AI RESEARCH
Empowering Semantic-Sensitive Underwater Image Enhancement with VLM
arXiv CS.AI
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ArXi:2603.12773v1 Announce Type: cross In recent years, learning-based underwater image enhancement (UIE) techniques have rapidly evolved. However, distribution shifts between high-quality enhanced outputs and natural images can hinder semantic cue extraction for downstream vision tasks, thereby limiting the adaptability of existing enhancement models. To address this challenge, this work proposes a new learning mechanism that leverages Vision-Language Models (VLMs) to empower UIE models with semantic-sensitive capabilities.