Zero norm based analysis model for image smoothing and reconstruction
Jiebo Song, Jia Li, Zheng‐an Yao, Kaisheng Ma, Chenglong Bao · Inverse Problems · 2020
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 ℓ 0 + ℓ 2 -norm based analysis model， which requires solving a challenging non-separable ℓ 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.