Abstract
When shooting underwater, limited by different camera hardware and lighting, underwater images are generally of low quality and blurred details. Moreover, due to the different absorption rates of the R, G, and B three-color wavelengths, the color of underwater photos is distorted. This affects the feasibility of studying underwater images. The majority of currently available techniques for improving underwater photographs mainly solve the problem of overall denoising and brightness enhancement of underwater images while ignoring the edge details of the image. As a solution to the aforementioned issues, we suggest a method using a double extraction network structure model based on the Laplacian operator, which can dehaze underwater images more quickly. One is to use downsampling and upsampling to reduce the excessive distortion of the image during training, and the other is to use the Laplacian operator to reduce the blurring of the edge to enhance the clarity of the edge and use the blue‒green model for multiscale fusion. We adopted MSE loss and proved its superiority through experiments and index analysis by training images of different underwater scenes.
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The funding for this project came from the National Natural Science Foundation of China (Grant No. 62206274).
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Li, X., Yu, S., Gu, H., Tan, Y., Xing, L. (2023). Underwater Image Clearing Algorithm Based on the Laplacian Edge Detection Operator. In: Yadav, S., Kumar, H., Kankar, P.K., Dai, W., Huang, F. (eds) Proceedings of 2nd International Conference on Artificial Intelligence, Robotics, and Communication . ICAIRC 2022. Lecture Notes in Electrical Engineering, vol 1063. Springer, Singapore. https://doi.org/10.1007/978-981-99-4554-2_16
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DOI: https://doi.org/10.1007/978-981-99-4554-2_16
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