MBL OVERLAY SOFTWARE
Our camera characterization algorithm is implemented for the popular software packages Micro-Manager 16 as well as ImageJ/Fiji 17 and enables (s)CMOS specific corrections for the broad imaging community.Ĭamera characteristics are conventionally determined by evaluating mean and variance of the signal in each pixel over many images at several light levels 1. We demonstrate that we can accurately characterize diverse (s)CMOS cameras and use the calibrations to avoid bias in 2D and 3D SMLM and in diffraction-limited imaging. In addition to gain, offset and noise maps, it also allows for the explicit consideration of dark current and thermal noise in the image reconstruction, which is of particular importance for long exposure times in SMLM or low light level live-cell imaging. Our pipeline does not require any specific camera illumination, as it relies solely on dark current and associated thermal noise. Here, we developed a fully automated camera characterization pipeline, which determines pixel- and exposure time-dependent noise, offset and gain maps that are the basis for numerous camera correction algorithms.
Especially for those cameras, a precise characterization and correction of the large pixelwise variability is indispensable. Industry-grade cameras approach the specifications of scientific-grade cameras and are increasingly used in the scientific community 8, 9, 10, 11, 12, 13, 14, 15. Consequently, a majority of sCMOS data is analyzed without explicit camera correction 7. Additionally, pixels feature individual dark current characteristics 5, rendering both noise and offset functions of the camera exposure time, which is often neglected in characterization and correction algorithms. Specific correction software is readily available 1, 3, 4, 5, 6, but tools which can easily acquire the necessary data for pixel-dependent noise, offset, and photon response are still missing.
This approach has been used to remove camera artifacts in both single molecule localization microscopy (SMLM) 1, 2 and diffraction-limited imaging 2, 3, 4. For quantitative analysis of the images, pixelwise properties of the camera must be well characterized and accounted for in the analysis algorithm to avoid artifacts. Scientific complementary metal oxide semiconductor (sCMOS) cameras are increasingly popular for scientific imaging including fluorescence and super-resolution microscopy. As our approach for camera characterization does not require any user interventions or additional hardware implementations, numerous correction algorithms that rely on camera characterization become easily applicable. In addition, our approach enables high-quality 3D super-resolution as well as live-cell time-lapse microscopy with cheap, industry-grade cameras. This allowed us to avoid structural bias in single-molecule localization microscopy (SMLM), which without correction is substantial even for scientific-grade, cooled cameras. Besides supplying the conventional camera maps of noise, offset and gain, our pipeline also gives access to dark current and thermal noise as functions of the exposure time.
Here, we developed a fully automated pipeline for camera characterization based solely on thermally generated signal, and implemented it in the popular open-source software Micro-Manager and ImageJ/Fiji. Although a variety of algorithmic corrections exists, they require a thorough characterization of the camera, which typically is not easy to access or perform. Such variations lead to image artifacts and render quantitative image interpretation difficult. Modern implementations of widefield fluorescence microscopy often rely on sCMOS cameras, but this camera architecture inherently features pixel-to-pixel variations.
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