Underwater Image Enhancement Using Pre-trained Transformer

TL;DR

This paper introduces a novel application of a pre-trained image transformer for underwater image enhancement, demonstrating its effectiveness in restoring image quality and supporting marine exploration with reduced manual effort.

Contribution

It is the first to apply a transformer-based approach to underwater image restoration, showing promising results on a standard dataset.

Findings

Effective removal of underwater distortions

Improved image quality compared to traditional methods

Potential to support marine research and monitoring

Abstract

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.