[IPOL announce] new article: Electron Paramagnetic Resonance Image Reconstruction with Total Variation Regularization

[announcements about the IPOL journal]

A new article is available in IPOL: https://www.ipol.im/pub/art/2023/414/

Rémy Abergel, Mehdi Boussâa, Sylvain Durand, and Yves-Michel Frapart, 
Electron Paramagnetic Resonance Image Reconstruction with Total 
Variation Regularization, Image Processing On Line, 13 (2023), pp. 
90–139. https://doi.org/10.5201/ipol.2023.414

Abstract
This work focuses on the reconstruction of two and three dimensional 
images of the concentration of paramagnetic species from electron 
paramagnetic resonance (EPR) measurements. A direct operator, modeling 
how the measurements are related to the paramagnetic sample to be 
imaged, is derived in the continuous framework taking into account the 
physical phenomena at work during the acquisition process. Then, this 
direct operator is discretized to closely take into account the discrete 
nature of the measurements and provide an explicit link between them and 
the discrete image to be reconstructed. A variational inverse problem 
with total variation regularization is formulated and an efficient 
resolvant scheme is implemented. The setting of the reconstruction 
parameters is thoroughly studied and facilitated thanks to the 
introduction of appropriate normalization factors. Moreover, an a 
contrario algorithm is proposed to derive the optimal resolution at 
which the data should be acquired. Finally, an in-depth experimental 
study over real EPR datasets is done to illustrate the potential and 
limitations of the presented image reconstruction model.