الملخص
Adverse weather image restoration aims to recover images degraded by rain, haze, snow, and other weather-induced artifacts, thereby improving the robustness of outdoor vision systems. Existing unified restoration models exhibit limited generalization to real-world scenes due to their reliance on synthetic supervision and insufficient semantic constraints. In this paper, we propose a novel student--teacher semi-supervised framework that addresses both challenges. Specifically, we introduce an unreliable database that preserves failed teacher predictions as informative negative samples for contrastive learning, while a reliable database stores high-quality teacher predictions as positive samples. By jointly exploiting reliable pseudo-ground truths and unreliable teacher outputs, the proposed framework learns to enhance desirable restoration characteristics while avoiding common failures. We further propose a phase spectrum-based semantic constraint that replaces computationally expensive text-based supervision with an efficient and naturally aligned semantic prior. An adaptive phase consistency loss is also designed to dynamically balance supervision between the degraded input and teacher pseudo-ground truths according to degradation severity. Extensive experiments on real-world benchmarks demonstrate that the proposed method consistently outperforms existing state-of-the-art approaches in restoration quality and perceptual fidelity while exhibiting stronger generalization to real-world adverse weather conditions.
الكلمات المفتاحية
الموضوع
بيانات النشر
- المجلة
- غير متاح
- وصول مفتوح
- وصول مفتوح أخضر
اقتبس هذه المقالة
APA 7
Kiet, C. D. X., & Cham, T. J. (2026). Learning from Failure: Leveraging Unreliable Predictions in Semi-Supervised Real-World Adverse Weather Removal. https://omanscience.com/ar/articles/learning-from-failure-leveraging-unreliable-predictions-in-semi-supervised-real-world-adverse-weather-removal
MLA 9
Kiet, Cap Dang Xuan, and Tat-Jen Cham. "Learning from Failure: Leveraging Unreliable Predictions in Semi-Supervised Real-World Adverse Weather Removal." https://omanscience.com/ar/articles/learning-from-failure-leveraging-unreliable-predictions-in-semi-supervised-real-world-adverse-weather-removal.
شيكاغو (المؤلف–التاريخ)
Kiet, Cap Dang Xuan, and Tat-Jen Cham. 2026. "Learning from Failure: Leveraging Unreliable Predictions in Semi-Supervised Real-World Adverse Weather Removal." https://omanscience.com/ar/articles/learning-from-failure-leveraging-unreliable-predictions-in-semi-supervised-real-world-adverse-weather-removal.
هارفارد
Kiet, C. D. X. and Cham, T. J. (2026) 'Learning from Failure: Leveraging Unreliable Predictions in Semi-Supervised Real-World Adverse Weather Removal', Available at: https://omanscience.com/ar/articles/learning-from-failure-leveraging-unreliable-predictions-in-semi-supervised-real-world-adverse-weather-removal.
فانكوفر
Kiet CDX, Cham TJ. Learning from Failure: Leveraging Unreliable Predictions in Semi-Supervised Real-World Adverse Weather Removal. https://omanscience.com/ar/articles/learning-from-failure-leveraging-unreliable-predictions-in-semi-supervised-real-world-adverse-weather-removal
IEEE
C. D. X. Kiet, and T. J. Cham, "Learning from Failure: Leveraging Unreliable Predictions in Semi-Supervised Real-World Adverse Weather Removal," https://omanscience.com/ar/articles/learning-from-failure-leveraging-unreliable-predictions-in-semi-supervised-real-world-adverse-weather-removal.