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X-Restormer++ wins CVPR 2026 challenge with enhanced image restoration

By PulseAugur Editorial · [1 sources] ·

Researchers have developed X-Restormer++, a novel framework that secured first place in the UG2+ CVPR 2026 All-Weather Restoration Challenge. The method builds on the X-Restormer baseline, enhancing it with a spatially-adaptive input scaling mechanism and a new Gradient-Guided Edge-Aware loss function. Significant improvements were achieved by expanding the training dataset with an additional 24,500 image pairs, leading to superior performance in image restoration under various weather conditions. AI

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

RANK_REASON Academic paper presenting a novel method that won a challenge. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

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

X-Restormer++ wins CVPR 2026 challenge with enhanced image restoration

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Academic paper presenting a novel method that won a challenge. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Fengjie Zhu ·

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

    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 strong baseline framework X-Restormer, which effectively captures both channel-wise global dependencies and s…

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