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Proceedings Paper

GASPACHO: a generic automatic solver using proximal algorithms for convex huge optimization problems
Author(s): Bart Goossens; Hiêp Luong; Wilfried Philips
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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
Show Author Affiliations
Bart Goossens, Univ. Gent (Belgium)
Hiêp Luong, Univ. Gent (Belgium)
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)

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