Rethinking Underwater Image Enhancement: Learning through Text-Guided Degradation Curriculum

Key points

• Existing paired clean-degraded datasets have advanced learning-based underwater image enhancement, but they remain either limited in scale or difficult to acquire, and still fail to adequately cover the diverse degradations encountered in real-world underwater environments.1
• Such scarce and insufficient data ultimately restrict the generalization of underwater image enhancement models, highlighting the need for realistic and controllable degradation modeling to better capture real underwater conditions and improve enhancement robustness.1
• A Text-Conditioned Degradation Augmentation Pipeline uses a text-guided diffusion model to synthesize diverse and semantically consistent degraded samples, providing paired data to support the subsequent curriculum learning strategy.1
• The proposed Progressive Degradation Curriculum organizes training across multiple attributes and difficulty levels to promote balanced learning, and a Transmission-Guided One-Step Diffusion Model leverages this progressive process to better address the wide range of degradation severity and restoration ambiguity in underwater images.1

Sources (1)

• [1]Rethinking Underwater Image Enhancement: Learning through Text-Guided Degradation Curriculum
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 11:02 AM

  To this end, we propose a unified degradation-aware training framework for the underwater image enhancement task.

  Existing paired clean-degraded datasets have advanced learning-based underwater image enhancement, but they remain either limited in scale or difficult to acquire, and still fail to adequately cover the diverse degradations encountered in real-world underwater environments.

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