利用散射实现动态随机介质中的超分辨率成像
Imaging with super-resolution in changing random media
- 通过稀疏字典学习与聚类分析处理海量阵列数据
- 在数据充足时突破均匀介质极限，实现超分辨成像
- 适合需要高精度成像的复杂环境应用
我们开发了一种成像算法，利用强散射在变化的随机介质中实现超分辨率。该方法通过稀疏字典学习、聚类和多维缩放处理大规模多样化的阵列数据。从随机初始化出发，算法可稳定提取用于精确成像所需的未知介质特性，采用反向传播、ℓ₂或ℓ₁方法。令人惊讶的是，散射反而提升了分辨率，超越了均匀介质的极限。当数据充足时，该算法可实现成像中的超分辨率。
原文摘要 · Abstract (English)
We develop an imaging algorithm that exploits strong scattering to achieve super-resolution in changing random media. The method processes large and diverse array datasets using sparse dictionary learning, clustering, and multidimensional scaling. Starting from random initializations, the algorithm reliably extracts the unknown medium properties necessary for accurate imaging using back-propagation, $\ell_2$ or $\ell_1$ methods. Remarkably, scattering enhances resolution beyond homogeneous medium limits. When abundant data are available, the algorithm allows the realization of super-resolution in imaging.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。