A denoising algorithm based on neural network for side-scatter lidar signal

Ma Yuzhao; Zhang Yanfeng; Feng Shuai

doi:10.12086/oee.2023.220341

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Ma Y Z, Zhang Y F, Feng S. A denoising algorithm based on neural network for side-scatter lidar signal[J]. Opto-Electron Eng, 2023, 50(6): 220341. doi: 10.12086/oee.2023.220341

Citation:

Ma Y Z, Zhang Y F, Feng S. A denoising algorithm based on neural network for side-scatter lidar signal[J]. Opto-Electron Eng, 2023, 50(6): 220341. doi: 10.12086/oee.2023.220341

A denoising algorithm based on neural network for side-scatter lidar signal

1.
Tianjin Key Laboratory for Advanced Signal Processing, Civil Aviation University of China, Tianjin 300300, China
2.
Engineering Techniques Training Center, Civil Aviation University of China, Tianjin 300300, China

Fund Project: National Natural Science Foundation of China (U1833111), the Fundamental Research Funds for the Central Universities of China (3122019058)，and Tianjin Natural Science Foundation of China (21JCYBJC00680）

More Information

^*Corresponding author: yzma@cauc.edu.cn

Received Date 15 December 2022

Revised Date 21 March 2023

Accepted Date 24 March 2023

Published Date 25 June 2023

Abstract

Abstract

A side-scatter lidar is known to have evident advantages over other types of lidar in atmosphere detection. However, the signal of the side-scatter lidar may suffer from the noise as all other lidars. It is noted that the original signal of the side-scatter lidar is an image captured by a CCD camera. Therefore, denoising the side-scatter lidar signal may need more efforts than ordinary radar signals. In the paper, a denoising algorithm based on convolution neutral network is proposed for the side-scatter lidar signal. We combine the residual learning with batch standardization in the network. Further, attention mechanism and activation function in the network are optimized in order to improve the learning efficiency and the network output performance. Using the proposed algorithm, we successfully identify the noise and separate the noise from the simulated lidar signal. The signal-to-noise ratio is hence increased. Simulation results show that the peak signal-to-noise ratio is increased by over 5 dB using the proposed denoising algorithm. The relative error of signal is reduced to 9.62%. The proposed denoising algorithm based on the convolution neutral network is shown to be efficient for improving the side-scatter lidar signal, compared with the possible denoising algorithms based on wavelet transform and Wiener filtering.
- side-scatter lidar /
- signal processing /
- neural network /
- image denoising

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References

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Overview

Overview

A side-scatter lidar is known to have evident advantages over other types of lidar in atmosphere detection, especially for lower atmosphere. For a side-scatter lidar, a high-power laser is normally used as the light source. As the charge coupled device (CCD) optoelectronic detector is used to capture the light backscattered by the atmosphere. Correspondingly, the original side-scatter lidar signal is depicted as a 2D CCD image. The 2D CCD image of the side-scatter lidar may suffer from the noise as all other lidars. Therefore, denoising the side-scatter lidar signal may need more efforts than ordinary lidar signals. The extinction coefficient profile can be derived from the CCD image. With the help of other auxiliary techniques, atmosphere features such as wind speed and meteorological optical range can be obtained.

In the paper a denoising algorithm based on denoising convolution neutral network (DnCNN) is proposed for side-scatter lidar signal, called DnCNN+. The DnCNN+ uses scaled exponential linear units (SELU) as the activation function of the network in order to avoid the gradient explosion and gradient disappearance that might happen frequently in the traditional network. On the other hand, convolutional block attention module (CBAM) is used in the DnCNN+ to ensure the efficient allocation of the computation resources in the training process, hence increasing the learning efficiency. Furthermore, we introduce residual learning and batch standardization in the network to improve the network output performance.

For the denoising strategy, we identify the noise and separate the noise from the simulated lidar signal. The signal-to-noise ratio (SNR) is hence increased. The denoising performances of five methods, including wavelet transform soft threshold, wavelet transform hard threshold, Visual Geometry Group (VGG16), DnCNN, and DnCNN+, are evaluated for the signals with SNR of 0.01-0.03 dB. VGG16 is one of the classic convolution neutral networks. Peak signal to noise ratio (PSNR) and structural similarity (SSIM) are used to evaluate the denoising performance. Simulation results showe that the PSNR is increased by over 5 dB using the DnCNN+. The DnCNN+ has the best denoising performance in terms of PSNR and SSIM. Additionally, it is also seen that the DnCNN+ has smaller network loss than the methods using convolution neutral networks, VGG16, and DnCNN. Furthermore, the 1D signal photon number is retrieved from the CCD image. It is shown that the DnCNN+ has the smallest relative error of signal of 9.62%. The proposed denoising algorithm based on the convolution neutral network is shown to be efficient for improving the side-scatter lidar signal.