Abstract
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.
| Original language | English |
|---|---|
| Pages | 332-348 |
| Number of pages | 17 |
| Volume | 53 |
| Specialist publication | Applied and Computational Harmonic Analysis |
| DOIs | |
| Publication status | Published - Jul 2021 |
| Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2021 Elsevier Inc.
Funding
Research supported in part by the Shenzhen R&D Program (JCYJ20180305124325555).Research supported in part by HKRGC Grants No. CUHK14301718, CityU Grant: 9380101, CRF Grant C1007-15G, AoE/M-05/12.The work of L. Shen was supported in part by the National Science Foundation under grant DMS-1913039.Research was supported in part by the Research Grants Council of Hong Kong (Project no. CityU 11301419) and City University of Hong Kong (Project no. 7005497).