
Proceedings Paper
GASPACHO: a generic automatic solver using proximal algorithms for convex huge optimization problemsFormat | Member Price | Non-Member Price |
---|---|---|
$17.00 | $21.00 |
Paper Abstract
Many inverse problems (e.g., demosaicking, deblurring, denoising, image fusion, HDR synthesis) share various similarities:
degradation operators are often modeled by a specific data fitting function while image prior knowledge (e.g., sparsity)
is incorporated by additional regularization terms. In this paper, we investigate automatic algorithmic techniques for evaluating
proximal operators. These algorithmic techniques also enable efficient calculation of adjoints from linear operators
in a general matrix-free setting. In particular, we study the simultaneous-direction method of multipliers (SDMM) and the
parallel proximal algorithm (PPXA) solvers and show that the automatically derived implementations are well suited for
both single-GPU and multi-GPU processing. We demonstrate this approach for an Electron Microscopy (EM) deconvolution
problem.
Paper Details
Date Published: 21 September 2017
PDF: 14 pages
Proc. SPIE 10394, Wavelets and Sparsity XVII, 1039410 (21 September 2017); doi: 10.1117/12.2274424
Published in SPIE Proceedings Vol. 10394:
Wavelets and Sparsity XVII
Yue M. Lu; Dimitri Van De Ville; Manos Papadakis, Editor(s)
PDF: 14 pages
Proc. SPIE 10394, Wavelets and Sparsity XVII, 1039410 (21 September 2017); doi: 10.1117/12.2274424
Show Author Affiliations
Wilfried Philips, Univ. Gent (Belgium)
Published in SPIE Proceedings Vol. 10394:
Wavelets and Sparsity XVII
Yue M. Lu; Dimitri Van De Ville; Manos Papadakis, Editor(s)
© SPIE. Terms of Use
