Image component analysis based on sparse signal decomposition and its applications to image processing
| Project/Area Number |
20500154
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Perception information processing/Intelligent robotics
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| Research Institution | Osaka University |
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Principal Investigator |
NAKASHIZUKA Makoto Osaka University, 大学院・基礎工学研究科, 准教授 (10251787)
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| Project Period (FY) |
2008 – 2010
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| Project Status |
Completed (Fiscal Year 2010)
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| Budget Amount *help |
¥3,770,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥870,000)
Fiscal Year 2010: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2009: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2008: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
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| Keywords | 画像処理 / 信号分解 / 画像復元 / スパース信号表現 / 基底学習 / 教師なし学習 / 画像生成モデル / 画像特徴抽出 / スパースコーディング / 雑音除去 / マセマティカルモルフォロジ / 画像インペインティング |
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| Research Abstract |
In this study, an image decomposition into a set of components with different features is proposed. The proposed decomposition is achieved with learning of representative micro structures of an image. The image is decomposed into components ; each of them is generated by a linear combination of the translated microstructures. In the learning, the sparsity prior is imposed on the occurrences of the microstructures. The learning of the representative structure is applied to image inpainting, which is recovery of the original image from a degraded image with missing pixels. By using the learning, the micro structures of the original image are successfully learnt from the degraded image. Simultaneously, the missing pixels are recovered with the learnt structures.
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Report
(4 results)
Research Products
(24 results)
