# Adaptive parameter selection for weighted-TV image reconstruction problems

arXiv: https://arxiv.org/abs/1905.11264 · HTML: https://mlsanity.com/paper/1905.11264/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2019.

## Built on by (most cited first, 2 of 2)

- [Whiteness-based bilevel learning of regularization parameters in imaging](https://mlsanity.com/paper/2403.07026/) (2024)
- [Mixed geometry information regularization for image multiplicative denoising](https://mlsanity.com/paper/2412.16445/) (2024)

## Abstract (arXiv)

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)