Unsupervised Underwater Image Enhancement Based on Disentangled Representations via Double-Order Contrastive Loss

Images captured in underwater environments often suffer from color distortion, low contrast, and reduced visual quality. Most existing methods solve underwater image enhancement (UIE) by applying supervised training on synthetic images or pseudo references. However, the synthetic paired data fail to accurately replicate real-world data due to the inherent differences, and the quantity and quality of pseudo references are limited, which seriously reduces the generalization ability and performance of the model when testing on real underwater images. In contrast, unsupervised-based method is not constrained by paired data, which is more robust and potentially more promising for practical applications. Nevertheless, existing unsupervised-based methods cannot effectively constrain the network to train a model that can adapt to various degradation. Inspired by the fact that people often resolve problems from opposing but complementary perspectives, we maintain that there is implicit cooperation between the removal and generation of water layers, as they can constrain and promote each other at the same time. Based on the above analysis, a new unsupervised-based UIE framework that jointly learns water layer generation and removal based on disentangled representations is proposed. Specifically, we propose a bidirectional disentangling network in which each unidirectional network contains a loop consisting of water layer removal and generation, and restricts the image to remain consistent after a loop. Meanwhile, a novel double-order contrastive loss is proposed to improve the ability of disentanglement by utilizing the joint implicit constraint of first-order features and second-order features. Extensive experimental results demonstrate that the model outperforms the state-of-the-art methods in both qualitative and quantitative evaluation with a relatively high processing speed. The experimental results of the ablation study demonstrate the usefulness of the various components.