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- W1594233226 abstract "We study the global and local convergence of a generic half-quadratic optimization algorithm inspired from the dual energy formulation of Geman and Reynolds [IEEE Trans. Pattern Anal. Mach. Intell., 14 (1992), pp. 367--383]. The target application is the minimization of $C^{1}$ convex and nonconvex objective functionals arising in regularized image reconstruction. Our global convergence proofs are based on a monotone convergence theorem of Meyer [J. Comput. System Sci., 12 (1976), pp. 108--121]. Compared to existing results, ours extend to a larger class of objectives and apply under weaker conditions; in particular, we cover the case where the set of stationary points is not discrete. Our local convergence results use a majorization-minimization interpretation to derive an insightful characterization of the basins of attraction; this new perspective grounds a formal description of the intuitive water-flooding analogy. We conclude with image restoration experiments to illustrate the efficiency of the algorithm under various nonconvex scenarios." @default.
- W1594233226 created "2016-06-24" @default.
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- W1594233226 date "2015-01-01" @default.
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- W1594233226 title "Generic Half-Quadratic Optimization for Image Reconstruction" @default.
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- W1594233226 doi "https://doi.org/10.1137/140987845" @default.
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