Underwater Image Enhancement based on Deep Learning and Image Formation Model

TL;DR

This paper introduces a deep learning-based underwater image enhancement method that improves color, contrast, and detail while being faster than existing techniques, aiding underwater robotic perception.

Contribution

It proposes a novel underwater image enhancement algorithm combining deep learning with an image formation model, addressing environmental distortions effectively.

Findings

Improves PSNR and SSIM metrics significantly

Enhances color richness and detail clarity

Faster computation speed than existing methods

Abstract

Underwater robots play an important role in oceanic geological exploration, resource exploitation, ecological research, and other fields. However, the visual perception of underwater robots is affected by various environmental factors. The main challenge now is that images captured by underwater robots are color-distorted. The hue of underwater images tends to be close to green and blue. In addition, the contrast is low and the details are fuzzy. In this paper, a new underwater image enhancement algorithm based on deep learning and image formation model is proposed. Experimental results show that the advantages of the proposed method are that it eliminates the influence of underwater environmental factors, enriches the color, enhances details, achieves higher scores in PSNR and SSIM metrics, and helps feature key-point point matching get better results. Another significant advantage is that its computation speed is much faster than other methods.