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English(EN) Learning from Failure: Leveraging Unreliable Predictions in Semi-Supervised Real-World Adverse Weather Removal

新框架利用失败的预测来改进恶劣天气图像恢复

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新颖的半监督式恶劣天气图像恢复框架，旨在提高户外视觉系统的准确性和泛化能力。所提出的方法采用学生-教师模型，该模型同时从可靠的伪地面真实和不可靠的教师预测中学习，将失败的预测视为对比学习的信息性负样本。此外，还引入了基于相位频谱的语义约束和自适应相位一致性损失，以在不依赖计算成本高昂的文本监督的情况下提高恢复质量和感知保真度。 AI

影响 这项研究可以通过改善恶劣天气条件下的图像质量来提高户外视觉系统的鲁棒性。

排序理由 详细介绍图像恢复新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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新框架利用失败的预测来改进恶劣天气图像恢复

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详细介绍图像恢复新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Cap Dang Xuan Kiet, Tat-Jen Cham ·

    从失败中学习：利用不可靠的预测进行半监督真实世界恶劣天气去除

    arXiv:2610.02051v1 Announce Type: new Abstract: 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 limi…

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