Abstract
Underwater image restoration is still a challenging task until now, because underwater images are degenerated due to the complex underwater imaging environment and poor light condition. The image degeneration includes color distortion, low contrast, and blur. In this paper, we propose Underwater-GAN, a conditional generative adversarial network for underwater image restoration. In Underwater-GAN, we use Wasserstein GAN with gradient penalty term as the backbone network. We design the loss function as the sum of the loss of generative adversarial network and the perceptual loss. In the discriminator of Underwater-GAN, we use a convolution patchGAN classifier to learn a structural loss instead of the image-level loss or pixel-wise loss. Moreover, we construct an underwater image dataset by simulating to generate underwater images according to the underwater imaging model. We train our model with these simulated underwater dataset. The results of our experiments show that the proposed method produces better visual qualitative and quantitative indicators than existing methods.
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Hummel, R.: Image enhancement by histogram transformation. Comput. Graph. Image Process. 6, 184–195 (1977)
Liu, Y.C., Chan, W.H., Chen, Y.Q.: Automatic white balance for digital still camera. IEEE Trans. Consum. Electron. 41, 460–466 (1995)
Henke, B., Vahl, M., Zhou, Z.: Removing color cast of underwater images through non-constant color constancy hypothesis. In: IEEE International Symposium on Image and Signal Processing and Analysis (2013)
Galdran, A., Pardo, D., et al.: Automatic red-channel underwater image restoration. J. Vis. Commun. Image Represent. (JVCIR) 26, 132–145 (2014)
Li, C., Guo, J., Pang, Y., Chen, S., Wang, J.: Single underwater image restoration by blue-green channels dehazing and red channel correction. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2016)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)
Goodfellow, I., et al.: Generative adversarial nets. In: Conference on Neural Information Processing Systems (NIPS) (2014)
Mirza, M., Osindero, S.: Conditional generative adversarial nets. arXiv: 1411.1784
Ledig, C., Theis, L., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)
He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. (T-PAMI) 33, 2341–2353 (2011)
Jaffe, J.S.: Computer modeling and the design of optimal underwater imaging systems. IEEE J. Ocean. Eng. 15, 101–111 (1990)
Koschmieder, H.: Theorie der horizontalen Sichtweite. In: Beitrage zur Physik der freien. Atmosphare (1924)
Li, J., Skinner, K.A., et al.: WaterGAN: unsupervised generative network to enable real-time color correction of monocular underwater images. arXiv: 1701.07875
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.: Improved training of wasserstein GANs. arXiv: 1704.00028
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: CVPR (2017)
Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., Efros, A.A.: Context encoders: feature learning by inpainting. In: CVPR (2016)
Johnson, J., Alahi, A., Li, F.: Perceptual losses for real-time style transfer and super-resolution. In: European Conference on Computer Vision (ECCV) (2016)
Kupyn, O., Budzan, V., Mykhailych, M.: DeblurGAN: blind motion deblurring using conditional adversarial networks. arXiv:1711.07064
Gonzalez, R., Woods, R.: Digital Image Processing. Addison-Wesley Publishing Company, Boston (1992). Chapter 4
Johnson-Roberson, M., Bryson, M., et al.: High-resolution underwater robotic vision-based mapping and 3D reconstruction for archaeology. Field Robot. 34, 625–643 (2016)
Bekaert, P., Haber, T., et al.: Enhancing underwater images and videos by fusion. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2012)
Fabbri, C., Islam, M.J., et al.: Enhancing underwater imagery using generative adversarial networks. In: IEEE Conference on Robotics and Automation (ICRA) (2018)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (ICLR) (2015)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(11), 600–612 (2004)
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Yu, X., Qu, Y., Hong, M. (2019). Underwater-GAN: Underwater Image Restoration via Conditional Generative Adversarial Network. In: Zhang, Z., Suter, D., Tian, Y., Branzan Albu, A., Sidère, N., Jair Escalante, H. (eds) Pattern Recognition and Information Forensics. ICPR 2018. Lecture Notes in Computer Science(), vol 11188. Springer, Cham. https://doi.org/10.1007/978-3-030-05792-3_7
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DOI: https://doi.org/10.1007/978-3-030-05792-3_7
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