A study on reflectance estimation for hyperspectral imaging and its application to image restoration
| Project/Area Number |
16H07021
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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 |
Perceptual information processing
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| Research Institution | Kagawa University |
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Principal Investigator |
Matsuoka Ryo 香川大学, 工学部, 助教 (40780391)
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| Project Period (FY) |
2016-08-26 – 2018-03-31
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| Project Status |
Completed (Fiscal Year 2017)
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| Budget Amount *help |
¥2,990,000 (Direct Cost: ¥2,300,000、Indirect Cost: ¥690,000)
Fiscal Year 2017: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2016: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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| Keywords | スパースコーディング / 凸最適化 / 非凸最適化 / 画像復元 / ハイパースペクトル画像 / 分光反射特性 / 固有画像分解 |
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| Outline of Final Research Achievements |
Storing scene reflectance, which independents to environmental light sources, as an image (reflectance estimation) is an important issue for the development of digital archive and medical technology. However, a reflectance estimation technique still has not been established for hyperspectral imaging having high spectral resolution. A major factor is a lack of light intensity when taking an HS image, and it causes sensor noise, focus blur, under-exposure.
To solve these problems, this research established novel multiple image blending methods based on convex optimization and sparse coding techniques.
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Report
(3 results)
Research Products
(16 results)
