Joint image registration and reconstruction from compressed multi-view measurements

We present a method for joint reconstruction of a set of images representing a given scene from few multi-view measurements obtained by compressed sensing. We model the correlation between measurements using global geometric transformations represented by few parameters. Then, we propose an algorithm able to jointly estimate these transformation parameters and the observed images from the available measurements. This method is also robust to occlusions appearing in the scene. The reconstruction algorithm minimizes a non-convex functional and generates a sequence of estimates converging to a critical point of this functional. Finally, we demonstrate the efficiency of the proposed method using numerical simulations.


Publié dans:
Wavelets And Sparsity Xv, 8858
Présenté à:
Wavelets and Sparsity XV, August 26-29, 2013
Année
2013
Publisher:
Bellingham, Spie-Int Soc Optical Engineering
ISSN:
0277-786X
ISBN:
978-0-8194-9708-6
Mots-clefs:
Laboratoires:




 Notice créée le 2013-09-20, modifiée le 2018-09-13


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