Self-STORM: Deep Unrolled Self-Supervised Learning for Super-Resolution Microscopy

arXiv:2403.16974v1 Announce Type: cross
Abstract: The use of fluorescent molecules to create long sequences of low-density, diffraction-limited images enables highly-precise molecule localization. However, this methodology requires lengthy imaging times, which limits the ability to view dynamic interactions of live cells on short time scales. Many techniques have been developed to reduce the number of frames needed for localization, from classic iterative optimization to deep neural networks. Particularly, deep algorithm unrolling utilizes both the structure of iterative sparse recovery algorithms and …

abstract arxiv cells cs.cv cs.lg dynamic eess.iv however images imaging interactions localization low methodology microscopy molecules reduce resolution self-supervised learning storm supervised learning type view