Zero norm based analysis model for image smoothing and reconstruction

Jiebo Song, Jia Li, Zhengan Yao, Kaisheng Ma, Chenglong Bao

Abstract:

The sparsity-based approaches have demonstrated promising performance in
image processing. In this paper, for better preservation of the salient edge
structures of images, we propose an 0 + 2-norm based analysis model,
which requires solving a challenging non-separable 0-norm related minimization
problem, and we also propose an inexact augmented Lagrangian method
with proven convergence to a local minimum. Extensive experiments in image
smoothing, including texture removal and context smoothing, show that our
method achieves better visual results over various sparsity-based models and
the CNN method. Also, experiments on sparse view CT reconstruction further
validate the advantage of the proposed method.