Marine snow, the floating particles in underwater images, severely degrades the visibility and performance of human and machine vision systems. This paper proposes a novel method to reduce the marine snow interference using deep learning techniques. We first synthesize realistic marine snow samples by training a Generative Adversarial Network (GAN) model and combine them with natural underwater images to create a paired dataset. We then train a U-Net model to perform marine snow removal as an image to image translation task. Our experiments show that the U-Net model can effectively remove both synthetic and natural marine snow with high accuracy, outperforming state-of-the-art methods such as the Median filter and its adaptive variant. We also demonstrate the robustness of our method by testing it on the MSRB dataset, which contains synthetic artifacts that our model has not seen during training. Our method is a practical and efficient solution for enhancing underwater images affected by marine snow.

通过使用深度学习技术，该研究提出了一种新方法来减少水下图像中的浮游生物干扰，通过训练生成对抗网络模型合成逼真的浮游生物样本，并将其与自然水下图像相结合创建配对数据集，然后使用U-Net模型进行图像翻译任务来去除浮游生物干扰。实验结果表明，该方法能够高精度地去除合成和自然的浮游生物，并且胜过中值滤波器及其自适应变体等最先进的方法。同时，通过在MSRB数据集上的测试，研究还展示了该方法的鲁棒性，该数据集包含了我们模型在训练过程中未曾见过的合成伪影。该方法是一种改善受浮游生物影响的水下图像的实用高效解决方案。

一种深度学习方法用于海洋雪合成与去除