Hokkaido University Collection of Scholarly and Academic Papers >
Graduate School of Information Science and Technology / Faculty of Information Science and Technology >
Peer-reviewed Journal Articles, etc >
Adaptive example-based super-resolution using kernel PCA with a novel classification approach
This item is licensed under:Creative Commons Attribution 2.0 Generic
Title: | Adaptive example-based super-resolution using kernel PCA with a novel classification approach |
Authors: | Ogawa, Takahiro Browse this author →KAKEN DB | Haseyama, Miki Browse this author →KAKEN DB |
Keywords: | Super-resolution | resolution enhancement | image enlargement | Kernel PCA | classification |
Issue Date: | 22-Dec-2011 |
Publisher: | Springer |
Journal Title: | EURASIP Journal on Advances in Signal Processing |
Volume: | 2011 |
Start Page: | 138 |
Publisher DOI: | 10.1186/1687-6180-2011-138 |
Abstract: | An adaptive example-based super-resolution (SR) using kernel principal component analysis (PCA) with a novel classification approach is presented in this paper. In order to enable estimation of missing high-frequency components for each kind of texture in target low-resolution (LR) images, the proposed method performs clustering of high-resolution (HR) patches clipped from training HR images in advance. Based on two nonlinear eigenspaces, respectively, generated from HR patches and their corresponding low-frequency components in each cluster, an inverse map, which can estimate missing high-frequency components from only the known low-frequency components, is derived. Furthermore, by monitoring errors caused in the above estimation process, the proposed method enables adaptive selection of the optimal cluster for each target local patch, and this corresponds to the novel classification approach in our method. Then, by combining the above two approaches, the proposed method can adaptively estimate the missing high-frequency components, and successful reconstruction of the HR image is realized. |
Rights: | http://creativecommons.org/licenses/by/2.0/ |
Type: | article |
URI: | http://hdl.handle.net/2115/48727 |
Appears in Collections: | 情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
|
Submitter: 小川 貴弘
|