  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: https://github.com/WindySprint/UVZ.
