2013 Fiscal Year Final Research Report
Learning of translation-invariant image model with subspace sparsity and its applications to image processing
| Project/Area Number |
23500210
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Perception information processing/Intelligent robotics
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| Research Institution | Chiba Institute of Technology (2012-2013)
Osaka University (2011) |
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Principal Investigator |
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| Project Period (FY) |
2011 – 2013
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| Keywords | 画像情報処理 / 基底系学習 / スパース信号表現 / スパース正則化 / 教師なし学習 |
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| Research Abstract |
In this study, image models based on subspace sparsity is proposed. Both the synthesis and analysis image model are proposed for image recovery problem. For image synthesis model, an image is approximated as a linear combination of translated generating atoms, which represent micro structures of the image. In order to learn the local structures, the sparsity is imposed on the numbers of the translated atoms and the subspaces that are spanned by the generating atoms simultaneously. The learnt generating atoms are successfully applied to single-image super resolution problem. For image analysis model, nonlinear filters are introduced to definition of the subspace of images. The image recovery problem is achieved by minimizing the norm that is defined between the subspace of the image and the degraded image. The proposed nonlinear analysis model is applied to denoisng problem. The proposed model obtains superior results comparing with the linear analysis model.
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Research Products
(11 results)
