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ACsN (pronounced as action) stands for Automatic Correction of sCMOS-related Noise. It combines an accurate estimation of noise variation with sparse filtering to eliminate the most relevant noise sources in the images of a sCMOS sensor. This results in a drastic reduction of pixel-dependent noise in sCMOS images and an enhanced stability of denoising performance at a competitive computational speed.
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Please, cite our paper on Nature Communications.
Mandracchia, B., Hua, X., Guo, C. et al. Fast and accurate sCMOS noise correction for fluorescence microscopy. Nat Commun 11, 94 (2020) doi:10.1038/s41467-019-13841-8
ACsN requires a standard computer with enough RAM to support MATLAB 2018b. For minimum performance, this will be a computer with about 4 GB of RAM. For optimal performance, we recomend the following specs:
RAM: 16+ GB; CPU: 6+ cores, 3.2+ GHz/core.
MATLAB 2018b+
MATLAB "Curve Fitting" Toolbox
Windows OS 64 bit, Linux 64 bit or Mac OS X 64 bit
To run ACsN from MATLAB command line:
- Add the folder ACsN_code to your MATLAB path (including subfolders).
- In the command line type help ACSN or run the Sample code script in the Test Images folder to see the code usage.
To run ACsN from ImageJ/Fiji follow these steps:
- Add the ImageJ-MATLAB update site to ImageJ. To see how, look at here.
- Go to Edit > Options > MATLAB and enter the file path for MATLAB licence.
- Add the ACsN_code folder and subfolders to the MATLAB path.
- Copy the file 'ACsN_.m' to the folder '\plugins\Scripts\Process'.
- Select an open image in ImageJ and then press Process > ACsN from the menu toolbar.
- To test the program you can use the images provided in the Test Images folder. See the file Settings.txt for the aquisition parameters.
The installation on a recommended computer should take less than 3 seconds.