Adaptive parameter selection for weighted-TV image reconstruction problems. (March 2020) - Calatroni, Luca; Lanza, Alessandro; Pragliola, Monica; Sgallari, Fiorella
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].