Underwater color constancy: enhancement of automatic live fish recognition

TLDR

The ACE model is a perceptual, unsupervised color equalization approach inspired by human visual adaptation, offering robustness and local filtering that improves underwater image quality. The study aims to advance color restoration for underwater images to mitigate strong, non‑uniform color casts. The method applies the ACE unsupervised color equalization algorithm to correct underwater image color. Preliminary results show that the restored images improve fish segmentation and feature extraction, yielding promising performance.

Abstract

We present in this paper some advances in color restoration of underwater images, especially with regard to the strong and non uniform color cast which is typical of underwater images. The proposed color correction method is based on ACE model, an unsupervised color equalization algorithm. ACE is a perceptual approach inspired by some adaptation mechanisms of the human visual system, in particular lightness constancy and color constancy. A perceptual approach presents a lot of advantages: it is unsupervised, robust and has local filtering properties, that lead to more effective results. The restored images give better results when displayed or processed (fish segmentation and feature extraction). The presented preliminary results are satisfying and promising.