A Fusion Adversarial Underwater Image Enhancement Network with a Public Test Dataset

TL;DR

This paper introduces a new underwater image enhancement network using a fusion adversarial approach, evaluated on a novel public dataset U45, achieving superior results with faster processing and fewer parameters.

Contribution

The paper proposes a fusion adversarial network for underwater image enhancement and introduces the U45 dataset for standardized evaluation.

Findings

Outperforms state-of-the-art methods in qualitative and quantitative tests

Corrects color casts effectively and faster than existing methods

Ablation study confirms the effectiveness of each component

Abstract

Underwater image enhancement algorithms have attracted much attention in underwater vision task. However, these algorithms are mainly evaluated on different data sets and different metrics. In this paper, we set up an effective and pubic underwater test dataset named U45 including the color casts, low contrast and haze-like effects of underwater degradation and propose a fusion adversarial network for enhancing underwater images. Meanwhile, the well-designed the adversarial loss including Lgt loss and Lfe loss is presented to focus on image features of ground truth, and image features of the image enhanced by fusion enhance method, respectively. The proposed network corrects color casts effectively and owns faster testing time with fewer parameters. Experiment results on U45 dataset demonstrate that the proposed method achieves better or comparable performance than the other state-of-the-art methods in terms of qualitative and quantitative evaluations. Moreover, an ablation study demonstrates the contributions of each component, and the application test further shows the effectiveness of the enhanced images.