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English(EN) EvLIR: Learning Illumination Residuals from Ordered Events for Low-Light Image Enhancement

新的EvLIR框架使用时间事件数据增强低光图像

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了EvLIR，一种使用事件相机增强低光图像的新型框架。与先前将事件数据视为静态的方法不同，EvLIR显式地对短窗口内亮度变化的 temporal evolution 进行建模。这种 temporal information 使用轻量级的ConvGRU模块进行编码，然后生成一个照明校正来指导图像恢复。EvLIR在多个基准测试中表现出色，在十二个数据集-指标对中的十一个中优于现有方法。 AI

影响 这项研究可能带来自动驾驶和机器人等应用中低光成像能力的提升。

排序理由 详细介绍低光图像增强新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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新的EvLIR框架使用时间事件数据增强低光图像

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详细介绍低光图像增强新方法的学术论文。[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) · Haoxian Zhou, Chuanzhi Xu, Langyi Chen, Pengfei Ye, Haodong Chen, Qiang Qu, Ali Anaissi, Weidong Cai ·

    EvLIR：从有序事件中学习光照残差以增强低光图像

    arXiv:2606.29430v1 Announce Type: new Abstract: Low-light image enhancement is severely ill-posed when the input frame contains missing structure, saturated noise, and weak local contrast. Event cameras provide asynchronous brightness-change observations with high temporal resolu…

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