Adverse weather conditions such as rain, haze, and snow significantly degrade image quality, posing challenges for both human perception and physical AI. Existing restoration methods require large computational budgets, struggling to process high-resolution images and handle different degradations. In this paper, we pr...
Paula Garrido-Mellado, Daniel Feijoo, Yuning Cui et al.· 0 citations
An Uncertainty-guided Adverse-weather Restoration Network (UAR-Net), a weather-specific AiO framework that integrates a gated transformer with balanced multi-scale skip connections that achieves state-of-the-art performance across multiple adverse-weather benchmarks.
Zhe-Ke Jin, Yuning Cui, Tianhu Jin et al.· 0 citations
This paper proposes AdaptIR, an efficient all-in-one image restoration network equipped with adaptive frequency enhancement, which achieves state-of-the-art performance on all-in-one image restoration tasks involving multiple degradations, while maintaining high computational efficiency.
Yuning Cui, Wen-Qi Ren, Alois C. Knoll· IEEE Transactions on Image P...· 1 citation
Backbone-Agnostic Stochastic Perturbation Learning (BSPL) is proposed, a plug-and-play framework for end-to-end real-world image dehazing and a Dual-space Domain-diversified Distribution-aware Contrastive Loss to regularize both clean restoration and hazy reconstruction spaces with real-world and synthetic negatives.
Bingcai Wei, Yuning Cui, Mingyu Liu et al.· arXiv.org· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
