论文标题
学识渊博的Sparcom：展开的深层超分辨率显微镜
Learned SPARCOM: Unfolded Deep Super-Resolution Microscopy
论文作者
论文摘要
使用光激活的荧光分子来创建长发射极密度衍射有限图像的长序列可实现高精度的发射极定位，但以低时间分辨率为代价。我们建议使用算法展开方法将Sparcom（Sparcom（Sparcom）（一种最近的高性能经典方法）与基于模型的深度学习结合，以设计结合域知识的紧凑神经网络。我们的结果表明，我们可以使用拟议的学识渊博的Sparcom（LSPARCOM）网络从少数高发射极密度框架以及在不同的测试集中获得超分辨率成像。我们认为，LSPARCOM可以在许多情况下为可解释的，有效的活细胞成像铺平道路，并在生物结构的单分子定位显微镜中广泛使用。
The use of photo-activated fluorescent molecules to create long sequences of low emitter-density diffraction-limited images enables high-precision emitter localization, but at the cost of low temporal resolution. We suggest combining SPARCOM, a recent high-performing classical method, with model-based deep learning, using the algorithm unfolding approach, to design a compact neural network incorporating domain knowledge. Our results show that we can obtain super-resolution imaging from a small number of high emitter density frames without knowledge of the optical system and across different test sets using the proposed learned SPARCOM (LSPARCOM) network. We believe LSPARCOM can pave the way to interpretable, efficient live-cell imaging in many settings, and find broad use in single-molecule localization microscopy of biological structures.
