Abstract
Underwater image acquisition is susceptible to color distortion, insufficient exposure, and blurry details caused by the characteristics of underwater scenes. To address the above issues, this paper proposes an underwater image enhancement algorithm based on multi-scale underwater convolutional neural networks, combining convolutional neural networks with underwater scene physical imaging models. Firstly, improve the underwater imaging model to make the imaging process of underwater objects more natural and reasonable; Then design encoder, multi-scale convolution, and decoder network modules for estimating background scattered light, direct transmission component, and backward transmission component, respectively. At the same time, the overall network design combined with the residual network model introduced skip connections to reduce the complexity of network computation. Finally, the underwater image is reconstructed based on the improved underwater imaging model. The enhancement results of underwater images show that compared to a single scale network, the method proposed in this paper can effectively eliminate the impact of underwater environmental factors. While restoring image color, it also enhances texture details, and achieves better results in quantitative evaluation, providing a new approach to underwater image enhancement.
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Acknowledgements
This research is partially supported by the National Natural Science Foundation of China (Grant No. 62176149 and Grant No. 61673252).
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Luo, H., Liu, J., Zhang, X., Tu, D. (2023). Image Enhancement Algorithm Based on Multi-scale Convolution Neural Network. In: Yang, H., et al. Intelligent Robotics and Applications. ICIRA 2023. Lecture Notes in Computer Science(), vol 14270. Springer, Singapore. https://doi.org/10.1007/978-981-99-6492-5_52
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DOI: https://doi.org/10.1007/978-981-99-6492-5_52
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