A Study on Tensor and Matrix Models for Super-Resolution Fluorescence Microscopy

2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)(2019)

引用 0|浏览1
暂无评分
摘要
Super-resolution techniques for fluorescence microscopy are invaluable tools for studying phenomena that take place at sub-cellular scales, thanks to their capability of overcoming light diffraction. Yet, achieving sufficient temporal resolution for imaging live-cell processes remains a challenging problem. Exploiting the temporal fluctuations (blinking) of fluo-rophores is a promising approach that allows employing standard equipment and harmless excitation levels. In this work, we study a novel constrained tensor modeling approach that takes this temporal diversity into account to estimate the spatial distribution of fluorophores and their overall intensities. We compare this approach with an also novel matrix-based formulation which promotes structured sparsity via a continuous approximation of the cardinality function, as well as with other state-of-the-art methods.
更多
查看译文
关键词
fluorescence microscopy,super-resolution,tensor,structured sparsity
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要