Whiteness-based bilevel estimation of weighted TV parameter maps for image denoising
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Abstract (arXiv)
We consider a bilevel optimisation strategy based on normalised residual whiteness loss for estimating the weighted total variation parameter maps for denoising images corrupted by additive white Gaussian noise. Compared to supervised and semi-supervised approaches relying on prior knowledge of (approximate) reference data and/or information on the noise magnitude, the proposal is fully unsupervised. To avoid noise overfitting an early stopping strategy is used, relying on simple statistics of optimal performances on a set of natural images. Numerical results comparing the supervised/unsupervised procedures for scalar/pixel-dependent mboxparameter maps are shown.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 7 of its 21 references that are in the map. First posted on arXiv 10 Mar 2025.
Builds on
7 of 21 references.
References · 7
- 2014Microsoft COCO: Common Objects in Contextcited by 17,608
- 2024UNSURE: self-supervised learning with Unknown Noise level and Stein's Unbiased Risk Estimatecited by 16
- 2015Bilevel parameter learning for higher-order total variation regularisation modelscited by 14
- 2023Learning Regularization Parameter-Maps for Variational Image Reconstruction using Deep Neural Networks and Algorithm Unrollingcited by 14
- 2016Techniques for Gradient Based Bilevel Optimization with Nonsmooth Lower Level Problemscited by 12
- 2017Backtracking strategies for accelerated descent methods with smooth composite objectivescited by 7
- 2024Whiteness-based bilevel learning of regularization parameters in imagingcited by 4
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Citing papers per year: 2026: 2.
2 papers.