  The goal of this work is to apply a denoising image transformer to remove the
distortion from underwater images and compare it with other similar approaches.
Automatic restoration of underwater images plays an important role since it
allows to increase the quality of the images, without the need for more
expensive equipment. This is a critical example of the important role of the
machine learning algorithms to support marine exploration and monitoring,
reducing the need for human intervention like the manual processing of the
images, thus saving time, effort, and cost. This paper is the first application
of the image transformer-based approach called "Pre-Trained Image Processing
Transformer" to underwater images. This approach is tested on the UFO-120
dataset, containing 1500 images with the corresponding clean images.
