Dynamic Sampling Schemes for Optimal Noise Learning Under Multiple Nonsmooth Constraints

Abstract : We consider the bilevel optimisation approach proposed in [5] for learning the optimal parameters in a Total Variation (TV) denoising model featuring for multiple noise distributions. In applications, the use of databases (dictionaries) allows an accurate estimation of the parameters, but reflects in high computational costs due to the size of the databases and to the nonsmooth nature of the PDE constraints. To overcome this computational barrier we propose an optimisation algorithm that, by sampling dynamically from the set of constraints and using a quasi-Newton method, solves the problem accurately and in an efficient way.
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Christian Pötzsche; Clemens Heuberger; Barbara Kaltenbacher; Franz Rendl. 26th Conference on System Modeling and Optimization (CSMO), Sep 2013, Klagenfurt, Austria. Springer Berlin Heidelberg, IFIP Advances in Information and Communication Technology, AICT-443, pp.85-95, 2014, System Modeling and Optimization. 〈10.1007/978-3-662-45504-3_8〉
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Luca Calatroni, Juan Los Reyes, Carola-Bibiane Schönlieb. Dynamic Sampling Schemes for Optimal Noise Learning Under Multiple Nonsmooth Constraints. Christian Pötzsche; Clemens Heuberger; Barbara Kaltenbacher; Franz Rendl. 26th Conference on System Modeling and Optimization (CSMO), Sep 2013, Klagenfurt, Austria. Springer Berlin Heidelberg, IFIP Advances in Information and Communication Technology, AICT-443, pp.85-95, 2014, System Modeling and Optimization. 〈10.1007/978-3-662-45504-3_8〉. 〈hal-01286460〉

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