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arXiv · 1905.11264

Adaptive parameter selection for weighted-TV image reconstruction problems

Luca Calatroni·Alessandro Lanza·Monica Pragliola·Fiorella Sgallari
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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 (Dong et al. '11), the latter relying on a bilevel learning paradigm (Hinterm\"uller et al., '17)

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Luca Calatroni, Alessandro Lanza, Monica Pragliola, Fiorella Sgallari. 2019-05-27. Adaptive parameter selection for weighted-TV image reconstruction problems. https://doi.org/10.1088/1742-6596/1476/1/012003

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