超分辨定位成像技术凭借对数千甚至数万张采集的原始图像进行单分子定位及重建, 可以获得几十纳米的超高分辨率, 观察到之前看不到的细胞结构以及生物现象。然而, 在实际的成像过程中, 采集到的图像会受到像差(来源于光学系统的不完美或样品本身的不均匀性)的影响而导致分辨率下降, 甚至会造成错误结果。为此, 定量表征了几种典型像差对超分辨定位成像的影响, 并提出了一种基于样品图像本身的像差校正方法。仿真和实验结果表明, 像差会造成系统点扩展函数的变形以及成像分辨率的下降, 使用基于图像本身的像差校正方法可以恢复图像的成像质量。

Low-light camera is an indispensable component in various fluorescence microscopy techniques. However, choosing an appropriate low-light camera for a specific technique (for example, single molecule imaging) is always time-consuming and sometimes confusing, especially after the commercialization of a new type of camera called sCMOS camera, which is now receiving heavy demands and high praise from both academic and industrial users. In this tutorial, we try to provide a guide on how to fully access the performance of low-light cameras using a well-developed method called photon transfer curve (PTC). We first present a brief explanation on the key parameters for characterizing low-light cameras, then explain the experimental procedures on how to measure PTC. We also show the application of the PTC method in experimentally quantifying the performance of two representative low-light cameras. Finally, we extend the PTC method to provide offset map, read noise map, and gain map of individual pixels inside a camera.