Underwater images suffer from light refraction and absorption, which impairs visibility and interferes the subsequent applications. Existing underwater image enhancement methods mainly focus on image quality improvement, ignoring the effect on practice. To balance the visual quality and application, we propose a heuristic normalizing flow for detection-driven underwater image enhancement, dubbed WaterFlow. Specifically, we first develop an invertible mapping to achieve the translation between the degraded image and its clear counterpart. Considering the differentiability and interpretability, we incorporate the heuristic prior into the data-driven mapping procedure, where the ambient light and medium transmission coefficient benefit credible generation. Furthermore, we introduce a detection perception module to transmit the implicit semantic guidance into the enhancement procedure, where the enhanced images hold more detection-favorable features and are able to promote the detection performance. Extensive experiments prove the superiority of our WaterFlow, against state-of-the-art methods quantitatively and qualitatively.

翻译：摘要：水下图像因光折射与吸收而受损，导致可见度下降并干扰后续应用。现有水下图像增强方法主要聚焦于提升图像质量，却忽视了实际应用效果。为平衡视觉质量与实用性能，我们提出一种检测驱动的水下图像启发式归一化流增强方法，命名为WaterFlow。具体而言，我们首先构建可逆映射以实现退化图像与其清晰对应图像间的转换。考虑到可微性与可解释性，我们在数据驱动映射过程中融入启发式先验知识，其中环境光与介质透射系数有助于生成可信结果。此外，我们引入检测感知模块，将隐式语义引导信息传递至增强流程，使增强图像具有更有利于检测的特征，从而提升检测性能。大量实验结果表明，WaterFlow在定量与定性指标上均优于现有最先进方法。