Stochastic Convex Optimization Algorithm for Image Processing and Transfer
| Project/Area Number |
15K06078
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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 | Chiba Institute of Technology |
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Principal Investigator |
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| Project Period (FY) |
2015-04-01 – 2019-03-31
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| Project Status |
Completed (Fiscal Year 2018)
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| Budget Amount *help |
¥4,940,000 (Direct Cost: ¥3,800,000、Indirect Cost: ¥1,140,000)
Fiscal Year 2017: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
Fiscal Year 2016: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2015: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | 画像処理 / 凸最適化 / 確率的最適化 / 信号処理 / 画像復元 / 確率的凸最適化 |
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| Outline of Final Research Achievements |
We proposed new image restoration methods based on convex and non-convex optimization by using stochastic optimization approach. The main research topics are an image deblurring and restoration method from compressed sampling via stochastic convex optimization, a color image restoration method by weighted tensor nuclear norm minimization, a color image completion method by weighted tensor Schatten-p norm minimization, and a texture recovery method by statistics estimation for image denoising. We publish the results of these research topics at the corresponding international conferences.
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| Academic Significance and Societal Importance of the Research Achievements |
画像からのノイズ除去やぼけの復元などを含む画像復元技術は，デジタルカメラやテレビなど社会のあらゆる場面で利用されている．近年，数理最適化を用いた画像復元手法が高い性能を持つことが明らかとなっているが，計算量が大きいことがそれらの手法を実際に利用する際の課題となっていた．本研究は，計算量の削減を目指して提案された確率的最適化手法を，数理最適化を用いた画像復元技術に応用することを検討し，当該課題の解決を試みるものである．
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Report
(5 results)
Research Products
(34 results)
