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Proceedings of the 2nd International Conference on Machine Learning and Automation, CONF-MLA 2024, November 21, 2024, Adana, Turkey

Research Article

DSR-Net: Dynamic Star Map Denoising Algorithm Based on Deep Reinforcement Learning

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  • @INPROCEEDINGS{10.4108/eai.21-11-2024.2354638,
        author={Yifan  Zhao and Shiji  Song and Shaochen  Jiang},
        title={DSR-Net: Dynamic Star Map Denoising Algorithm Based on Deep Reinforcement Learning},
        proceedings={Proceedings of the 2nd International Conference on Machine Learning and Automation, CONF-MLA 2024, November 21, 2024, Adana, Turkey},
        publisher={EAI},
        proceedings_a={CONF-MLA},
        year={2025},
        month={3},
        keywords={star image image denoising deep reinforcement learning},
        doi={10.4108/eai.21-11-2024.2354638}
    }
    
  • Yifan Zhao
    Shiji Song
    Shaochen Jiang
    Year: 2025
    DSR-Net: Dynamic Star Map Denoising Algorithm Based on Deep Reinforcement Learning
    CONF-MLA
    EAI
    DOI: 10.4108/eai.21-11-2024.2354638
Yifan Zhao1, Shiji Song2,*, Shaochen Jiang1
  • 1: Xinjiang University, Xinjiang, China
  • 2: Tsinghua University, Beijing, China
*Contact email: shijis@mail.tsinghua.edu.cn

Abstract

As astronomical observation technology continues to progress, obtaining high-quality star maps provides us with valuable opportunities to explore the universe. However, The acquired star maps are often affected by various random noises, including speckle noise, Poisson noise, Impulse noise, Thermal noise,Reynolds noise, and Gaussian noise,etc. These noises degrade the image quality and limit the efficiency of scientific research. Traditional denoising methods are often limited in their effectiveness when faced with such complex noise and lack the ability to model the temporal features of dynamic star maps, making it difficult to handle the sparsity and complex background of dynamic star maps. Therefore, this paper introduces DSR-Net, a deep reinforcement learning-based dynamic star map denoising algorithm. The algorithm combines Convolutional Gated Recurrent Units (ConvGRU) and Region-based Reward Convolution (Rrc) modules, enabling it to capture the dynamic changes of star maps, effectively remove noise, and preserve important details in the star maps. Experimental results show that DSR-Net outperforms traditional denoising methods on multiple real dynamic star map datasets, providing an effective solution for improving the quality of star maps.

Keywords
star image image denoising deep reinforcement learning
Published
2025-03-11
Publisher
EAI
http://dx.doi.org/10.4108/eai.21-11-2024.2354638
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