2016 Fiscal Year Final Research Report
Learning-Based Design and Implementation of Non-separable Oversampled Lapped Transforms for Multidimensional Signal Restoration
| Project/Area Number | 26420347 |
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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 |
Communication/Network engineering
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| Research Institution | Niigata University |
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Principal Investigator |
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| Project Period (FY) |
2014-04-01 – 2017-03-31
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| Keywords | 冗長変換 / スパース表現 / ボリュームデータ復元 / 辞書学習 / GPGPU実装 / 確率的勾配降下法 / タイトフレーム / 画像処理 |
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| Outline of Final Research Achievements |
In this project, we proposed a multidimensional transform with the redundant, non-separable, overlapped, symmetric, compact-supported and tight property. We also conducted the theoretical analysis, design, implementation, and application development. First, we extended the existing non-separable lapped orthogonal transform to redundant configuration and clarify its properties. As well, we proposed an example-based learning design method and showed its effectiveness. In addition, we showed the possibility of real-time processing through GPGPU/FPGA implementation. Besides, it was applied to image/volumetric data restoration and validity was confirmed. We also extended the proposed transform to complex coefficient type as preparation for application development to complex image restoration processing.
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| Free Research Field |
信号処理
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