Underwater images are often affected by light refraction and absorption, reducing visibility and interfering with subsequent applications. Existing underwater image enhancement methods primarily focus on improving visual quality while overlooking practical implications. To strike a balance between visual quality and application, we propose a heuristic invertible network for underwater perception enhancement, dubbed HUPE, which enhances visual quality and demonstrates flexibility in handling other downstream tasks. Specifically, we introduced an information-preserving reversible transformation with embedded Fourier transform to establish a bidirectional mapping between underwater images and their clear images. Additionally, a heuristic prior is incorporated into the enhancement process to better capture scene information. To further bridge the feature gap between vision-based enhancement images and application-oriented images, a semantic collaborative learning module is applied in the joint optimization process of the visual enhancement task and the downstream task, which guides the proposed enhancement model to extract more task-oriented semantic features while obtaining visually pleasing images. Extensive experiments, both quantitative and qualitative, demonstrate the superiority of our HUPE over state-of-the-art methods. The source code is available at https://github.com/ZengxiZhang/HUPE.

本研究解决了现有水下图像增强方法只关注视觉质量但忽视实际应用的问题。提出了一种名为HUPE的启发式可逆网络，通过信息保持的可逆变换和语义协作学习模块，实现了水下图像与清晰图像之间的双向映射，并在美观的图像和任务导向的特征提取之间取得了良好的平衡。大量实验证明了HUPE在水下图像增强方面的优越性。

HUPE：基于语义协作学习的启发式水下感知增强