Abstract
The interference of suspended particles causes the problems of color distortion, haze effect and visibility reduction in complex underwater environment. However, existing methods for enhancement often result in overexposure of the low contrast area or distortion of the seriously turbid area. The main factor is the diversity of the underwater images. In this paper, we propose a novel underwater imaging model which can recover different kinds of underwater images, especially the image from the murky water. Specifically, we develop a reverse dark channel prior algorithm which can separate the image with large degradation by setting a maximum filter dark channel threshold. Using the algorithm, we can calculate the adaptive transmissivity and restore the blurred images. The restored image will be sent into an end-to-end generative model for coloring transformation and enhancement. Furthermore, we build an edge deepening module for edge recovery. Comprehensive experimental evaluations show that our method performs promising on image enhancement for different kinds of turbid water. Compared with other previous methods, our method is more universal and more suitable for practical applications.
This work was supported by the National Natural Science Foundation of China (No. 61971388, U1706218, 41576011, L1824025), Key Research and Development Program of Shandong Province (No. GG201703140154), and Major Program of Natural Science Foundation of Shandong Province (No. ZR2018ZB0852).
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Li, C., Anwar, S.: Underwater scene prior inspired deep underwater image and video enhancement. Pattern Recogn. 98, 107038 (2019)
He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2011)
Serikawa, S., Lu, H.: Underwater image dehazing using joint trilateral filter. Comput. Electr. Eng. 40(1), 41–50 (2014)
Aiping, Y., Chang, Q., Jian, W., Liyun, Z.: Underwater image visibility restoration based on underwater imaging model. J. Electron. Inf. Technol. 40, 298–305 (2018)
Drews, P.L.J., Nascimento, E.R., Botelho, S.S.C., Campos, M.F.M.: Underwater depth estimation and image restoration based on single images. IEEE Comput. Graph. Appl. 36(2), 24–35 (2016)
Peng, Y.T., Cao, K., Cosman, P.C.: Generalization of the dark channel prior for single image restoration. IEEE Trans. Image Process. 27(6), 2856–2868 (2018)
Carlevaris-Bianco, N., Mohan, A., Eustice, R.M.: Initial results in underwater single image dehazing. In: OCEANS 2010 MTS/IEEE SEATTLE, pp. 1–8 (2010)
McCartney, E.: Scattering phenomena (book reviews: optics of the atmosphere scattering by molecules and particles). Science 196, 1084–1085 (1977)
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: IEEE International Conference on Computer Vision (ICCV), pp. 2242–2251 (2017)
Yi, Z., Zhang, H., Tan, P., Gong, M.: DualGAN: unsupervised dual learning for image-to-image translation. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2868–2876 (2017)
Kim, T.: Learning to discover cross-domain relations with generative adversarial networks. In: Proceedings of the 34th International Conference on Machine Learning, PMLR, vol. 70, pp. 1857–1865 (2017)
Liu, T.Y.: Conditional image-to-image translation. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5524–5532 (2018)
Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: Computer Vision and Pattern Recognition (CVPR), pp. 4681–4690 (2016)
Wang, X., et al.: ESRGAN: enhanced super-resolution generative adversarial networks. In: Leal-Taixé, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11133, pp. 63–79. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11021-5_5
Li, J., Skinner, K.A., Eustice, R.M., Johnson-Roberson, M.: WaterGAN: unsupervised generative network to enable real-time color correction of monocular underwater images. IEEE Robot. Autom. Lett. 3(1), 387–394 (2017)
Sun, X., Shi, J., Liu, L., Dong, J., Plant, C., Wang, X., Zhou, H.: Transferring deep knowledge for object recognition in low-quality underwater videos. Neurocomputing 275, 897–908 (2017)
Fabbri, C., Islam, M.J., Sattar, J.: Enhancing underwater imagery using generative adversarial networks. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, 2018, pp. 7159–7165 (2018)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, vol. 9351, pp. 234–241 (2015)
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.: Image-to-image translation with conditional adversarial networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5967–5976, July 2017
Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein generative adversarial networks. In: Proceedings of the 34th International Conference on Machine Learning (PMLR), vol. 70, pp. 214–223, January 2017
Wang, N., Zhou, Y., Han, F., Zhu, H., Zheng, Y.: UWGAN: underwater GAN for real-world underwater color restoration and dehazing. In: ICLR 2020 Conference Blind Submission (2020). https://openreview.net/forum?id=HkgMxkHtPH
Sun, X., Liu, L., Li, Q., Dong, J., Lima, E., Yin, R.: Deep pixel-to-pixel network for underwater image enhancement and restoration. IET Image Process. 13(3), 469–474 (2019)
Chen, Y., Lai, Y.K., Liu, Y.J.: CartoonGAN: generative adversarial networks for photo cartoonization. In: IEEE/CVF Conference on Computer Vision Pattern Recognition, pp. 9465–9474 (2018)
Silberman, N., Hoiem, D., Kohli, P., Fergus, R.: Indoor segmentation and support inference from RGBD images. In: Proceedings of the 12th European conference on Computer Vision - Volume Part V, pp. 746–760 (2012)
Uplavikar, P., Wu, Z., Wang, Z.: All-in-one underwater image enhancement using domain-adversarial learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 1–8 (2019)
Galdran, A., Pardo, D., Picon, A., Alvarez-Gila, A.: Automatic red-channel underwater image restoration. J. Vis. Commun. Image Representation 26, 132–145 (2015)
Li, C.Y., Guo, J.C., Cong, R.M., Pang, Y.W., Wang, B.: Underwater image enhancement by dehazing with minimum information loss and histogram distribution prior. IEEE Trans. Image Process. 25(12), 5664–5677 (2016)
Peng, Y.T., Cosman, P.C.: Underwater image restoration based on image blurriness and light absorption. IEEE Trans. Image Process. 26(4), 1579–1594 (2017)
Ancuti, C., Codruta, A., Haber, T., Bekaert, P.: Enhancing underwater images and videos by fusion. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 81–88, June 2012
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Shen, Y., Zhao, H., Sun, X., Zhang, Y., Dong, J. (2020). Underwater Enhancement Model via Reverse Dark Channel Prior. In: Peng, Y., et al. Pattern Recognition and Computer Vision. PRCV 2020. Lecture Notes in Computer Science(), vol 12305. Springer, Cham. https://doi.org/10.1007/978-3-030-60633-6_37
Download citation
DOI: https://doi.org/10.1007/978-3-030-60633-6_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60632-9
Online ISBN: 978-3-030-60633-6
eBook Packages: Computer ScienceComputer Science (R0)