Covariant LEAst-square Re-fitting for image restoration

938738dae7f1cea7e1ca8d400bac742a?s=47 npapadakis
March 30, 2017

Covariant LEAst-square Re-fitting for image restoration

In this talk, a framework to remove parts of the systematic errors affecting popular restoration algorithms is presented, with a special focus on image processing tasks. Generalizing ideas that emerged for ℓ1 regularization, an approach re-fitting the results of standard methods towards the input data is developed. Total variation regularization and non-local means are special cases of interest. Important covariant information that should be preserved by the re-fitting method are identified, and the importance of preserving the Jacobian (w.r.t. the observed signal) of the original estimator is emphasized. Then, a numerical approach is proposed. It has a twicing flavor and allows re-fitting the restored signal by adding back a local affine transformation of the residual term. The benefits of the method are illustrated on numerical simulations for image restoration tasks.



March 30, 2017