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2020 Fiscal Year Annual Research Report

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

Research Project

Project/Area Number 19J13820
Research InstitutionRitsumeikan University

Principal Investigator

LI YINHAO  立命館大学, 情報理工学研究科, 特別研究員(DC2)

Project Period (FY) 2019-04-25 – 2021-03-31
KeywordsImage Processing
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.

Research Progress Status

令和2年度が最終年度であるため、記入しない。

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。

  • Research Products

    (4 results)

All 2021 2020 Other

All Int'l Joint Research (1 results) Journal Article (1 results) (of which Int'l Joint Research: 1 results,  Peer Reviewed: 1 results) Presentation (2 results) (of which Int'l Joint Research: 2 results)

  • [Int'l Joint Research] Zhejiang Lab/Zhejiang University/Dalian University of Technology(中国)

    • Country Name
      CHINA
    • Counterpart Institution
      Zhejiang Lab/Zhejiang University/Dalian University of Technology
  • [Journal Article] VolumeNet: A Lightweight Parallel Network for Super-Resolution of MR and CT Volumetric Data2021

    • Author(s)
      Yinhao Li, Yutaro Iwamoto, Lanfen Lin, Rui Xu, Ruofeng Tong, Yen-Wei Chen
    • Journal Title

      IEEE Transactions on Image Processing

      Volume: - Pages: -

    • DOI

      10.1109/TIP.2021.3076285

    • Peer Reviewed / Int'l Joint Research
  • [Presentation] Parallel-Connected Residual Channel Attention Network for Remote Sensing Image Super-Resolution2020

    • Author(s)
      Yinhao Li, Yutaro Iwamoto, Lanfen Lin, Yen-Wei Chen
    • Organizer
      ACCV Workshop on Machine Learning and Computing for Visual Semantic Analysis
    • Int'l Joint Research
  • [Presentation] A 3D Shrinking-and-Expanding Module with Channel Attention for Efficient Deep Learning-Based Super-Resolution2020

    • Author(s)
      Yinhao Li, Yutaro Iwamoto, Lanfen Lin, Yen-Wei Chen
    • Organizer
      Innovation in Medicine and Healthcare
    • Int'l Joint Research

URL: 

Published: 2021-12-27  

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