Whiteness-based bilevel estimation of weighted TV parameter maps for image denoising

Monica Pragliola, Luca Calatroni, Alessandro Lanza
3/10/2025
math.OCcs.CV

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Abstract

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 \mbox{parameter maps are shown.

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Cite this paper

@article{pragliola2025whitenessbased,
  title  = {Whiteness-based bilevel estimation of weighted TV parameter maps for image denoising},
  author = {Monica Pragliola and Luca Calatroni and Alessandro Lanza},
  year   = {2025},
  eprint = {2503.07814},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2503.07814v1}
}

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