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21 February 2017 Quantitative evaluation of the accuracy and variance of individual pixels in a scientific CMOS (sCMOS) camera for computational imaging
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The“scientific” CMOS (sCMOS) camera architecture fundamentally differs from CCD and EMCCD cameras. In digital CCD and EMCCD cameras, conversion from charge to the digital output is generally through a single electronic chain, and the read noise and the conversion factor from photoelectrons to digital outputs are highly uniform for all pixels, although quantum efficiency may spatially vary. In CMOS cameras, the charge to voltage conversion is separate for each pixel and each column has independent amplifiers and analog-to-digital converters, in addition to possible pixel-to-pixel variation in quantum efficiency. The “raw” output from the CMOS image sensor includes pixel-to-pixel variability in the read noise, electronic gain, offset and dark current. Scientific camera manufacturers digitally compensate the raw signal from the CMOS image sensors to provide usable images. Statistical noise in images, unless properly modeled, can introduce errors in methods such as fluctuation correlation spectroscopy or computational imaging, for example, localization microscopy using maximum likelihood estimation. We measured the distributions and spatial maps of individual pixel offset, dark current, read noise, linearity, photoresponse non-uniformity and variance distributions of individual pixels for standard, off-the-shelf Hamamatsu ORCA-Flash4.0 V3 sCMOS cameras using highly uniform and controlled illumination conditions, from dark conditions to multiple low light levels between ~20 to ~1,000 photons / pixel per frame to higher light conditions. We further show that using pixel variance for flat field correction leads to errors in cameras with good factory calibration.
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Shigeo Watanabe, Teruo Takahashi, and Keith Bennett "Quantitative evaluation of the accuracy and variance of individual pixels in a scientific CMOS (sCMOS) camera for computational imaging", Proc. SPIE 10071, Single Molecule Spectroscopy and Superresolution Imaging X, 100710Z (21 February 2017);

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