大気歪み画像モデルを組み込んだ深層学習によるリモートセンシング画像の画質改善
| Project/Area Number |
19J13820
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| Research Category |
Grant-in-Aid for JSPS Fellows
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| Allocation Type | Single-year Grants |
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| Section | 国内 |
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| Review Section |
Basic Section 61010:Perceptual information processing-related
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| Research Institution | Ritsumeikan University |
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Principal Investigator |
LI YINHAO 立命館大学, 情報理工学研究科, 特別研究員(DC2)
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| Project Period (FY) |
2019-04-25 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥1,700,000 (Direct Cost: ¥1,700,000)
Fiscal Year 2020: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 2019: ¥900,000 (Direct Cost: ¥900,000)
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| Keywords | Image Processing / Image processing / Artificial intelligence |
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| Outline of Research at the Start |
本研究では、主に大気歪みリモートセンシング画像の画質改善に注目しています。宇宙から小さな物体を認識することは非常に困難でやりがいのある作業なので、従来の研究ではめったに関与しません。近年人工知能は画質を向上させるための非常に効果的な方法であり、画像処理に広く採用されています。 したがって、本研究は人工知能を用いた画質改善技術及び提案した大気歪み画像モデルに基づいてより詳細な研究を行うことを計画しています。
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| Outline of Annual Research Achievements |
The purpose of this research is to enhance the quality of remote sensing images using deep 3D convolutional neural networks (CNNs).
Improving the performance of CNNs-based methods with a few parameters and short processing time is very difficult, although it is a desirable task to improve the quality of remote sensing images. Thus, I proposed a new 2D CNN network using a parallel-connected backbone, the architecture of which consists residual connections and channel-attention mechanism. This work has been accepted by ACCV Workshop on Machine Learning and Computing for Visual Semantic Analysis, 2020.
In addition, I proposed a new multi-spectral image fusion method using a combination of the proposed lightweight 3D VolumeNet model (which has been accepted by IEEE Transactions on Image Processing, 2021) and the texture transfer method using other modality high-resolution images. The experimental results show that the proposed method outperforms the existing methods in terms of objective accuracy assessment, efficiency and visual subjective evaluation. Consequently, I plan to submit this work to the IEEE Transactions on Geoscience and Remote Sensing.
Overall, the progress of the research is basically in line with the original plan. I studied and referred to various state-of-the-art methods and then built my original models. It is worth noting that the proposed methods not only can exceed the existing methods in accuracy, but also has a faster processing speed and lower hardware requirements for saving the model, so they are suitable for practical applications.
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| Research Progress Status |
令和2年度が最終年度であるため、記入しない。
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| Strategy for Future Research Activity |
令和2年度が最終年度であるため、記入しない。
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Report
(2 results)
Research Products
(8 results)
