  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.
