In this paper, we propose a new framework to remove parts of the systematic errors affecting popular restoration algorithms, with a special focus for image processing tasks. Generalizing ideas that emerged for ℓ1 regularization, we develop an approach re-fitting the results of standard methods towards the input data. Total variation regularizations and non-local means are special cases of interest. We identify important covariant information that should be preserved by the re-fitting method, and emphasize the importance of preserving the Jacobian (w.r.t. the observed signal) of the original estimator. Then, we provide an approach that has a "twicing" flavor and allows re-fitting the restored signal by adding back a local affine transformation of the residual term. We illustrate the benefits of our method on numerical simulations for image restoration tasks.
@article{arxiv.1606.05158,
title = {CLEAR: Covariant LEAst-square Re-fitting with applications to image restoration},
author = {C-A. Deledalle and N. Papadakis and J. Salmon and S. Vaiter},
journal= {arXiv preprint arXiv:1606.05158},
year = {2016}
}