21 September 2017 GASPACHO: a generic automatic solver using proximal algorithms for convex huge optimization problems
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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.
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Bart Goossens, Hiêp Luong, Wilfried Philips, "GASPACHO: a generic automatic solver using proximal algorithms for convex huge optimization problems", Proc. SPIE 10394, Wavelets and Sparsity XVII, 1039410 (21 September 2017); doi: 10.1117/12.2274424; https://doi.org/10.1117/12.2274424
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