English(EN) Robust Self-Supervised Cross-Modal Super-Resolution against Real-World Misaligned Observations

新型自监督模型应对真实世界图像错位问题

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-13 04:00

研究人员开发了RobSelf，一种新颖的自监督跨模态超分辨率模型，能够有效处理真实世界的错位观测。与依赖模拟数据或次优对齐的先前方法不同，RobSelf联合优化了感知错位的特征翻译器和感知内容的参考滤波器。这种方法实现了无监督的跨模态和跨分辨率对齐，达到了最先进的性能，并且效率显著提高，比先前自监督技术快15.3倍。 AI

影响这种新的自监督方法通过更好地处理错位数据，有望提高真实世界应用中的图像质量。

排序理由研究论文发布在arXiv上，详细介绍了一个新模型。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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arXiv cs.CV TIER_1 English(EN) · Xiaoyu Dong, Jiahuan Li, Ziteng Cui, Naoto Yokoya · 2026-07-13 04:00

鲁棒的自监督跨模态超分辨率，对抗真实世界中的错位观测

arXiv:2602.18822v3 Announce Type: replace Abstract: Cross-modal super-resolution (SR) on real-world misaligned data is challenging, as only unlabeled low-resolution (LR) source and high-resolution (HR) guide images with complex spatial misalignment are available. Previous methods…

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鲁棒的自监督跨模态超分辨率，对抗真实世界中的错位观测

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