Paper Info
Title
Phase retrieval with sparsity priors and application to microscopy video reconstruction
Abstract
The theory of compressed sensing (CS) predicts that structured images can be sampled in a compressive manner with very few nonadaptive linear measurements, made in a proper adjacent domain. However, is such a recovery still possible with non-linear measurements, such as optical-based Fourier modulus? In this paper, we study the problem of Fourier phase retrieval required for optical Fourier CS imaging. We propose an algorithm to solve this problem, exploiting a specific TV-based regularization constraint. We demonstrate the performance of the proposed method on synthetic and real test sequences, in the context of microscopy video reconstructions.
Year
DOI
Venue
2013
10.1109/ISBI.2013.6556547
Biomedical Imaging
Keywords
Field
DocType
Fourier transforms,biomedical optical imaging,compressed sensing,image reconstruction,image sequences,medical image processing,optical microscopy,video signal processing,Fourier phase retrieval,TV-based regularization constraint,compressed sensing theory,compressive manner,microscopy video reconstruction,nonadaptive linear measurements,optical Fourier CS imaging,optical-based Fourier modulus,phase retrieval,real test sequences,structured images,synthetic sequences,Fourier measurements,Phase retrieval,sparsity,total variation,video reconstruction
Iterative reconstruction,Computer vision,Phase retrieval,Pattern recognition,Computer science,Image processing,Fourier transform,Digital image correlation,Regularization (mathematics),Artificial intelligence,Compressed sensing,Phase correlation
Conference
ISSN
ISBN
Citations 
1945-7928
978-1-4673-6456-0
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Yoann Le Montagner1222.55
Elsa D. Angelini274060.44
Jean-Christophe Olivo-Marin374777.94
Le Montagner, Y.400.34
Olivo-Marin, J.-C.5638.68