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| ファイル | 記述 | サイズ | フォーマット | |
|---|---|---|---|---|
| Kanemura_2010_IEEETIP.pdf | 1.03 MB | Adobe PDF | 見る/開く |
| タイトル: | Sparse Bayesian learning of filters for efficient image expansion |
| 著者: | Kanemura, Atsunori Maeda, Shin-ichi  Ishii, Shin |
| 著者名の別形: | 兼村, 厚範 |
| キーワード: | automatic relevance determination (ARD) image expansion image interpolation resolution synthesis (RS) sparse Bayesian estimation variational estimation |
| 発行日: | Jun-2010 |
| 出版者: | IEEE |
| 誌名: | IEEE transactions on image processing |
| 巻: | 19 |
| 号: | 6 |
| 開始ページ: | 1480 |
| 終了ページ: | 1490 |
| 抄録: | We propose a framework for expanding a given image using an interpolator that is trained in advance with training data, based on sparse Bayesian estimation for determining the optimal and compact support for efficient image expansion. Experiments on test data show that learned interpolators are compact yet superior to classical ones. |
| 著作権等: | (c) 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. |
| URI: | http://hdl.handle.net/2433/123421 |
| DOI(出版社版): | 10.1109/TIP.2010.2043010 |
| PubMed ID: | 20215080 |
| 出現コレクション: | 学術雑誌掲載論文等 |
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