Research on inference of visual images from incomplete neural activity data in primary visual cortex
| Project/Area Number |
21700263
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Single-year Grants |
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| Research Field |
Sensitivity informatics/Soft computing
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| Research Institution | Waseda University |
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Principal Investigator |
INOUE Masato 早稲田大学, 理工学術院, 准教授 (70376953)
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| Project Period (FY) |
2009 – 2011
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| Project Status |
Completed (Fiscal Year 2011)
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| Budget Amount *help |
¥4,030,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥930,000)
Fiscal Year 2011: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2010: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2009: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | 確率的情報処理 / 画像情報処理 |
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| Research Abstract |
In the research process, we found that super resolution technique, which infers a high resolution image from low resolution images, is more substantial and general technique of the inference of visual images from visual cortex data. Afterward, we researched the super resolution and succeeded to develop the best method in low noise level situation. The method can analytically and approximately determine the optimal estimator over the original image model of the compound Markov random field prior. Such problem had been an unsolved problem for a long time.
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Report
(4 results)
Research Products
(24 results)
