用动态模态分解自适应分离视频中不同形态成分，提升去噪与目标识别效果。
A Dynamic Mode Decomposition Approach to Morphological Component Analysis
- 通过动态模态分解特征聚类，生成数据驱动的字典替代传统预设字典。
- 在Adobe 240fps视频数据集上实现显著去噪，提升信号质量。
- 适用于雷达图像中弱目标分离，适合信号处理与视频分析研究者。
本文提出一种基于动态模态分解（DMD）的新方法，用于根据场景内容变化动态调整视频表示。通过聚类DMD特征值，构建数据驱动的字典，实现对视频中结构差异显著成分的自适应分离。将传统形态成分分析（MCA）扩展为动态形态成分分析（DMCA），取代原有固定不相干字典，引入可学习的稀疏先验。首先以静态图像为例展示其动机，随后在Adobe 240fps数据集上验证其在视频去噪中的有效性。进一步展示了在海况背景下增强微弱目标信噪比的能力，并最终应用于逆合成孔径雷达图像中自行车与风杂波的分离任务。
原文摘要 · Abstract (English)
This paper introduces a novel methodology of adapting the representation of videos based on the dynamics of their scene content variation. In particular, we demonstrate how the clustering of dynamic mode decomposition eigenvalues can be leveraged to learn an adaptive video representation for separating structurally distinct morphologies of a video. We extend the morphological component analysis (MCA) algorithm, which uses multiple predefined incoherent dictionaries and a sparsity prior to separate distinct sources in signals, by introducing our novel eigenspace clustering technique to obtain data-driven MCA dictionaries, which we call dynamic morphological component analysis (DMCA). After deriving our novel algorithm, we offer a motivational example of DMCA applied to a still image, then demonstrate DMCA's effectiveness in denoising applications on videos from the Adobe 240fps dataset. Afterwards, we provide an example of DMCA enhancing the signal-to-noise ratio of a faint target summed with a sea state, and conclude the paper by applying DMCA to separate a bicycle from wind clutter in inverse synthetic aperture radar images.
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