Underwater Image Restoration and Enhancement via Residual Two-Fold Attention Networks

Abstract

Underwater images or videos are common but essential information carrier for observation, fishery industry and intelligent analysis system in underwater vehicles. But underwater images are usually suffering from more complex imaging interfering impacts. This paper describes a novel residual two-fold attention networks for underwater image restoration and enhancement to eliminate the interference of color deviation and noise at the same time. In our network framework, nonlocal attention and channel attention mechanisms are respectively embedded to mine and enhance more features. Quantitative and qualitative experiment data demonstrates that our proposed approach generates more visually appealing images, and also provides higher objective evaluation index score.