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Research questionHow can underwater image enhancement train effectively when paired labels vary in quality?Paired underwater images may be matched with reference labels that differ substantially in restoration quality. Treating all labels as equally reliable can introduce misleading supervision and limit enhancement performance.
AI
Computer Vision
Diffusion Models
Image & Video Processing
Machine Learning
Latest papersRecent research connected to this question, newest first.RQUL-UIE: Revitalizing Quality-Unstable Labels for Underwater Image Enhancement via In-Dataset Self-SupervisionThe source concerns learning-based underwater image enhancement using paired datasets. It evaluates a diffusion-based in-dataset self-supervised strategy that estimates label quality, applies level-wise supervision, and uses Fourier-based refinement; the reported evidence is limited to the paper’s stated evaluations.research paper · Sep 4, 2026
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