Abstract
This paper addresses the problem of jointly haze detection and color correction from a single underwater image. We present a framework based on stacked conditional Generative adversarial networks (GAN) to learn the mapping between the underwater images and the air images in an end-to-end fashion. The proposed architecture can be divided into two components, i.e., haze detection sub-network and color correction sub-network, each with a generator and a discriminator. Specifically, a underwater image is fed into the first generator to produce a hazing detection mask. Then, the underwater image along with the predicted mask go through the second generator to correct the color of the underwater image. Experimental results show the advantages of our proposed method over several state-of-the-art methods on publicly available synthetic and real underwater datasets.
This work was supported by National Natural Science Foundation of China (NSFC) under Grant 61702078, 61772106, and by the Fundamental Research Funds for the Central Universities.
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- 1.
Note that, Gray World algorithm aims at correcting the color, while Non-local Image Dehazing can be recast as a post processing to deblur the corrected image. The combination of both algorithms can achieve a relatively high performance.
- 2.
There is no in-air ground truth for comparison, so we just show the visual comparison, and give some explanations.
References
Iqbal, K., Salam, R.A., Osman, M., Talib, A.Z., et al.: Underwater image enhancement using an integrated colour model. IAENG Int. J. Comput. Sci. 32(2), 239–244 (2007)
Provenzi, E., Fierro, M., Rizzi, A.: A spatially variant white-patch and gray-world method for color image enhancement driven by local contrast. IEEE Trans. Pattern Anal. Mach. Intell. 30(10), 1757–1770 (2008)
Ancuti, C., Ancuti, C.O., Haber, T., Bekaert, P.: Enhancing underwater images and videos by fusion. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 81–88. IEEE (2012)
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), 56645677 (2016)
Schechner, Y.Y., Averbuch, Y.: Regularized image recovery in scattering media. IEEE Trans. Pattern Anal. Mach. Intell. 29(9) (2007)
Chiang, J.Y., Chen, Y.-C.: Underwater image enhancement by wavelength compensation and dehazing. IEEE Trans. Image Process. 21(4), 1756–1769 (2012)
Zhang, S., Zhang, J., Fang, S., Cao, Y.: Underwater stereo image enhancement using a new physical model. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 5422–5426. IEEE (2014)
Berman, D., Treibitz, T., Avidan, S.: Non-local image dehazing. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 27–30 June 2016 (2016)
Zhang, H., Patel, V.M.: Densely connected pyramid dehazing network. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 18–22 July 2018 (2018)
Shin, Y.S., Cho, Y., Pandey, G., et al.: Estimation of ambient light and transmission map with common convolutional architecture. In: Oceans, pp. 1–7. IEEE (2016)
Wang, Y., Zhang, J., Cao, Y., et al.: A deep CNN method for underwater image enhancement. In: IEEE International Conference on Image Processing, pp. 1382–1386. IEEE (2017)
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, 387–394 (2018)
Goodfellow, I.J., et al.: Generative adversarial networks (2014)
Li, C., Guo, J., Guo, C.: Emerging from water: underwater image color correction based on weakly supervised color transfer. IEEE Signal Process. Lett. PP(99), 1 (2017)
Chen, X., Yu, J., Kong, S., et al.: Towards quality advancement of underwater machine vision with generative adversarial networks (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
Silberman, N., Hoiem, D., Kohli, P., Fergus, R.: Indoor segmentation and support inference from RGBD images. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7576, pp. 746–760. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33715-4_54
Chen, Q., Xu, J., Koltun, V.: Fast image processing with fully-convolutional networks. In: ICCV (2017)
Zhu, J.-Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: ICCV (2017)
Isola, P., Zhu, J.-Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. arXiv:1611.07004
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Ye, X., Xu, H., Ji, X., Xu, R. (2018). Underwater Image Enhancement Using Stacked Generative Adversarial Networks. In: Hong, R., Cheng, WH., Yamasaki, T., Wang, M., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2018. PCM 2018. Lecture Notes in Computer Science(), vol 11166. Springer, Cham. https://doi.org/10.1007/978-3-030-00764-5_47
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