A Fusion Adversarial Underwater Image Enhancement Network with a Public Test Dataset
This work addresses the need for standardized evaluation in underwater vision tasks, though it is incremental as it builds on existing adversarial methods.
The authors tackled the problem of inconsistent evaluation in underwater image enhancement by creating a public test dataset (U45) and proposing a fusion adversarial network that effectively corrects color casts with faster testing time and fewer parameters, achieving better or comparable performance to state-of-the-art methods.
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.