Learned SPARCOM: unfolded deep super-resolution microscopy
Learned SPARCOM: unfolded deep super-resolution microscopy
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DOI：
10.1364/oe.401925
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发表时间：
2020-09-14
期刊：
影响因子：
3.8
通讯作者：
Eldar, Yonina C.
中科院分区：
文献类型：
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作者：
Dardikman-Yoffe, Gili;Eldar, Yonina C.
使用光激活荧光分子创建低发射体密度衍射限制图像的长序列可以实现高精度发射体定位，但代价是时间分辨率低。我们建议将SPARCOM（一种最新的高性能经典方法）与基于模型的深度学习相结合，使用算法展开方法，设计一个包含领域知识的紧凑神经网络。我们的结果表明，我们可以在不了解光学系统的情况下，使用所提出的学习 SPARCOM (LSPARCOM) 网络在不同的测试集上从少量高发射器密度帧中获得超分辨率成像。我们相信 LSPARCOM 可以为许多环境中可解释、高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得到广泛应用。 (C) 2020 年美国光学学会根据 OSA 开放获取出版协议的条款
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. (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement