Abstract
In this paper, we propose three methods for removing floating particles from underwater images. The first two methods are based on Generative Adversarial Networks (GANs). The first method uses CycleGAN which can be trained with an unpaired dataset, and the second method uses pix2pixHD that is trained with a paired dataset created by adding artificial particles to underwater images. The third method consists of two-step process – particle detection and image inpainting. For particle detection, an image segmentation neural network U-Net is trained by using underwater images added with artificial particles. Using the output of U-Net, the particle regions are repaired by an image inpainting network Partial Convolutions. The experimental results showed that the methods using GANs were able to remove floating particles, but the resolution became lower than that of the original images. On the other hand, the results of the method using U-Net and Partial Convolutions showed that it is capable of accurate detection and removal of floating particles without loss of resolution.
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
Lee, H.S., Moon, S.W., Eom, I.K.: Underwater image enhancement using successive color correction and superpixel dark channel prior. Symmetry 12(8), 1220 (2020)
Yeh, C.H., Huang, C.H., Lin, C.H.: Deep learning underwater image color correction and contrast enhancement based on hue preservation. In: 2019 IEEE Underwater Technology (UT), pp. 1–6 (2019)
Li, C., et al.: An underwater image enhancement benchmark dataset and beyond. IEEE Trans. Image Process. 29, 4376–4389 (2019)
Zhang, S., Wang, T., Dong, J., Yu, H.: Underwater image enhancement via extended multi-scale Retinex. Neurocomputing, 245, 1–9 (2017)
Zhang, M., Peng, J.: Underwater image restoration based on a new underwater image formation model. IEEE Access, 58634–58644 (2018)
Li, C., Guo, J., Chen, S., Tang, Y., Pang, Y., Wang, J.: Underwater image restoration based on minimum information loss principle and optical properties of underwater imaging. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 1993–1997 (2016)
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)
Hou, G., Li, J., Wang, G., Pan, Z., Zhao, X.: Underwater image dehazing and denoising via curvature variation regularization. Multimedia Tools Appl. 79(27), 20199–20219 (2020)
Hao, Z., You, S., Li, Y., Li, K., Lu, F.: Learning from synthetic photorealistic raindrop for single image raindrop removal. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, p. 0 (2019)
Luo, W., Lai, J., Xie, X.: Weakly supervised learning for raindrop removal on a single image. IEEE Trans. Circuits Syst. Video Technol. (2020)
Lin, J., Dai, L.: X-net for single image raindrop removal. In: 2020 IEEE International Conference on Image Processing (ICIP), pp. 1003–1007 (2020)
Li, Z., et al.: Single image snow removal via composition generative adversarial networks. IEEE Access 7, 25016–25025 (2019)
Huang, S.C., Jaw, D.W., Chen, B.H., Kuo, S.Y.: Single image snow removal using sparse representation and particle swarm optimizer. ACM Transa. Intell. Syst. Technol. (TIST) 11(2), 1–15 (2020)
Patel, K.F., Tatariw, C., MacRae, J.D., Ohno, T., Nelson, S.J., Fernandez, I.J.: Soil carbon and nitrogen responses to snow removal and concrete frost in a northern coniferous forest. Can. J. Soil Sci. 98(3), 436–447 (2018)
Jiang, Q., Chen, Y., Wang, G., Ji, T.: A novel deep neural network for noise removal from underwater image. Signal Process. Image Commun. 87, 115921 (2020)
Koziarski, M., Cyganek, B.: Marine snow removal using a fully convolutional 3D neural network combined with an adaptive median filter. In: Zhang, Z., Suter, D., Tian, Y., Branzan Albu, A., Sidère, N., Jair Escalante, H. (eds.) ICPR 2018. LNCS, vol. 11188, pp. 16–25. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-05792-3_2
Zhu, J.Y., Park, T., Isola, P., Efros, A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223–2232 (2018)
Wang, T.C., Liu, M.Y., Zhu, J.Y., Tao, A., Kautz, J., Catanzaro, B.: High-resolution image synthesis and semantic manipulation with conditional GANs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8798–8807 (2018)
Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Liu, G., Reda, F.A., Shih, K.J., Wang, T.-C., Tao, A., Catanzaro, B.: Image inpainting for irregular holes using partial convolutions. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11215, pp. 89–105. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01252-6_6
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Li, L., Komuro, T., Enomoto, K., Toda, M. (2021). Removal of Floating Particles from Underwater Images Using Image Transformation Networks. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12662. Springer, Cham. https://doi.org/10.1007/978-3-030-68790-8_32
Download citation
DOI: https://doi.org/10.1007/978-3-030-68790-8_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-68789-2
Online ISBN: 978-3-030-68790-8
eBook Packages: Computer ScienceComputer Science (R0)
