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Adaptive example-based super-resolution using kernel PCA with a novel classification approach

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Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/48727

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

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