Adaptive parameter selection for weighted-TV image reconstruction problems. (March 2020)

• Record Type: Journal Article
• Authors: Calatroni, Luca; Lanza, Alessandro; Pragliola, Monica; Sgallari, Fiorella
• Is Part Of: Journal of physics. Volume 1476(2020)
• Publication Date: 2020-03
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1476/1/012003
• Languages: English
• ISSNs: 1742-6588

Abstract

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].