2 citations · 3 across the 5 of their papers we have counts for
5 papers
Real order total variation with applications to the loss functions in learning schemes
Pan Liu, Xin Yang Lu, Kunlun He
Loss function are an essential part in modern data-driven approach, such as bi-level training scheme and machine learnings. In this paper we propose a loss function consisting of a…
Learning optimal orders of the underlying Euclidean norm in total variation image denoising
Pan Liu, Carola-Bibiane Schönlieb
A novel class of semi-norms, generalising the notion of the isotropic total variation and the an-isotropic total variation is introduced. A supervised learning me…
Adaptive image processing: a bilevel structure learning approach for mixed-order total variation regularizers
Pan Liu
A class of mixed-order \emph{PDE}-constraint regularizer for image processing problem is proposed, generalizing the standard first order total variation . A semi-supervised (…
Adaptive image processing: first order PDE constraint regularizers and a bilevel training scheme
Elisa Davoli, Irene Fonseca, Pan Liu
A bilevel training scheme is used to introduce a novel class of regularizers, providing a unified approach to standard regularizers , and . Optimal parameters…
The Weighted Ambrosio - Tortorelli Approximation Scheme
Irene Fonseca, Pan Liu
The Ambrosio-Tortorelli approximation scheme with weighted underlying metric is investigated. It is shown that it Γ-converges to a Mumford-Shah image segmentation functional depend…