PolUR: Polarization Aware Multimodal Degradation Perception for Underwater Image Restoration
To overcome these limitations, we propose PolUR, a unified multimodal language-driven framework that jointly uses polarization and RGB information to identify degradation types and severity levels through structured semantic instructions and enhances joint restoration representations under the guidance of degradation-aware semantic features to improve restoration robustness under mixed degradations.
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Key points
- Recovering high-quality images from degraded underwater observations is crucial for underwater exploration.
- Polarization observations show promise as an effective source of complementary physical cues for recovering target radiance under clear-water conditions.
- We further construct a new benchmark named MixPolar, which contains underwater images exhibiting real-world degradations and their corresponding degradation descriptions.
- Extensive experiments demonstrate that PolUR effectively integrates polarization and RGB information, achieving competitive performance in degradation perception and image restoration, while also demonstrating generalization capability and utility for downstream tasks.
Sources (1)
- [1]PolUR: Polarization Aware Multimodal Degradation Perception for Underwater Image RestorationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 08:55 AM
To overcome these limitations, we propose PolUR, a unified multimodal language-driven framework that jointly uses polarization and RGB information to identify degradation types and severity levels through structured semantic instructions and enhances joint restoration representations under the guidance of degradation-aware semantic features to improve restoration robustness under mixed degradation
Recovering high-quality images from degraded underwater observations is crucial for underwater exploration.
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