siFRC

Highlights

(1) This implementation helps to understand Fourier Ring Correlation (FRC).
(2) It allows one to calculate FRC-based image resolution from a Single Image.

Additional guide

(1) Create demo images: python lenanoisecreation.py.
(2) Go through lenaFRC.ipynb to see the relation between the FRC and the SNR.
(3) Calculate single image resolution using demo images.
(4) Compare FRC value obtained from single image (siFRC) against that obtained from two images using main_2imgs.py.
(5) Added a siFRC result on a rotated image to show that FRC is sensitive to rotation due to the use of the imaginary term in its calculation.

``` usage: main.py [-h] [--input-dir INPUT_DIR] [--output-dir OUTPUTDIR] [--io-plot IOPLOT] [--thres THRES] [--crop] [--frc-len FRCLEN] [--frc-center FRCCENTER]

Python FRC

optional arguments: -h, --help show this help message and exit --input-dir input folder name with experimental images --output-dir output folder name that stores FRC plots (default: ) --io-plot whether to display splitted images and its FRC (default: False) --thres threshold type to be used in the FRC (default: half- bit) --crop whether to pass input images as is or to crop before passing (default: False) --frc-len total length in x-y direction with center as frc_len/2 (default: 400) --frc-center new center of cropped image become (min(image.shape)/2) + frc-center (default: 0) ```

Example usage

python main.py --input-dir './demo_images/noisy_lena_512/' --output-dir 'results/demo_images/noisy_lena_512' --thres 'half-bit'

Final Resolution

==> if the intersection of the FRC curve and the threhold is (P) in x-axis ==> and 1 pixel = q unit (may be [nm] or [um] or [cm]) ==> then the final resolution is (1/P)*q*sqrt(2) unit

Package requirements

numpy, matplotlib, glob, imageio, itertools, scipy

References

M. Van Heel and M. Schatz, “Fourier shell correlation threshold criteria,” Journal of structural biology, vol. 151, no. 3, pp. 250–262, 2005.

S. V. Koho, G. Tortarolo, M. Castello, T. Deguchi, A. Di- aspro, and G. Vicidomini, “Fourier ring correlation simplifies image restoration in fluorescence microscopy,” bioRxiv, p. 535583, 2019.