Joint-ID: Transformer-Based Joint Image Enhancement and Depth Estimation for Underwater Environments

     Underwater imaging is a challenging task due to factors such as scattering, absorption, and turbulence, which degrade image quality and limit visibility. In this article, we propose a novel approach for enhancing underwater images that leverages the benefits of joint learning for simultaneous image enhancement and depth estimation. We introduce Joint-ID, a transformer-based neural network that can obtain high-perceptual image quality and depth information from raw underwater images. Our approach formulates a multimodal objective function that addresses invalid depth, lack of sharpness, and image degradation based on color and local texture. We design an end-to-end training pipeline that enables joint restoration and depth estimation in a shared hierarchical feature space. In addition, we propose a synthetic dataset with various distortions and scene depths for multitask learning. 

     We evaluate Joint-ID on synthetic and standard datasets, as well as real underwater images with diverse spectra and harsh turbidity, demonstrating its effectiveness for underwater image enhancement (UIE) and depth estimation. Our proposed method has the potential to improve the visual perception of underwater environments and benefit applications such as oceanography and underwater robotics.