Dual-branch Contrastive Learning for Image Enhancement of Underwater Internet of Things

Learning a single underwater image enhancement network from unpaired degraded and clear images is of practical interest. In reality, it is almost infeasible to obtain clear reference images corresponding to captured images in the IoT underwater. As a result, training enhancement networks in a supervised manner is challenging in the absence of such paired data. At the same time, existing methods are often insufficient to learn the semantic and texture knowledge inherent in clear images from limited data due to the significant variability between clear and degraded image domains. In response, we propose a two-branch contrast enhancement framework (DCE-Net) for unpaired underwater image enhancement, which learns mutual information between clear and degraded image domains from a limited amount of unpaired data. The proposed DCE-Net consists of a Cyclic Consistency Module (CCM) and a Contrast Enhancement Module (CEM). Specifically, the CCM is designed to guide feature transformation and latent feature learning between underwater clear images and underwater degraded images. the CEM is designed to constrain the consistency of semantic information between underwater clear images and underwater degraded images, encouraging better enhancement and improving image recovery quality. Extensive experiments are conducted using publicly available underwater datasets. The results demonstrate the effectiveness of the proposed method.

I. Introduction

Underwater Internet of Things(UIoT) has become an important tool for the exploitation and use of marine resources by utilising high performance Remotely Operated Vehi-cles(ROVs) [1] and Autonomous Underwater Vehicles(AUVs) [2] for monitoring and exploring the water environment. However, the adverse effects of the complex underwater environment often produce images with low contrast and blurred details, resulting in low performance for underwater computer vision tasks, which seriously affects the development of the UIoT. Therefore, it is crucial to develop an effective underwater image enhancement algorithm to generate high quality underwater clear images for the development of UIoT.