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
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 for tackling these
challenges. In this paper, we propose WaterMono, a novel framework for depth
estimation coupled with image enhancement. It incorporates the following key
measures: (1) We present a Teacher-Guided Anomaly Mask to identify dynamic
regions within the images; (2) We employ depth information combined with the
Underwater Image Formation Model to generate enhanced images, which in turn
contribute to the depth estimation task; and (3) We utilize a rotated
distillation 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
pre-trained models are available on the project home page:
this https URL

WaterMono 是一个用于深度估计和图像增强的新型框架，针对水下场景中的挑战，通过采用教师引导的异常掩模、深度信息结合水下图像成像模型生成增强图像以及旋转蒸馏策略来提高模型的鲁棒性，从而有效地实现了深度估计和图像增强任务。