DENOISING OF LIDAR ECHO SIGNAL BASED ON WAVELET ADAPTIVE THRESHOLD METHOD

Abstract. The wavelet threshold method is widely used in signal denoising. However, traditional hard threshold method or soft threshold method is deficient for depending on fixed threshold and instability. In order to achieve efficient denoising of echo signals, an adaptive wavelet threshold denoising method, absorbing the advantages of the hard threshold and the soft threshold, is proposed. Based on the advantages of traditional threshold method, new threshold function is continuous, steerable and flexibly changeable by adjusting two parameters. The threshold function is flexibly changed between the hard threshold and the soft threshold function by two parameter adjustments. According to the Stein unbiased risk estimate (SURE), this new method can determine thresholds adaptively. Adopting different thresholds adaptively at different scales, this method can automatically track noise, which can effectively remove the noise on each scale. Therefore, the problems of noise misjudgement and incomplete denoising can be solved, to some extent, in the process of signal processing. The simulation results of MATLAB show that compared with hard threshold method and soft threshold method, the signal-to-noise ratio (SNR) of the proposed de-noising method is increased by nearly 2dB, and 4dB respectively. It is safely to conclude that, when background noise eliminated, the new wavelet adaptive threshold method preserves signal details effectively and enhances the separability of signal characteristics.

[1]  Oktay Baysal,et al.  Flash Lidar Sensor Using Fiber-Coupled APDs , 2015, IEEE Sensors Journal.

[2]  Rosemary A. Renaut,et al.  Wavelet-Based De-noising of Positron Emission Tomography Scans , 2012, J. Sci. Comput..

[3]  Ramesh Kumar Sunkaria,et al.  Despeckling of ultrasound images using novel adaptive wavelet thresholding function , 2018, Multidimens. Syst. Signal Process..

[4]  Guang Deng,et al.  A signal denoising algorithm based on overcomplete wavelet representations and Gaussian models , 2007, Signal Process..

[5]  P. C. Cortez,et al.  A new approach to QRS segmentation based on wavelet bases and adaptive threshold technique. , 2007, Medical engineering & physics.

[6]  David L. Donoho,et al.  De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.

[7]  Abdourrahmane M. Atto,et al.  Wavelet shrinkage: unification of basic thresholding functions and thresholds , 2011, Signal Image Video Process..

[8]  Quan Pan,et al.  Two denoising methods by wavelet transform , 1999, IEEE Trans. Signal Process..

[9]  Hossein Nezamabadi-pour,et al.  Image denoising in the wavelet domain using a new adaptive thresholding function , 2009, Neurocomputing.

[10]  J. Mohanalin,et al.  Wavelet based seismic signal de-noising using Shannon and Tsallis entropy , 2012, Comput. Math. Appl..

[11]  G. Nason.  Wavelet Shrinkage using Cross-validation , 1996 .

[12]  Tae Jin Kang,et al.  Automated defect detection system using wavelet packet frame and Gaussian mixture model. , 2006, Journal of the Optical Society of America. A, Optics, image science, and vision.

[13]  Nicolas Baghdadi,et al.  Wa-LiD: A New LiDAR Simulator for Waters , 2012, IEEE Geoscience and Remote Sensing Letters.

[14]  I. Osorio,et al.  Intrinsic time-scale decomposition: time–frequency–energy analysis and real-time filtering of non-stationary signals , 2007, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[15]  Jian Li,et al.  Wavelet de-noising of partial discharge signals based on genetic adaptive threshold estimation , 2012, IEEE Transactions on Dielectrics and Electrical Insulation.

[16]  Adhemar Bultheel,et al.  Generalized cross validation for wavelet thresholding , 1997, Signal Process..

[17]  Celia Shahnaz,et al.  Noisy speech enhancement based on an adaptive threshold and a modified hard thresholding function in wavelet packet domain , 2013, Digit. Signal Process..

[18]  Guoqing Zhou,et al.  Advances and perspectives in bathymetry by airborne lidar , 2015, Intelligent Earth Observing Systems.

[19]  C. Stein.  Estimation of the Mean of a Multivariate Normal Distribution , 1981 .

[20]  Stéphane Mallat,et al.  Singularity detection and processing with wavelets , 1992, IEEE Trans. Inf. Theory.