Adaptive image processing: a bilevel structure learning approach for mixed-order total variation regularizers
Analysis of PDEs
2019-03-19 v1
Abstract
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 (bilevel) training scheme, which provides a simultaneous optimization with respect to parameters and the new class of regularizers, is studied. Also, A finite approximation method, which used to solve the global optimization solutions of such training scheme, is introduced and analyzed.
Cite
@article{arxiv.1903.06911,
title = {Adaptive image processing: a bilevel structure learning approach for mixed-order total variation regularizers},
author = {Pan Liu},
journal= {arXiv preprint arXiv:1903.06911},
year = {2019}
}