mlsanityData to 14 Oct 2026

MetaWeather: Few-Shot Weather-Degraded Image Restoration

arXiv 2308.14334 ↗First posted 28 Aug 2023cs.CVcited by 2

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Abstract (arXiv)

Real-world weather conditions are intricate and often occur concurrently. However, most existing restoration approaches are limited in their applicability to specific weather conditions in training data and struggle to generalize to unseen weather types, including real-world weather conditions. To address this issue, we introduce MetaWeather, a universal approach that can handle diverse and novel weather conditions with a single unified model. Extending a powerful meta-learning framework, MetaWeather formulates the task of weather-degraded image restoration as a few-shot adaptation problem that predicts the degradation pattern of a query image, and learns to adapt to unseen weather conditions through a novel spatial-channel matching algorithm. Experimental results on the BID Task II.A, SPA-Data, and RealSnow datasets demonstrate that the proposed method can adapt to unseen weather conditions, significantly outperforming the state-of-the-art multi-weather image restoration methods.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 34 of its 52 references that are in the map. First posted on arXiv 28 Aug 2023.

Builds on

34 of 52 references.

References · 34

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22 more
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Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025OneForecast: A Universal Framework for Global and Regional Weather Forecastingcited by 34
  2. 2025Clear Nights Ahead: Towards Multi-Weather Nighttime Image Restorationcited by 2