As a wide-field imaging technique, super-resolution localization microscopy (SRLM) is theoretically capable of increasing field-of-view (FOV) without sacrificing either imaging speed or spatial resolution. There are two key factors for realizing large FOV SRLM: one is high-power illumination over the whole FOV with sufficient illumination homogeneity and the other is large FOV signal detection by a camera that has large number of pixels and sufficient detection sensitivity. However nowadays, even though the state-of-art scientific complementary metal-oxide semiconductor (sCMOS) cameras provide single molecule fluorescence signal detection ability over an FOV of more than 200 μm × 200 μm, large FOV SRLM still has not been achieved due to the lack of high-power homogeneous illumination. In this paper, we report large FOV SRLM with a high-power homogeneous illumination system. We demonstrate experimentally that our illumination system, which is based on a newly designed multimode fiber combiner, is capable of providing sufficient illumination intensity (~4.7 kW/cm @ 640 nm) and excellent illumination homogeneity. Compared with the reported approaches, our illumination system is advantageous in laser power scaling and square-shape illumination without beam clipping. As a result, our system makes full use of the sensor of a representative Hamamatsu Flash 4.0 V2 sCMOS camera (2048 × 2048 active pixels) and achieves a FOV as large as 221 μm × 221 μm with homogeneous spatial resolution. The flexible solution for realizing large FOV SRLM reported in this paper pushes a significant step toward the development of SRLM.
Low-light camera is an indispensable component in various°uorescence microscopy techniques. However, choosing an appropriate low-light camera for a speci¯c 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¯rst 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 o®set map, read noise map, and gain map of individual pixels inside a camera.
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