![]() Li, C., Guo, C., Ren, W., et al.: An underwater image enhancement benchmark dataset and beyond. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. ![]() 6723–6732 (2018)Īkkaynak, D., Treibitz, T.: Sea-thru: A Method For Removing Water From Underwater Images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 30(2), 228–242 (2007)Īkkaynak, D., Treibitz, T.: A Revised underwater image formation model. Levin, A., Lischinski, D., Weiss, Y.: A closed-form solution to natural image matting. He, K., Sun, J., Tang, X.: Guided image filtering. Galdran, A., Pardo, D., Picón, A., et al.: Automatic red-channel underwater image restoration. In: The IEEE International Conference on Computer Vision Workshops. D, Moraes, F., et al.: Transmission estimation in underwater single images. Han, M., Lyu, Z., Qiu, T., et al.: A review on intelligence dehazing and color restoration for underwater images. Ren, W., Pan, J., Zhang, H., et al.: Single image dehazing via multi-scale convolutional neural networks with holistic edges. He, K., Sun, J., Fellow, et al.: Single image haze removal using dark channel prior. Xie, H., Liang, J., Wang, Z., et al.: Scanning imaging restoration of moving or dynamically deforming objects. Raveendran, S., Patil, M.D., Birajdar, G.K.: Underwater image enhancement: a comprehensive review, recent trends, challenges and applications. Wang, Y., Song, W., Fortino, G., et al.: An experimental-based review of image enhancement and image restoration methods for underwater imaging. Finally, both qualitative and quantitative experimental results show that the proposed method can produce better restoration results in different underwater scenes compared to other state-of-the-art underwater image restoration methods. Secondly, the alternating direction method of multipliers and the histogram displacement in the Lab color space are used to improve the uniform brightness and to correct the color distortion of the restored underwater images. First of all, the estimated transmission map by image blurriness is adopted in the image formation model to remove the haze of underwater images. To overcome these problems, an adaptive color correction method is proposed for underwater single image haze removal. The exponential attenuation phenomenon in the underwater light propagation process causes the low contrast, color distortion, and blurred edges problems of underwater images and consequently limits the application of the vision-based underwater technology. The issue of underwater image haze removal is investigated in this paper.
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