WaterMono: Teacher-Guided Anomaly Masking and Enhancement Boosting for Robust Underwater Self-Supervised Monocular Depth Estimation

Depth information serves as a crucial prerequisite for various visual tasks, whether on land or underwater. Recently, self-supervised methods have achieved remarkable performance on several terrestrial benchmarks despite the absence of depth annotations. However, in more challenging underwater scenarios, they encounter numerous brand-new obstacles such as the influence of marine life and the degradation of underwater images, which break the assumption of a static scene and bring low-quality images, respectively. Besides, the camera angles of underwater images are more diverse. Fortunately, we have discovered that knowledge distillation presents a promising approach to tackling these challenges. In this article, we propose WaterMono, a novel learning framework for depth estimation coupled with image enhancement. It incorporates the following key measures: 1) we present a teacher-guided anomaly masking (TGAM) to identify dynamic regions in the images; 2) we employ depth information combined with the underwater image formation model (UIFM) to generate enhanced images, which, in turn, contribute to the depth estimation task; and 3) we utilize a rotated distillation (RD) strategy to enhance the model’s rotational robustness. Comprehensive experiments demonstrate the effectiveness of our proposed method for both depth estimation and image enhancement. The source code and pretrained models are available at https://github.com/OUCVisionGroup/WaterMono.