Find Paper, Faster
Example:10.1021/acsami.1c06204 or Chem. Rev., 2007, 107, 2411-2502
Regularization with Multilevel Non-stationary Tight Framelets for Image Restoration
Applied and Computational Harmonic Analysis  (IF3.055),  Pub Date : 2021-03-23, DOI: 10.1016/j.acha.2021.03.003
Yan-Ran Li, Raymond H.F. Chan, Lixin Shen, Xiaosheng Zhuang

Variational regularization models are one of the popular and efficient approaches for image restoration. The regularization functional in the model carries prior knowledge about the image to be restored. The prior knowledge, in particular for natural images, are the first-order (i.e. variance in luminance) and second-order (i.e. contrast and texture) information. In this paper, we propose a model for image restoration, using a multilevel non-stationary tight framelet system that can capture the image's first-order and second-order information. We develop an algorithm to solve the proposed model and the numerical experiments show that the model is effective and efficient as compared to other higher-order models.