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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Notes
- 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.
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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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