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New method enhances underwater images using unstable label data

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new method called RQUL-UIE to improve underwater image enhancement by addressing issues with unstable label quality in training data. The approach uses a diffusion-based, self-supervised learning strategy that leverages semantic perception embeddings from a pre-trained diffusion model to assess label quality. This allows the system to effectively utilize even low-quality labels while preventing them from degrading performance, and a Fourier-based network further refines high-frequency components for superior restoration. AI

IMPACT Improves image restoration techniques, potentially benefiting applications in marine research and underwater exploration.

RANK_REASON The cluster contains a research paper detailing a novel method for image enhancement.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Haochen Hu, Yanrui Bin, Chih-yung Wen, Bing Wang ·

    RQUL-UIE: Revitalizing Quality-Unstable Labels for Underwater Image Enhancement via In-Dataset Self-Supervision

    arXiv:2606.06176v1 Announce Type: new Abstract: Underwater Image Enhancement (UIE) is essential for mitigating degradations caused by water medium. Although learning-based methods have advanced significantly, most rely on paired datasets with unstable label quality, which bottlen…

  2. arXiv cs.CV TIER_1 English(EN) · Bing Wang ·

    RQUL-UIE: Revitalizing Quality-Unstable Labels for Underwater Image Enhancement via In-Dataset Self-Supervision

    Underwater Image Enhancement (UIE) is essential for mitigating degradations caused by water medium. Although learning-based methods have advanced significantly, most rely on paired datasets with unstable label quality, which bottlenecks model performance. This paper proposes a di…

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