Acknowledgement
This study was supported in part by research grants from Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1D1A1B07049296), and by the project titled 'Development of the support vessel and systems for the offshore field test and evaluation of offshore equipment', funded by the Ministry of Oceans and Fisheries (MOF), Korea.
References
- Achantay, R., Hemamiz, S., Estraday, F., & Susstrunk, S. (2009). Frequency Tuned Salient Region Detection. Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA, 1597-1604. https://doi.org/10.1109/CVPR.2009.5206596
- Ancuti, C.O., Ancuti, C., Vleeschouwer, C.D., & Bekaert, P. (2017). Color Balance and Fusion for Underwater Image Enhancement. IEEE Transactions on Image Processing, 27(1), 379-393. https://doi.org/10.1109/TIP.2017.2759252
- Anwar, S., Li, C., & Porikli, F. (2018). Deep Underwater Image enhancement. arXiv:1807.03528. Retrieved from https://arxiv.org/abs/1807.03528
- Anwar S. & Li, C. (2019). Diving Deeper into Underwater Image Enhancement: A Survey. arXiv:1907.07863. Retrieved 2019 from https://arxiv.org/abs/1907.07863
- Arjovsky, M., Chintala, S., & Bottou, L. (2017). Wasserstein GAN. arXiv:1701.07875. Retrieved from https://arxiv.org/abs/1701.07875
- Arnold-Bos, A., Malkasse, J.-P., & Kervern, G. (2005). A Preprocessing Framework for Automatic Underwater Images Denoising. Proceeding of European Conference on Propagation and Systems, Brest, France, 1-8. Retrieved from https://hal.archives-ouvertes.fr/hal-00494314
- Bazeille, S., Quidu, I., Jaulin, L., & Malkasse, J.-P. (2006). Automatic Underwater Image Pre-processing. Proceeding of Caracterisation Du Milieu Marin, 16-19. Retrieved from https://hal.archivesouvertes.fr/hal-00504893/
- Berman, D., Levy, D., Avidan, S., & Treibitz, T. (2018). Underwater single Image Color Restoration Using Haze-lines and a New Quantitative Dataset. arXiv:1811.01343. Retrieved from https://arxiv.org/abs/1811.01343
- Cao, K., Peng, Y., & Cosman, P. (2018). Underwater Image Restoration Using Deep Networks to Estimate Background Light and Scene Depth. Proceeding of IEEE Southwest Symposium on Image Analysis and Interpretation, Las Vegas, NV, USA, 1-4. https://doi.org/10.1109/SSIAI.2018.8470347
- Carlevaris-Bianco, N., Mohan, A., & Eustice, R.M. (2010). Initial Results in Underwater Single Image Dehazing. Proceeding of Oceans 2010 MTS/IEEE Seattle, WA, USA, 1-8. https://doi.org/10.1109/OCEANS.2010.5664428
- Cho, Y., Jeong, J., Kim A. (2018). Model-assisted Multiband Fusion for Single Image Enhancement and Applications to Robot Vision. IEEE Robotics and Automation Letters, 30(4), 2822-2829. https://doi.org/10.1109/LRA.2018.2843127
- Crete-Roffet, F., Dolmière, T., Ladret, P., & Nicolas, M. (2007). The Blur Effect: Perception and Estimation with a New No-Reference Perceptual Blur Metric. Proceeding SPIE Electronic Imaging Symposium Conf Human Vision and Electronic Imaging, San Jose, USA, EI6492-16. Retrieved from https://hal.archivesouvertes.fr/hal-00232709
- Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009). Imagenet: A Large-scale Hierarchical Image Database. Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA, 248-255. https://doi.org/10.1109/CVPR.2009.5206848
- Drews, P., Nascimento, E., Moraes, F., Botelho, S., & Campos, M. (2013). Transmission Estimation in Underwater Single Images. Proceeding of IEEE International Conference on Computer Vision Workshops, Sydney, NSW, Australia, 825-830. https://doi.org/10.1109/ICCVW.2013.113
- Fabbri, C., Islam, M.J., & Sattar, J. (2018). Enhancing Underwater Imagery Using Generative Adversarial Networks. Proceeding of IEEE International Conference on Robotics and Automation, Brisbane, QLD, Australia, 7159-7165. https://doi.org/10.1109/ICRA.2018.8460552
- Fattal, R. (2008). Single Image Dehazing. ACM Transactions on Graphics, 27(3), 1-9. https://doi.org/10.1145/1360612.1360671
- Ghani, A.S.A., & Isa, N.A.M. (2014). Underwater Image Quality Enhancement Trough Composition of Dual-intensity Images and Rayleigh-stretching. SpringerPlus, 3, 757. https://doi.org/10.1186/2193-1801-3-757
- Ghani, A.S.A., & Isa, N.A.M. (2015). Underwater Image Quality Eenhancement Through Integrated Color Model with Rayleigh Distribution. Applied Soft Computing, 27, 219-230. https://doi.org/10.1016/j.asoc.2014.11.020
- Guo, Y., Li, H., & Zhuang, P. (2019). Underwater Image Enhancement Using a Multiscale Dense Generative Adversarial Network. IEEE Journal of Oceanic Engineering, 45(3), 862-870. https://doi.org/10.1109/JOE.2019.2911447
- He, K., Sun, J., & Tang, X. (2011). Single Image Haze Removal Using Dark Channel Prior. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(12), 2341-2353. https://doi.org/10.1109/TPAMI.2010.168
- Hou, M., Liu, R., Fan, X., & Luo, Z. (2018). Joint Residual Learning for Underwater Image Enhancement. Proceeding of IEEE International Conference on Image Processing, Athens, Greece, 4043-4047. https://doi.org/10.1109/ICIP.2018.8451209
- Isola, P., Zhu, J., Zhou, T., & Efros, A.A. (2017). Image-to-Image Translation with Conditional Adversarial Networks. arXiv: 1611.07004. Retrieved from https://arxiv.org/abs/1611.07004
- Jian S., & Wen, W. (2017). Study on Underwater Image Denoising Agorithm Based on Wavelet Transform. Journal of Physics: Conference Series, 806, 012006. Retrieved from https://iopscience.iop.org/article/10.1088/1742-6596/806/1/012006
- Khan, A., Ali, S.S.A., Malik, A., Anwer, A., & Meriaudeau, F. (2016). Underwater Image Enhancement by Wavelet Based Fusion. Proceeding of IEEE International Conference on Underwater System Technology: Theory and Applications, Penang, Malaysia, 83-88. https://10.1109/USYS.2016.7893927
- Li, W.J., Gu, B., Huang, J.T., Wang, S.Y., & Wang, M.H. (2012). Single Image Visibility Enhancement in Gradient Domain. IET Image Processing, 6(5), 589-595. https://doi.org//10.1049/iet-ipr.2010. 0574
- Li, C., Guo, C., & Guo, J. (2018a). Emerging from Water: Underwater Image Color Correction Based on Weakly Supervised Color Transfer. IEEE Signal Processing Letters, 25(3), 232-327. https://doi.org/10.1109/LSP.2018.2792050
- Li, J., Skinner, K., Eustice, R., & Johnson-Roberson, M. (2018b). Watergan: Unsupervised Generative Network to Enable Real-time Color Correction of Monocular Underwater Images. IEEE Robotics and Automation Letters, 3(1), 387-394. https://doi.org/10.1109/LRA.2017.2730363.
- Li, C., Guo, C., Ren, W., Cong, R., Hou, J., & Kwong, S. (2019a). An Underwater Image Enhancement Dataset and Beyond. arXiv:1901.05495. Retrieved from https://arxiv.org/abs/1901.05495
- Li, H., Li, J., & Wang, W. (2019b). A Fusion Adversarial Underwater Image Enhancement Network with a Public Test Dataset. arXiv:1906.06819. Retrieved from https://arxiv.org/abs/1906.06819
- Luo, M., Fang, Y., & Ge, Y. (2019). An Effective Underwater Image Enhancement Method Based on CLAHE-HF. Journal of Physics: Conference Series, 1237(3), 032009. Retrieved from https://iopscience.iop.org/article/10.1088/1742-6596/1237/3/032009
- Mathieu, M., Couprie, C., & LeCun, Y. (2015). Deep Multi-Scale Video Prediction Beyond Mean Square Error. arXiv:1511.05440. Retrieved from https://arxiv.org/abs/1511.05440
- Mi, Z., Zhou, H., Zheng, Y., & Wang, M. (2016). Single Image Dehazing via Multiscale Gradient Domain Contrast Enhancement. IET Image Processing, 10(3), 206-214. https://doi.org/10.1049/iet-ipr.2015.0112
- Mobley, C. (1994). Light and Water: Radiative Transfer in Natural Waters. San Diego:Academic Press.
- Panetta, K., Agaian, S., Zhou, Y., & Wharton, E.J. (2011). Parameterized Logarithmic Framework for Image Enhancement. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 41(2), 460-473. https://doi.org/10.1109/TSMCB.2010.2058847
- Panetta, K., Gao, C., & Agaian, S. (2016). Human-Visual-SystemInspired Underwater Image Quality Measures. IEEE Journal of Oceanic Engineeringm, 41(3), 541-551. https://doi.org/10.1109/JOE.2015.2469915
- Park, E.P., & Sim, J. (2017). Gradient-based Contrast Enhancement and Color Correction for Underwater Images. Proceeding of Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, Kuala Lumpur, Malaysia, 1444-1447. https://doi.org/10.1109/APSIPA.2017.8282259
- Peng, Y., & Cosman, P.C. (2017). Underwater Image Restoration Based on Image Blurriness and Light Absorption. IEEE Transactions on Image Processing, 26(4), 1579-1594. https://doi.org/10.1109/TIP.2017.2663846
- Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional Networks for Biomedical Image Segmentation. arXiv: 1505.04597. Retrieved from https://arxiv.org/abs/1505.04597
- Roser, M., Dunbabin, M., & Geiger, A. (2014). Simultaneous Underwater Visibility Assessment, Enhancement and Improved Stereo. Proceeding of IEEE International Conference on Robotics and Automation, Hong Kong, China, 3840-3847. https://doi.org/10.1109/ICRA.2014.6907416
- Simchony, T., Chellappa R., & Shao, M. (1990). Direct Analytical Methods for Solving Poisson Equations in Computer Vision Problems. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(5), 435-446. https://doi.org/10.1109/34.55103
- Sun, X., Liu, L., Li, Q., Dong, J., Lima, E., & Yin, R. (2018). Deep Pixel to Pixel Network for Underwater Image Enhancement and Restoration. IET Image Processing, 13(3), 469-474. https://doi.org/10.1049/iet-ipr.2018.5237
- Tarel, J., & Hautière, N. (2009). Fast Visibility Restoration from a Single Color or Gray Level Image. Proceeding of IEEE International Conference on Computer Vision, Kyoto, Japan, 2201-2208. https://doi.org/10.1109/ICCV.2009.5459251
- Treibitz, T., & Schechner, Y.Y. (2012). Turbid Scene Enhancement Using Multi-Directional Illumination Fusion. IEEE Transactions on Image Processing, 21(11), 4662-4667. https://doi.org/10.1109/TIP.2012.2208978
- Uplavikar, P., Wu, Z., & Wang, Z. (2019). All-in-One Underwater Image Enhancement Using Domain-Adversarial Learning. arXiv: 1905.13342. Retrieved from https://arxiv.org/abs/1905.13342
- Vincent, L. (1993). Morphological Grayscale Reconstruction in Image Analysis: Applications and Efficient Algorithms. IEEE Transactions on Image Processing, 2(2), 176-201. https://doi.org/10.1109/83.217222
- Wang, Y., Ding, X., Wang, R., Zhang, J., & Fu, X. (2017a). Fusion-Based Underwater Image Enhancement by Wavelet Decomposition. Proceeding of IEEE International Conference on Industrial Technology, Toronto, ON, Canada, 1013-1018. https://doi.org/10.1109/ICIT.2017.7915500
- Wang, Y., Zhang, J., Cao, Y., & Wang, Z. (2017b). A Deep CNN Method for Underwater Image Enhancement. Proceeding of IEEE International Conference on Image Processing, Beijing, China, 1382-1386. https://doi.org/10.1109/ICIP.2017.8296508
- Wang, Y., Song, W., Fortino, G., Qi, L.-Z., Zhang, W., & Liotta, A. (2019). An Experimental-Based Review of Image Enhancement and Image Restoration Methods for Underwater Imaging. IEEE Access, 7, 140233-140251. https://doi.org/10.1109/ACCESS.2019.2932130
- Yang, M., & Sowmya, A. (2015). An Underwater Color Image Quality Evaluation Metric. IEEE Transactions on Image Processing, 24(12), 6062-6071. https://doi.org/10.1109/TIP.2015.2491020
- Yang, M., Hu, J., Li, C., Rohde, G., Du, Y., & Hu, K. (2019). An In-Depth Survey of Underwater Image Enhancement and Restoration. IEEE Access, 7, 123638-123657. https://doi.org/10.1109/ACCESS.2019.2932611
- Zetian, M., Zheng, L., Yafei, W., Xianping, F., & Zhengyu, C. (2018). Multiscale Gradient Domain Underwater Image Enhancement. Proceeding of 2018 Oceans - MTS/IEEE Kobe Techno-Oceans, Kobe, Japan, 1-5. https://doi.org/10.1109/OCEANSKOBE.2018.8559180
- Zhao, X., Tao, J., & Song, Q. (2015). Deriving Inherent Optical Properties from Background Color and Underwater Image Enhancement. Ocean Engineering, 94, 163-172. https://doi.org/10.1016/j.oceaneng.2014.11.036
- Zhang, S., Zhang, J., Fang, S., & Cao, Y. (2014). Underwater Stereo Image Enhancement Using a New Physical Model. Proceeding of IEEE International Conference on Image Processing, Paris, France, 5422-5426. https://doi.org/10.1109/ICIP.2014.7026097
- Zhu, J., Park, T., Isola, P., & Efros, A.A. (2018). Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks. arXiv:1703.10593. Retrieved from https://arxiv.org/abs/1703.10593