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基于混合神经表示的先导红外时空噪声建模

Chao Qu, Jing Han, Haotian Yu

|February 20, 2026

概括

这项研究介绍了IRSTN,一个用于红外空间时空噪声建模的新框架. 它准确地捕捉时间噪声变化,在动态场景中显著提高红外视频消噪性能.

科学领域:

计算机视觉计算机视觉
图像处理图像处理
传感器技术传感器技术

背景情况:

准确的噪声建模对于红外成像应用至关重要.
当前的方法在动态场景中与时间噪声变化作斗争.
这种限制阻碍了红外视频消噪算法的性能.

研究的目的:

开发一个强大的红外时空噪声建模框架.
解决现有的仅空间噪声模型的局限性.
为了提高合成红外噪声的保真度,用于算法评估.

主要方法:

构建一个全面的红外视频数据集用于噪声建模.
红外时空噪声 (IRSTN) 建模框架的建议.
通过循环反复的对抗性学习整合空间噪声先验和时间位置编码.

主要成果:

IRSTN有效地从未配对的数据中合成高保真性红外噪声.
生成的噪声与现实世界的红外噪声具有很高的时空一致性.
在使用IRSTN的视频拒绝网络中观察到显著的性能改进.

结论:

IRSTN为红外视频中的时空噪声建模提供了有效的解决方案.
该框架可以更准确地描述和合成噪声.
IRSTN提高了红外视觉系统的功能,特别是在动态环境中.

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