  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)
