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

Understand

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.