Example-based hierarchical dictionary learning for image super-resolution based on image self-similarity
| Project/Area Number |
26889031
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| Research Category |
Grant-in-Aid for Research Activity Start-up
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| Allocation Type | Single-year Grants |
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| Research Field |
Communication/Network engineering
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| Research Institution | Nagaoka University of Technology |
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Principal Investigator |
Yoshida Taichi 長岡技術科学大学, 工学(系)研究科(研究院), 助教 (60737914)
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| Project Period (FY) |
2014-08-29 – 2016-03-31
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| Project Status |
Completed (Fiscal Year 2015)
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| Budget Amount *help |
¥2,730,000 (Direct Cost: ¥2,100,000、Indirect Cost: ¥630,000)
Fiscal Year 2015: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2014: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Keywords | 事例学習辞書 / 自己相似性 / 画像超解像 / 画像合成 / 画像処理 / 基底の疎表現 / 画像の自己相似性 / 画像の自己合同性 |
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| Outline of Final Research Achievements |
This research is focused on the construction of an example-based hierarchical dictionary and its application for image super-resolution based on image self-similarity. The proposed dictionary constructs a hierarchical dictionary and relates a degraded atom with a multiple fine atom to represent various images from one degraded image. Based on the image self-similarity, we search similar neighbors and approximate target and neighbor patches with the dictionary and same coefficients under weights calculated from similarities of them. The proposed method save calculation costs and produces fine resultant images.
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Report
(3 results)
Research Products
(7 results)
