Selecting a camera can be a difficult due to the different technologies available and the range of different camera models. Technical specifications of different camera technologies vary, sometimes by several orders of magnitude, or sometimes by a seemingly small amount, and it is not clear how these differences affect camera performance for a specific application. The key to selecting the most suitable camera is by combining several key specifications with the needs of the specific application. The important parameters that influence how well suited a camera will be for a specific application can be identified as: sensitivity, speed, field of view and in some cases, low dark noise. A further subset of factors such as: dynamic range, shuttering modes, connectivity or vibration can also be used to determine suitability. This information should simplify the decision tree allowing for flexibility that may be needed in multi-user, multiapplication/ technique environment spanning often radically different light regimes and sample sensitivities. Recently cameras based on back-illuminated sCMOS technology have become available which offer further improvements in sensitivity. This provides a further option for consideration to the existing sCMOS and EMCCD cameras available. Considering this new camera technology, we will characterize the new sCMOS camera model within the wider context of the camera technologies and other models available.
Super-resolution radial fluctuations (SRRF) is a combination of temporal fluctuation analysis and localization microscopy. One of the key differences between SRRF and other super-resolution methods is its applicability to live-cell dynamics because it functions across a very wide range of fluorophore densities and excitation powers. SRRF is applied to data from imaging modes which include widefield, TIRF and confocal, where short frame bursts (e.g. 50 frames) can be processed to deliver spatial resolution enhancements similar to or better than structured illumination microscopy (SIM). On the other hand, with sparse data e.g. stochastic optical reconstruction microscopy (STORM), SRRF can deliver resolution similar to Gaussian fitting localization methods. Thus, SRRF could provide a route to super-resolution without the need for specialized optical hardware, exotic probes or very high-power densities. We present a fast GPUbased SRRF algorithm termed “SRRF-Stream” and apply it to imagery from an iXon EMCCD coupled to a multi-modal imaging platform, Dragonfly. The new implementation is <300 times faster than the standard CPU version running on an Intel Xeon 3.5GHz 4 core processor, and < 20 times faster than the NanoJ GPU implementation, while also being integrated with acquisition for real time use. In this paper we explore the image resolution and quality with EMCCD and sCMOS cameras and various fluorophores including fluorescent proteins and organic dyes.
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