An Example Based Non-local Mean Regularization For Image Super-resolution
Li Chuanhai, Hu Ruimin, Xia Yang
Available Online November 2013.
- https://doi.org/10.2991/icmt-13.2013.74How to use a DOI?
- Image super-resolution, nonlocal mean regularization..
- Image super-resolution (SR) is a very useful technique for visual surveillance, high-definition TV and medical image processing. In the traditional method, the inferred high-resolution image patch is represented as a linear combination of dictionaries obtained by training images, and least square estimation or sparse regularization is used to find better solution. However, the methods proposed so far neglect the prior knowledge about local structure similarity in natural images. In this paper we introduce an adaptive regularization terms into LLE based SR framework. First, an example based image nonlocal mean regularization term is learned from the dataset of example image patches, which captures the local structure similarity between the input image and training images. Then, the image nonlocal self-similarity is used as another regularization term. In addition, we propose an iterative optimization framework to find the latent HR image. Ex-perimental results on real surveillance images demonstrate the superiority of the proposed method over some state-of-the-art image SR approaches.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Li Chuanhai AU - Hu Ruimin AU - Xia Yang PY - 2013/11 DA - 2013/11 TI - An Example Based Non-local Mean Regularization For Image Super-resolution PB - Atlantis Press SP - 597 EP - 604 SN - 1951-6851 UR - https://doi.org/10.2991/icmt-13.2013.74 DO - https://doi.org/10.2991/icmt-13.2013.74 ID - Chuanhai2013/11 ER -