Underwater imaging in turbid environments: generation model, analysis, and verification

References

• Armstrong RA, Pizarro O, Roman C. Underwater robotic technology for imaging mesophotic coral ecosystems. In: Loya Y, Puglise K, Bridge T, editors. Mesophotic coral ecosystems. Cham: Springer; 2019. p. 973–988.
   Google Scholar
• Peng YT, Cao K, Cosman PC. Generalization of the dark channel prior for single image restoration. IEEE Trans Image Process. 2018;27(6):2856–2868.
   PubMed Web of Science ®Google Scholar
• Chang HH, Cheng CY, Sung CC. Single underwater image restoration based on depth estimation and transmission compensation. IEEE J Oceanic Eng. 2018;44(4):1130–1149.
   Google Scholar
• Zhou Y, Wu Q, Yan K, et al. Underwater image restoration using color-line model. IEEE Trans Circuits Syst Video Technol. 2018;29(3):907–911.
   Google Scholar
• Xie HL, Peng GH, Wang F, et al. Underwater image restoration based on background light estimation and dark channel prior. Acta Opt Sin. 2018;38(01):18–27.
   Google Scholar
• Akkaynak D, Treibitz T. “Sea-thru: A method for removing water from underwater images,” Proceedings of the IEEE conference on computer vision and pattern recognition, 2019, pp. 1682-1691.
   Google Scholar
• Lee HS, Moon SW, Eom IK. Underwater image enhancement using successive color correction and Superpixel dark channel prior. Symmetry (Basel). 2020;12(8):1220.
   Google Scholar
• Song W, Wang Y, Huang D, et al. Enhancement of underwater images with statistical model of background light and optimization of transmission map. IEEE Trans Broadcast. 2020;66(1):153–169.
   Google Scholar
• Liang Z, Wang Y, Ding X, et al. Single underwater image enhancement by attenuation Map Guided color correction and detail preserved dehazing. Neurocomputing. 2020;425; doi:https://doi.org/10.1016/j.neucom.2020.03.091.
   Google Scholar
• Li C, Anwar S, Porikli F. Underwater scene prior inspired deep underwater image and video enhancement. Pattern Recognit. 2020;98:107038.
   Google Scholar
• Cao K, Peng YT, Cosman PC. Underwater image restoration using deep networks to estimate background light and scene depth, 2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI). IEEE, 2018, pp. 1-4.
   Google Scholar
• Dudhane A, Hambarde P, Patil P, et al. Deep underwater image restoration and beyond. IEEE Signal Process Lett. 2020;27:675–679.
   Google Scholar
• Zong X, Chen Z, Wang D. Local-CycleGAN: a general end-to-end network for visual enhancement in complex deep-water environment. Appl Intell. 2020;51(4):1–12. doi:https://doi.org/10.1007/s10489-020-01931-w.
   Google Scholar
• Lu H, Li Y, Uemura T, et al. Low illumination underwater light field images reconstruction using deep convolutional neural networks. Future Gener Comput Syst. 2018;82:142–148.
   Web of Science ®Google Scholar
• Deng Z. “Research on the Key Problems of 3D Environment Reconstruction for Turbid Water,” M.S. Zhejiang University, China, 2019.
   Google Scholar
• Zhao X, Jin T, Qu S. Deriving inherent optical properties from background color and underwater image enhancement. Ocean Eng. 2015;94:163–172.
   Google Scholar
• Gall MP, Davies-Colley RJ, Merrilees RA. Exceptional visual clarity and optical purity in a sub-alpine lake. Limnol Oceanogr. 2013;58(2):443–451.
   Google Scholar
• Hammer UT. “Saline lake ecosystems of the world,” Springer Science & Business Media, 1986.
   Google Scholar
• Akkaynak D, Treibitz T. “A revised underwater image formation model,” Proceedings of the IEEE conference on computer vision and pattern recognition. 2018, pp. 6723-6732.
   Google Scholar
• Berman D, Treibitz T, Avidan S. “Diving into haze-lines: Color restoration of underwater images,” Proc. British Machine Vision Conference (BMVC). 2017, vol. 1, no. 2.
   Google Scholar
• Chiang JY, Chen YC. Underwater image enhancement by wavelength compensation and dehazing. IEEE Trans Image Process. 2011;21(4):1756–1769.
   PubMedGoogle Scholar
• Lu H, Li Y, Zhang L, et al. Contrast enhancement for images in turbid water. JOSA A. 2015;32(5):886–893.
   Google Scholar
• Lu H, Li Y, Nakashima S, et al. Turbidity underwater image restoration using spectral properties and light compensation. IEICE Trans Inf Syst. 2016;99(1):219–227.
   Google Scholar
• McGlamery BL. A computer model for underwater camera systems. Ocean Optics VI. Int Soc Opt Photonics. 1980;208:221–231.
   Google Scholar
• Jaffe JS. Computer modeling and the design of optimal underwater imaging systems. IEEE J Oceanic Eng. 1990;15(2):101–111.
   Google Scholar
• Nayar SK, Narasimhan SG. Vision in bad weather, Proceedings of the Seventh IEEE International Conference on Computer Vision. IEEE, 1999, 2: 820-827.
   Google Scholar
• Narasimhan SG, Nayar SK. Contrast restoration of weather degraded images. IEEE Trans Pattern Anal Mach Intell. 2003;25(6):713–724.
   Web of Science ®Google Scholar
• Narasimhan SG, Nayar SK. Vision and the atmosphere. Int J Comput Vision. 2002;48(3):233–254.
   Web of Science ®Google Scholar
• Narasimhan SG, Nayar SK. “Removing weather effects from monochrome images,” Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001. IEEE, 2001, vol. 2, pp. II-II.
   Google Scholar
• He K, Sun J, Tang X. Single image haze removal using dark channel prior. IEEE Trans Pattern Anal Mach Intell. 2010;33(12):2341–2353.
   PubMed Web of Science ®Google Scholar
• Zhu Q, Mai J, Shao L. A fast single image haze removal algorithm using color attenuation prior. IEEE Trans Image Process. 2015;24(11):3522–3533.
   PubMed Web of Science ®Google Scholar
• He K, Sun J, Tang X. “Guided image filtering,” European conference on computer vision. Springer, Berlin, Heidelberg, 2010, pp. 1-14.
   Google Scholar
• Yang S, Chen Z, Feng Z, et al. Underwater image enhancement using scene depth-based adaptive background light estimation and dark channel prior algorithms. IEEE Access. 2019;7:165318–165327.
   Google Scholar
• Kansal I, Kasana S S. Weighted image de-fogging using luminance dark prior. J Mod Opt. 2017;64(19):2023–2034.
   Web of Science ®Google Scholar
• Babaee M, Negahdaripour S. “Improved range estimation and underwater image enhancement under turbidity by opti-acoustic stereo imaging,” OCEANS 2015-Genova. IEEE, 2015, pp. 1-7.
   Google Scholar
• Wang Z, Bovik AC, Sheikh HR, et al. Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process. 2004;13(4):600–612.
   PubMed Web of Science ®Google Scholar
• Wang Y, Liu H, Chau L P. Single underwater image restoration using adaptive attenuation-curve prior. IEEE Trans Circuits Syst I Regul Pap. 2017;65(3):992–1002.
   Google Scholar
• Li C, Guo C, Ren W. An underwater image enhancement benchmark dataset and beyond. IEEE Trans Image Process. 2019;29:4376–4389.
   Google Scholar
• Mittal A, Moorthy AK, Bovik AC. No-Reference image quality Assessment in the Spatial domain. IEEE Trans Image Process. December 2012;21(12):4695–4708.
   PubMed Web of Science ®Google Scholar
• Mittal A, Moorthy AK, Bovik AC. Referenceless Image Spatial Quality Evaluation Engine. Presentation at the 45th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, November 2011.
   Google Scholar
• Mittal A, Soundararajan R, Bovik AC. Making a completely Blind image quality analyzer. IEEE Signal Process Lett. March 2013;22(3):209–212.
   Google Scholar
• Venkatanath N, Praneeth D, Chandrasekhar BM, et al. “Blind Image Quality Evaluation Using Perception Based Features”, In Proceedings of the 21st National Conference on Communications (NCC). Piscataway, NJ: IEEE, 2015.
   Google Scholar
• Sheikh HR, Wang Z, Cormack L, et al. LIVE Image Quality Assessment Database Release 2, https://live.ece.utexas.edu/research/quality.
   Google Scholar