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X-Restormer++ wins CVPR 2026 challenge with novel image restoration technique

By PulseAugur Editorial · [1 sources] ·

Researchers have developed X-Restormer++, a novel deep learning model that won first place in the UG2+ Challenge at CVPR 2026 for all-weather image restoration. The model builds upon the X-Restormer architecture, incorporating dual-attention mechanisms and a spatially-adaptive input scaling method. A key innovation is the Gradient-Guided Edge-Aware (GGEA) Loss, which enhances the preservation of structural details by focusing supervision on edges and high-frequency regions. The solution employed a two-stage training strategy with a dual-model ensemble inference for optimal performance. AI

IMPACT Sets a new benchmark for all-weather image restoration, potentially improving applications in autonomous driving and surveillance.

RANK_REASON This is a research paper detailing a novel method that won a competition. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

X-Restormer++ wins CVPR 2026 challenge with novel image restoration technique

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This is a research paper detailing a novel method that won a competition. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Youwei Pan, Leilei Cao, Yingfang Zhu, Fengjie Zhu ·

    X-Restormer++: 1st Place Solution for the UG2+ CVPR 2026 All-Weather Restoration Challenge

    arXiv:2605.13258v2 Announce Type: replace-cross Abstract: In this work, we present our winning solution for the 8th UG2+ Challenge (CVPR 2026) Track 1: Image Restoration under All-weather Conditions. Our method is built upon the X-Restormer baseline, which captures both channel-w…

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