Two-Stage Teacher-Student Reliable Prior Learning for Robust Underwater Image Enhancement
Underwater image enhancement (UIE) aims to recover clear images from observations affected by wavelength-dependent absorption, scattering, and spatially nonuniform degradation. Although existing generative methods can handle complex degradations, severe information loss may lead to semantic drift in the restored results. To address this issue, we propose RPL-UIE, a two-stage teacher--student framework for reliable prior learning. In the teacher stage, the network learns reliable and complementary spatial priors characterizing appearance and photometric properties from paired degraded and reference images. In the student stage, the network takes only degraded images as input and learns to ...
| Authors | Yifan Chen, Jiaming Liu, Ye Zheng et al. |
|---|---|
| Primary category | eess.IV |
| Published | 31 July 2026 |
| Last updated at source | 18 August 2026 |
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