Underwater scenes intrinsically involve degradation problems owing to
heterogeneous ocean elements. Prevailing underwater image enhancement (UIE)
methods stick to straightforward feature modeling to learn the mapping
function, which leads to limited vision gain as it lacks more explicit physical
cues (e.g., depth). In this work, we investigate injecting the depth prior into
the deep UIE model for more precise scene enhancement capability. To this end,
we present a novel depth-guided perception UIE framework, dubbed underwater
variable zoom (UVZ). Specifically, UVZ resorts to a two-stage pipeline. First,
a depth estimation network is designed to generate critical depth maps,
combined with an auxiliary supervision network introduced to suppress
estimation differences during training. Second, UVZ parses near-far scenarios
by harnessing the predicted depth maps, enabling local and non-local perceiving
in different regions. Extensive experiments on five benchmark datasets
demonstrate that UVZ achieves superior visual gain and delivers promising
quantitative metrics. Besides, UVZ is confirmed to exhibit good generalization
in some visual tasks, especially in unusual lighting conditions. The code,
models and results are available at: this https URL

通过将深度先验注入到深度水下图像增强模型中，本论文提出了一种名为水下可变变焦（UVZ）的新型深度引导感知水下图像增强框架，该方法在不同区域实现了局部和非局部感知，并在多个基准数据集上取得了卓越的视觉增益和有希望的定量指标，特别是在异常光照条件下表现良好。