Second-Order L1-Norm Regularization for Image Restoration with Biomedical Applications
Stamatis Lefkimmiatis, EPFL STI LIB

Seminar • 02 May 2011

Abstract
This work considers a second-order L1-norm regularization method that can be effectively used for image-restoration problems, in a variational framework. The new regularization term relies on the spectral norm of the Hessian operator and is well-suited for the restoration of a rich class of images that comprises more than merely piecewise-constant functions. We show that the proposed regularizer retains some of the most favorable properties of TV, namely, convexity, homogeneity, rotation, and translation invariance, while dealing effectively with the staircase effect. We further develop efficient minimization schemes of our complete objective function. The effectiveness of the proposed regularizer is validated through deblurring experiments under additive Gaussian noise on standard and biomedical images.