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Numerical evaluation of subsampling effects on image reconstruction in compressed sensing microscopy
 

Summary: Numerical evaluation of subsampling effects on image
reconstruction in compressed sensing microscopy
Yoann Le Montagnera,b, Marcio de Moraes Marima, Elsa Angelinib,
and Jean-Christophe Olivo-Marina
aInstitut Pasteur, Unité d'Analyse d'Images Quantitative CNRS URA 2582, F-75015 Paris
bInstitut Télécom, Télécom ParisTech CNRS LTCI, F-75013 Paris
ABSTRACT
When undergoing reconstruction through a compressed sensing scheme, real microscopic images are affected
by various artifacts that lead to detail loss. Here, we discuss how the sampling strategy and the subsampling
rate affect the compressed sensing reconstruction, and how they should be determined according to a targeted
accuracy level of the reconstruction. We investigate the relevance and limits of theoretical results through
several numerical reconstructions of test images. We discuss the quantitative and qualitative artifacts that affect
the reconstructed signal when reducing the number of measurements in the Fourier domain. We conclude by
extending our results to real microscopic images.
1. SUMMARY OF THEORETICAL BACKGROUND IN THE COMPRESSED
SENSING FIELD
1.1 Recovering sparse data from incomplete measurements
The recent sampling theory of compressed sensing (CS) predicts that sparse signals and images can be re-
constructed from what was previously believed to be incomplete information. CS was introduced by Candès,
Romberg and Tao,1

  

Source: Angelini, Elsa -Département Traitement du Signal et des Images, Telecom ParisTech

 

Collections: Engineering; Biology and Medicine