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Blind Hierarchical Deconvolution

Arjas, A; Roininen, L; Sillanpaa, MJ; Hauptmann, A; (2020) Blind Hierarchical Deconvolution. In: Proceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE: Espoo, Finland. Green open access

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Abstract

Deconvolution is a fundamental inverse problem in signal processing and the prototypical model for recovering a signal from its noisy measurement. Nevertheless, the majority of model-based inversion techniques require knowledge on the convolution kernel to recover an accurate reconstruction and additionally prior assumptions on the regularity of the signal are needed. To overcome these limitations, we parametrise the convolution kernel and prior length-scales, which are then jointly estimated in the inversion procedure. The proposed framework of blind hierarchical deconvolution enables accurate reconstructions of functions with varying regularity and unknown kernel size and can be solved efficiently with an empirical Bayes two-step procedure, where hyperparameters are first estimated by optimisation and other unknowns then by an analytical formula.

Type: Proceedings paper
Title: Blind Hierarchical Deconvolution
Event: 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP)
ISBN-13: 9781728166629
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/MLSP49062.2020.9231822
Publisher version: https://doi.org/10.1109/MLSP49062.2020.9231822
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Blind deconvolution, hierarchical prior models, Bayesian inversion, Gaussian process models
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10108543
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