English(EN) AnyMod-LLVE: Low-Light Video Enhancement with Modality-Agnostic Inference

新框架可在无辅助传感器的情况下增强低光视频

作者 PulseAugur 编辑部 · [2 个来源] · 2026-06-09 17:59

研究人员开发了一个名为 AMNet 的新框架，用于增强低光视频，即使在辅助传感器数据不可用的情况下也能运行。该系统使用空间-光谱双门控翻译器从可用的 RGB 输入生成隐式表示，有效模拟缺失的模态。该方法通过大规模多模态预训练和广泛的实验得到了验证，在模态缺失条件下低光视频增强方面表现出色。 AI

影响这种新方法可以改善各种应用在挑战性光照条件下的视频质量。

排序理由该集群包含一篇学术论文，详细介绍了低光视频增强的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

报道来源 [2]

arXiv cs.CV TIER_1 English(EN) · Hangfeng Liang, Yutao Hu, Yanhan Hu, Xiaohan Wu, Wenqi Shao, Ying Fu · 2026-06-10 04:00

AnyMod-LLVE: Low-Light Video Enhancement with Modality-Agnostic Inference

arXiv:2606.11186v1 Announce Type: new Abstract: Low-light video enhancement (LLVE) remains a challenging task due to severe information degradation under low-illumination conditions. Recent multimodal approaches have significantly improved enhancement performance by incorporating…
arXiv cs.CV TIER_1 English(EN) · Ying Fu · 2026-06-09 17:59

AnyMod-LLVE: Low-Light Video Enhancement with Modality-Agnostic Inference

Low-light video enhancement (LLVE) remains a challenging task due to severe information degradation under low-illumination conditions. Recent multimodal approaches have significantly improved enhancement performance by incorporating auxiliary modalities, such as event streams and…