17 citations · 34 across the 12 of their papers we have counts for
13 papers
Whiteness-based bilevel estimation of weighted TV parameter maps for image denoising
Monica Pragliola, Luca Calatroni, Alessandro Lanza
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…
Whiteness-based bilevel learning of regularization parameters in imaging
Carlo Santambrogio, Monica Pragliola, Alessandro Lanza +2
We consider an unsupervised bilevel optimization strategy for learning regularization parameters in the context of imaging inverse problems in the presence of additive white Gaussi…
Bilevel learning of regularization models and their discretization for image deblurring and super-resolution
Tatiana A. Bubba, Luca Calatroni, Ambra Catozzi +6
Bilevel learning is a powerful optimization technique that has extensively been employed in recent years to bridge the world of model-driven variational approaches with data-driven…
Whiteness-based parameter selection for Poisson data in variational image processing
Francesca Bevilacqua, Alessandro Lanza, Monica Pragliola +1
We propose a novel automatic parameter selection strategy for variational imaging problems under Poisson noise corruption. The selection of a suitable regularization parameter, who…
Automatic parameter selection for the TGV regularizer in image restoration under Poisson noise
Daniela di Serafino, Monica Pragliola
We address the image restoration problem under Poisson noise corruption. The Kullback-Leibler divergence, which is typically adopted in the variational framework as data fidelity t…
ADMM-based residual whiteness principle for automatic parameter selection in super-resolution problems
Monica Pragliola, Luca Calatroni, Alessandro Lanza +1
We propose an automatic parameter selection strategy for the problem of image super-resolution for images corrupted by blur and additive white Gaussian noise with unknown standard…