Fast and Lightweight Network for Single Frame Structured Illumination Microscopy Super-Resolution
Fast and Lightweight Network for Single Frame Structured Illumination Microscopy Super-Resolution
复制标题
用于单帧结构照明显微镜超分辨率的快速、轻量级网络
DOI：
10.1109/tim.2022.3161721
复制
发表时间：
2022-01-01
影响因子：
5.6
通讯作者：
Yang, Jian
中科院分区：
文献类型：
--
作者：
Cheng, Xi;Li, Jun;Yang, Jian
结构照明显微术（SIM）是一种重要的基于超分辨率的显微技术，它突破了衍射极限，增强了光学显微系统。随着生物学和医学工程的发展，对极低光照和短曝光环境下的实时和鲁棒的SIM成像提出了很高的要求。现有的SIM技术通常需要多个结构化照明帧来产生高分辨率图像。在本文中，我们提出了一种基于深度学习的单帧SIM（SF-SIM）。我们的SF-SIM只需要一个结构化照明帧的镜头，与传统的SIM系统（通常需要15个镜头）相比，可以产生类似的结果。在我们的SF-SIM中，我们提出了一个噪声估计器，可以有效地抑制图像中的噪声，使我们的方法能够在低光和短曝光环境中工作，而不需要堆叠多个帧进行非局部去噪。我们还设计了一个带通注意模块，使我们的深度网络对频率的变化更加敏感，提高了成像质量。我们提出的SF-SIM比传统的SIM方法快近14倍，达到类似的结果。因此，我们的方法对微生物学和医学的发展具有重要价值。
Structured illumination microscopy (SIM) is an important super-resolution-based microscopy technique that breaks the diffraction limit and enhances optical microscopy systems. With the development of biology and medical engineering, there is a high demand for real-time and robust SIM imaging under extreme low-light and short-exposure environments. Existing SIM techniques typically require multiple structured illumination frames to produce a high-resolution image. In this article, we propose a single-frame SIM (SF-SIM) based on deep learning. Our SF-SIM only needs one shot of a structured illumination frame and generates similar results compared with the traditional SIM systems that typically require 15 shots. In our SF-SIM, we propose a noise estimator that can effectively suppress the noise in the image and enable our method to work in the low-light and short-exposure environment without the need for stacking multiple frames for nonlocal denoising. We also design a bandpass attention module that makes our deep network more sensitive to the change of frequency and enhances the imaging quality. Our proposed SF-SIM is almost 14 times faster than traditional SIM methods when achieving similar results. Therefore, our method is significantly valuable for the development of microbiology and medicine.