Chinese Journal of Lasers, Volume. 48, Issue 11, 1109001(2021)
Shallow-Water LiDAR Echo Signal Denoising Based on Improved EWT
Figures & Tables(11)
Fig. 2. Shallow-water LiDAR echo signal model. (a) Echo signal without noise; (b) echo signal with Gaussian white noise (SNR is 20 dB)
Fig. 3. Processing results of signal model using improved EWT. (a) Signal with noise and its EMFs (SNR is 20 dB); (b) comparison of signal with noise before and after denosing
Fig. 4. Shallow-water LiDAR experimental system. (a) Principle diagram; (b) practical map
Fig. 5. Measured waveforms under different water depths. (a) ΔdL1=2.127 m; (b) ΔdL1=1.621 m; (c) ΔdL1=1.434 m; (d) ΔdL1= 0.774 m
Fig. 6. Denoised waveforms under different water depths. (a) ΔdL1=2.127 m; (b) ΔdL1=1.621 m;(c) ΔdL1=1.434 m; (d) ΔdL1= 0.774 m
Fig. 7. Spectral segmentation under different boundary detect methods. (a) LocalMax; (b) LocalMaxMin; (c) Scalespace; (d) improved EWT
Fig. 8. EMFs under different boundary detect methods. (a) LocalMax; (b) LocalMaxMin; (c) Scalespace; (d) improved EWT
Table 1. Comparison of denoising performance parameters
View tableTable 1. Comparison of denoising performance parameters
Type of denoising algorithm Denoising algorithm MSE / m MAE / m SNR / dB PSNR / dB T /s Cross correlation WT Soft thresholding 0.397 0.478 19.706 29.773 1.010 0.993 Hard thresholding 0.363 0.468 20.097 30.164 1.040 0.993 Fixed thresholding 0.688 0.625 17.321 27.388 1.150 0.987 EMD 0.306 0.393 20.843 30.909 1.300 0.995 EEMD 0.321 0.434 20.634 30.701 3.890 0.994 CEEMDAN 0.216 0.359 22.350 32.417 5.761 0.996 Type of denoising algorithm Denoising algorithm MSE / m MAE / m SNR / dB PSNR / dB T /s Cross correlation EWT LocalMax 0.867 0.754 16.315 26.382 0.590 0.988 LocMaxMin 1.045 0.854 15.504 25.571 0.510 0.981 Scalespace 0.288 0.438 21.093 31.159 0.770 0.994 Improved EWT 0.183 0.326 23.071 33.138 0.560 0.997
Table 2. Echo signal decomposition
View tableTable 2. Echo signal decomposition
Denoising method Group Surface echo Bottom echo ΔdL2 /m ΔdL1 /m Position / ns Height / V Width / ns Position / ns Height / V Width / ns CEEMDAN 1 19.100 0.322 1.995 38.155 0.490 2.127 2.149 2.127 2 18.301 0.186 1.647 32.010 0.238 2.375 1.546 1.621 3 18.136 0.408 3.774 30.342 0.606 4.697 1.376 1.434 4 15.855 0.258 2.072 22.767 0.329 2.480 0.776 0.774 LocalMax boundary detect method 1 23.672 0.480 6.502 42.001 0.400 5.673 2.067 2.127 2 24.068 0.098 4.845 33.256 0.168 4.117 1.036 1.621 3 18.593 0.473 3.841 29.067 0.594 2.749 1.181 1.434 4 16.048 0.321 1.881 22.585 0.861 2.500 0.737 0.774 Denoising method Group Surface echo Bottom echo ΔdL2 /m ΔdL1 /m Position / ns Height / V Width / ns Position / ns Height / V Width / ns Improved EWT method 1 22.708 0.445 5.003 40.684 0.527 5.136 2.027 2.127 2 19.467 0.151 2.698 33.992 0.231 3.195 1.638 1.621 3 15.563 0.446 2.258 28.726 0.644 2.379 1.484 1.434 4 16.084 0.386 2.110 22.284 0.848 2.777 0.699 0.774
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Tingting Wei, Jiazhi Yang, Guoqing Zhou, Xiang Zhou, Xueqin Nong. Shallow-Water LiDAR Echo Signal Denoising Based on Improved EWT[J]. Chinese Journal of Lasers, 2021, 48(11): 1109001
Paper Information
Category: holography and information processing
Received: Oct. 18, 2020
Accepted: Dec. 23, 2020
Published Online: May. 20, 2021
The Author Email: Yang Jiazhi (1791197996@qq.com)
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