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Conference Papers Year : 2020

Adaptive parameter selection for weighted-TV image reconstruction problems

Abstract

We propose an efficient estimation technique for the automatic selection of locally-adaptive Total Variation regularisation parameters based on an hybrid strategy which combines a local maximum-likelihood approach estimating space-variant image scales with a global discrepancy principle related to noise statistics. We verify the effectiveness of the proposed approach solving some exemplar image reconstruction problems and show its outperformance in comparison to state-of-the-art parameter estimation strategies, the former weighting locally the fit with the data [4], the latter relying on a bilevel learning paradigm [8, 9].

Dates and versions

hal-03141109 , version 1 (15-02-2021)

Identifiers

Cite

Luca Calatroni, Alessandro Lanza, Monica Pragliola, Fiorella Sgallari. Adaptive parameter selection for weighted-TV image reconstruction problems. NCMIP 2019 - 9th International Conference on New Computational Methods for Inverse Problems, May 2021, Cachan, France. pp.012003, ⟨10.1088/1742-6596/1476/1/012003⟩. ⟨hal-03141109⟩
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