New Algorithm Expands Use of sCMOS for Biological Microscopy


Scientific complementary metal-oxide semiconductor cameras, or sCMOS for short, are highly advanced cameras that provide significant advances in imaging speed, view of field and sensitivity compared with more traditional methods and detectors. They’re rapidly gaining popularity in various fields, including biological sciences. However, their use in this particular field has been limited until now. This is because of fluctuations in pixel quality which tend to generate more noise than other cameras. But now, thanks to a brand new algorithm, the use of sCMOS has been expanded even in biological sciences.
The new algorithm corrects the noise, making the sCMOS cameras available for a wide range of biological research.
As Fang Huang, an assistant professor in Purdue University’s Weldon School of Biomedical Engineering explains, scientists have been trying to use sCMOS for live-cell single-molecule super-resolution imaging, so they introduced an algorithm for that purpose four years ago. However, that algorithm worked only for single-molecule research, meaning all objects had to be what Huang calls point emitters. Biological research, on the other hand, involves studying complex structures, so to use sCMOS in this field meant they needed a new algorithm.
And so, scientists developed the new algorithm that allows sCMOS sensors to be used in a broad spectrum of imaging methods for biological and quantitative biological studies. Thanks to it, researchers are now able to estimate the actual photon level at each pixel location.

Purdue University via (


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