A Effective Image Matching Method for Bubbly Flow Based on Wavelet Transform

Wang, Hongyi; Dong, Feng

doi:10.1109/cisp.2009.5303643

Cited by 1 publication

(1 citation statement)

References 10 publications

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“…As a new transform, the wavelet transform is built based on the traditional Fourier transform, which decomposes the signal into different frequency components, localizes the signal time-frequency, and extracts the signal by stretching the translation sequence for denoising. In 2009, Wang et al applied the wavelet multiresolution thresholding method to the denoising analysis of lidar return signals [3]. In 2011, Mao et al used the wavelet packet to finely extract the original lidar signal to make up for the shortcomings of the wavelet transform and effectively filter out the noise [4].…”

Section: Introductionmentioning

confidence: 99%

A Novel Lidar Signal-Denoising Algorithm Based on Sparrow Search Algorithm for Optimal Variational Modal Decomposition

Mao

et al. 2022

Remote Sensing

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Atmospheric lidar is susceptible to the influence of light attenuation, sky background light, and detector dark currents during the detection process. This results in a large amount of noise in the lidar return signal. To reduce noise and extract a useful signal, a novel denoising method combined with variational modal decomposition (VMD), the sparrow search algorithm (SSA) and singular value decomposition (SVD) is proposed. The SSA is used to optimize the number of decomposition layers K and the quadratic penalty factor α values of the VMD algorithm. Some intrinsic mode function (IMF) components obtained from the VMD-SSA decomposition are grouped and reconstructed according to the interrelationship number selection criterion. Then, the reconstructed signal is further denoised by combining the strong noise-reduction ability of SVD to obtain a clean lidar return signal. To verify the effectiveness of the VMD-SSA-SVD method, the method is compared and analysed with wavelet packet decomposition, empirical modal decomposition (EMD), ensemble empirical modal decomposition (EEMD), and adaptive noise-complete ensemble empirical modal decomposition (CEEMD), and its noise-reduction effect is considerably improved over that of the other four methods. The method can eliminate the complex noise in the lidar return signal while retaining all the details of the signal. The signal is not distorted, the waveform is smoother, and far-field noise interference can be suppressed. The denoised signal is closer to the real signal with higher accuracy, which shows the feasibility and the practicality of the proposed method.

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Section: Introductionmentioning

confidence: 99%

A Novel Lidar Signal-Denoising Algorithm Based on Sparrow Search Algorithm for Optimal Variational Modal Decomposition

Mao

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

show abstract

A Effective Image Matching Method for Bubbly Flow Based on Wavelet Transform

Cited by 1 publication

References 10 publications

A Novel Lidar Signal-Denoising Algorithm Based on Sparrow Search Algorithm for Optimal Variational Modal Decomposition

A Novel Lidar Signal-Denoising Algorithm Based on Sparrow Search Algorithm for Optimal Variational Modal Decomposition

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