D range-gated imaging enhancement. Applied Optics, 59(2), 370-379.