Underwater Image Translation via Multi-Scale Generative Adversarial Network

Abstract

The role that underwater image translation plays assists in generating rare images for marine applications. However, such translation tasks are still challenging due to data lacking, insufficient feature extraction ability, and the loss of content details. To address these issues, we propose a novel multi-scale image translation model based on style-independent discriminators and attention modules (SID-AM-MSITM), which learns the mapping relationship between two unpaired images for translation. We introduce Convolution Block Attention Modules (CBAM) to the generators and discriminators of SID-AM-MSITM to improve its feature extraction ability. Moreover, we construct style-independent discriminators that enable the discriminant results of SID-AM-MSITM to be not affected by the style of images and retain content details. Through ablation experiments and comparative experiments, we demonstrate that attention modules and style-independent discriminators are introduced reasonably and SID-AM-MSITM performs better than multiple baseline methods.