Whiteness-based bilevel estimation of weighted TV parameter maps for image denoising
This addresses image denoising for applications like medical imaging or photography by providing an unsupervised method that avoids reliance on reference data, though it appears incremental as it builds on existing bilevel optimization and whiteness-based techniques.
The paper tackles the problem of estimating weighted total variation parameter maps for image denoising under additive white Gaussian noise, proposing a fully unsupervised bilevel optimization method based on normalized residual whiteness loss, with results showing competitive performance compared to supervised and semi-supervised approaches.
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.