Randomized hyperspectral imaging: Foundations and applications
| Project/Area Number |
17K12710
|
|---|
| Research Category |
Grant-in-Aid for Young Scientists (B)
|
| Allocation Type | Multi-year Fund |
|---|
| Research Field |
Perceptual information processing
|
|---|
| Research Institution | Tokyo Institute of Technology |
|---|
Principal Investigator |
Ono Shunsuke 東京工業大学, 情報理工学院, 准教授 (60752269)
|
|---|
| Project Period (FY) |
2017-04-01 – 2020-03-31
|
| Project Status |
Completed (Fiscal Year 2019)
|
| Budget Amount *help |
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2019: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2018: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2017: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
|
|---|
| Keywords | ハイパースペクトルイメージング / 乱択最適化 / 正則化 / 凸最適化 / ハイパースペクトル画像 / 確率的最適化 / 画像復元 |
|---|
| Outline of Final Research Achievements |
In this research project, we aim to develop a randomized hyperspectral imaging framework with the following three objectives: 1. we design a regularization function for hyperspectral imaging that can be optimized in a randomized manner, 2. we construct a randomized optimization algorithm for solving a broad class of objective functions with constraints associated with hyperspectral imagin, 3. we apply the framework to various problems of hyperspectral imaging, and we published scientific papers about them.
|
| Academic Significance and Societal Importance of the Research Achievements |
本研究の主要な成果は，既存のハイパースペクトルイメージング技術に比べて飛躍的に低計算量な手法であり，当該技術が重要な役割を果たすサイエンス・工学の諸分野の発展に大きく貢献するものである．特に，計算量の観点から実現が困難であった画期的なハイパースペクトルイメージング技術の応用，例えば超高解像度リモートセンシングなどの実現に向けて重要な基盤技術の一つとなる．
|
Report
(4 results)
Research Products
(50 results)
