Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5449015 | Optics Communications | 2017 | 8 Pages |
Abstract
Since the single image super-resolution (SR) is an extremely ill posed problem, we introduce a novel auto-regressive moving average (ARMA) model-based regularization term into the spare representation-based framework to deal with it in this paper. In our framework, we have a dual regularization. Firstly, we use the ARMA models trained from external samples to establish a regularization term. ARMA model-based regularization serves as a local constraint. Secondly, we introduce the nonlocal (NL) self-similarity as another regularization term. Both the local and the NL regularizations are unified into the sparse representation-based framework. Finally, extensive experiments verify the effectiveness of the proposed method.
Related Topics
Physical Sciences and Engineering
Materials Science
Electronic, Optical and Magnetic Materials
Authors
Weirong Liu, Chaopeng Zhang, Jie Liu, Chaorong Liu,