• Acta Photonica Sinica
  • Vol. 50, Issue 9, 0907001 (2021)
Mao YE1、2, Pinquan WANG1、2, Yiqiang ZHAO1、2、*, Rui CHEN1、2, Bin HU1、2, and Guoqing ZHOU3
Author Affiliations
  • 1The School of Microelectronics, Tianjin University, Tianjin300072, China
  • 2Tianjin Key Laboratory of Imaging and Sensing Microelectronic Technology, Tianjin30007, China
  • 3Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin University of Technology, Guilin, Guangxi541004, China
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    DOI: 10.3788/gzxb20215009.0907001 Cite this Article
    Mao YE, Pinquan WANG, Yiqiang ZHAO, Rui CHEN, Bin HU, Guoqing ZHOU. A Denoising Method for LiDAR Bathymetry System via Multidimensional Temporal-spatial Analysis[J]. Acta Photonica Sinica, 2021, 50(9): 0907001 Copy Citation Text show less
    Temporal-spatial correlation in multiple periods of echo signals
    Fig. 1. Temporal-spatial correlation in multiple periods of echo signals
    Framework of temporal-spatial joint denoising method
    Fig. 2. Framework of temporal-spatial joint denoising method
    Comparison on processing characteristic waveform by AGGF, Wavelet Filter, Wiener Filter and proposed method
    Fig. 3. Comparison on processing characteristic waveform by AGGF, Wavelet Filter, Wiener Filter and proposed method
    Results of processing continuous characteristic waveforms by basic denoising and final denoising
    Fig. 4. Results of processing continuous characteristic waveforms by basic denoising and final denoising
    Per-frame MSE and PSNR value of different methods
    Fig. 5. Per-frame MSE and PSNR value of different methods
    Comparison on processing simulation waveform by AGGF, Wavelet Filter, Wiener Filter and proposed method
    Fig. 6. Comparison on processing simulation waveform by AGGF, Wavelet Filter, Wiener Filter and proposed method
    Results of processing continuous simulation waveforms by basic denoising and final denoising
    Fig. 7. Results of processing continuous simulation waveforms by basic denoising and final denoising
    Seafloor point cloud of waveform set denoised by different methods
    Fig. 8. Seafloor point cloud of waveform set denoised by different methods
    Comparison on processing measured waveform by AGGF, Wavelet Filter, Wiener Filter, and proposed method
    Fig. 9. Comparison on processing measured waveform by AGGF, Wavelet Filter, Wiener Filter, and proposed method
    Results of processing continuous measured waveforms by basic denoising and final denoising
    Fig. 10. Results of processing continuous measured waveforms by basic denoising and final denoising
    Noise varianceEvaluation IndexOriginalAGGFWaveletWienerProposed method
    1.5MSE1.05×10-49.14×10-58.48×10-51.58×10-45.12×10-5
    PSNR-1.2787.8584.8055.4769.205
    2.5MSE1.95×10-41.01×10-41.18×10-41.72×10-45.16×10-5
    PSNR-7.7707.1933.0492.4749.099
    3.5MSE3.11×10-41.28×10-41.53×10-41.89×10-45.27×10-5
    PSNR-11.5876.9502.2592.1748.767
    4.5MSE4.55×10-41.33×10-41.91×10-42.07×10-45.30×10-5
    PSNR-17.5263.527-1.954-2.8577.664
    Table 1. MSE and PSNR values of different noise variance in simulated experiment
    Evaluation indexOriginalAGGFWaveletWienerProposed method
    SD/m0.10100.01590.02320.03410.0082
    MAE/m1.16580.58620.85090.88440.5130
    Table 2. SD and MAE values between depth extracted from denoised waveform set and true depth
    Mao YE, Pinquan WANG, Yiqiang ZHAO, Rui CHEN, Bin HU, Guoqing ZHOU. A Denoising Method for LiDAR Bathymetry System via Multidimensional Temporal-spatial Analysis[J]. Acta Photonica Sinica, 2021, 50(9): 0907001
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