[IPOL discuss] [IPOL announce] new article: Localization and Image Reconstruction in a STORM Based Super-resolution Microscope

[announcements about the IPOL journal]

A new article is available in IPOL: https://www.ipol.im/pub/art/2024/496/

Pranjal Choudhury, and Bosanta Ranjan Boruah,
Localization and Image Reconstruction in a STORM Based Super-resolution 
Microscope,
Image Processing On Line, 14 (2024), pp. 64–85.
https://doi.org/10.5201/ipol.2024.496


Abstract
In this paper, we present a comprehensive Python program for localizing 
the point spread functions (PSFs) present in a stack of images and 
thereby rendering a super-resolved image in a Stochastic Optical 
Reconstruction Microscopy (STORM). A microscope that provides 
super-resolved images is known as a super-resolution microscope. Optical 
super-resolution microscopy is playing a pivotal role in advancing the 
field of optical imaging and has found applications in a number of areas 
such as cellular biology, biotechnology, medical research, and 
nanotechnology. The proposed Python program utilizes image processing 
techniques to accurately identify the PSFs present in highly noisy 
images with densely packed fluorescent objects. Our program not only 
provides all the necessary tools for image reconstruction in a STORM 
microscope under open source license but also offers certain advantages 
over the existing reconstruction software packages. Some such advantages 
are an option to start the reconstruction process and the visualization 
of the rendered super-resolved image in parallel with image acquisition 
and disposal of the images immediately after acquisition for minimum use 
of disk space. Parallel visualization of the reconstructed image allows 
aborting the image acquisition in the case the images are not suitable 
for super-resolution, thereby saving valuable time. Our Python program 
is demonstrated using a number of different image stacks. The proposed 
software code can be applied not only to STORM but also to any other 
super-resolution technique using single-molecule localization.




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