Laser Journal, Volume. 46, Issue 3, 154(2025)

Sparse denoising method for LiDAR images based on deep learning

WANG Yonghong, WANG Xiaofeng, and LIU Ruiqing
Author Affiliations
  • Xinzhou Normal University, Xinzhou Shanxi 034000, China
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    References(7)

    [1] [1] Ke X, Zhang T, Shao Z. Scale-aware dimension-wise attention network for small ship instance segmentation in synthetic aperture radar images[J]. Journal of Applied Remote Sensing, 2023, 17(4): 046504-046504.

    [3] [3] Chen M, Wang X, Yu Y, et al. Enhancement algorithm for separability of inland water body in synthetic aperture radar image via sparse representation and image fusion[J]. International Journal of Remote Sensing, 2022, 43(1): 167-195.

    [5] [5] Liu J, Liu R. Synthetic aperture radar image despeckling using convolutional neural networks in wavelet domain[J]. IET Image Processing, 2023, 17(9): 2561-2574.

    [8] [8] Liu Y, Cao N, Mao M, et al. Shaped offset 8PSK and coded shaped offset 8PSK with improved spectrum efficiency for satellite communication[J]. IET Communications, 2021, 15(2): 305-316.

    [10] [10] Khan R A, Luo Y, Wu F X. Multi - scale GAN with residual image learning for removing heterogeneous blur[J]. IET Image Processing, 2022, 16(9): 2412-2431.

    [12] [12] Lyu J, Chen Z, Luan X, et al. Denoising stacked autoencoders - based near - infrared quality monitoring method via robust samples evaluation[J]. The Canadian Journal of Chemical Engineering, 2023, 101(5): 2693-2703.

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    WANG Yonghong, WANG Xiaofeng, LIU Ruiqing. Sparse denoising method for LiDAR images based on deep learning[J]. Laser Journal, 2025, 46(3): 154

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    Paper Information

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    Received: Aug. 14, 2024

    Accepted: Jun. 12, 2025

    Published Online: Jun. 12, 2025

    The Author Email:

    DOI:10.14016/j.cnki.jgzz.2025.03.154

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