| 研究課題/領域番号 |
19J13820
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| 研究機関 | 立命館大学 |
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研究代表者 |
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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| 研究期間 (年度) |
2019-04-25 – 2021-03-31
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| キーワード | Image processing / Artificial intelligence |
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| 研究実績の概要 |
The purpose of this research is to enhance the quality of remote sensing images using deep 3D convolutional neural networks incorporated with an atmospherically distorted image model.
In the first year, my research was mainly about the proposal and improvement of new technologies and methods.For the improvement of the image quality of large-size and high-dimensional images, based on the very hot artificial intelligence (AI) technology in recent years, I have proposed a lightweight deep learning method.
This method not only can maintain or exceed the existing methods in accuracy, but also has a faster processing speed and lower hardware requirements for saving the model, so it is more suitable for practical applications than traditional methods.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
So far, the progress of the research is basically in line with the original plan. I studied and referred to various deep learning methods in recent years, and then proposed and improved my original model. From the results, my method outperforms state-of-the-art methods in both accuracy and speed.
In addition, I have published or expected to publish three papers. The first one is about an improved multi-frame super-resolution method which has been published in the IET Image Processing. The second one is a proposed three-dimensional image processing model which has been submitted to a top international conference. The third one is my proposed lightweight super-resolution model which will be submitted to the IEEE Image Processing.
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| 今後の研究の推進方策 |
In this year, I am going to apply the proposed network to detection, segmentation or classification in geographic information and geological analysis. I plan to use the proposed method to automatically judge whether a region has landslides or major disasters based on satellite images before and after the earthquake.
According to the results, parameters and network architecture will be adjusted to optimize the proposed model. Then, I am going to do evaluations to demonstrate my method outperforms conventional methods in quality enhancement of remote sensing images. Finally, I will publish my findings in top international conferences and journals before I graduate.
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