Enhanced compressed sensing recovery with level set normals.

Details

Serval ID
serval:BIB_D5FEBB21714E
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Enhanced compressed sensing recovery with level set normals.
Journal
IEEE Transactions on Image Processing
Author(s)
Estellers V., Thiran J.P., Bresson X.
ISSN
1941-0042 (Electronic)
ISSN-L
1057-7149
Publication state
Published
Issued date
2013
Volume
22
Number
7
Pages
2611-2626
Language
english
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov't Publication Status: ppublish
Abstract
We propose a compressive sensing algorithm that exploits geometric properties of images to recover images of high quality from few measurements. The image reconstruction is done by iterating the two following steps: 1) estimation of normal vectors of the image level curves, and 2) reconstruction of an image fitting the normal vectors, the compressed sensing measurements, and the sparsity constraint. The proposed technique can naturally extend to nonlocal operators and graphs to exploit the repetitive nature of textured images to recover fine detail structures. In both cases, the problem is reduced to a series of convex minimization problems that can be efficiently solved with a combination of variable splitting and augmented Lagrangian methods, leading to fast and easy-to-code algorithms. Extended experiments show a clear improvement over related state-of-the-art algorithms in the quality of the reconstructed images and the robustness of the proposed method to noise, different kind of images, and reduced measurements.
Pubmed
Web of science
Create date
16/12/2013 9:34
Last modification date
20/08/2019 15:55
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