Denoising method for a lidar bathymetry system based on a low-rank recovery of non-local data structures.

The lidar bathymetry system (LBS) echo is often contaminated by mixed noise, which severely affects the accuracy of measuring sea depth. The denoising algorithm based on a single echo cannot deal with the decline of the signal-to-noise ratio and impulse noise caused by sea waves and abrupt terrain changes. Therefore, we propose a new denoising method for LBS based on non-local structure extraction and the low-rank recovery model. First, the high-frequency noise is eliminated based on the multiple echo in a small neighborhood, and then the matrix is constructed based on the processing results in a larger range. Then, we make full use of the structural similarity between LBS echoes by transforming the echo denoising issues into low-rank matrix restoration to further eliminate the noise. The experimental results show that this method can effectively preserve the seafloor signal and eliminate the mixed noise.

Robust Noise Suppression Technique for a LADAR System via Eigenvalue-Based Adaptive Filtering.

The laser detection and ranging system (LADAR) is widely used in various fields that require 3D measurement, detection, and modeling. In order to improve the system stability and ranging accuracy, it is necessary to obtain the complete waveform of pulses that contain target information. Due to the inevitable noise, there are distinct deviations between the actual and expected waveforms, so noise suppression is essential. To achieve the best effect, the filters' parameters that are usually set as empirical values should be adaptively adjusted according to the different noise levels. Therefore, we propose a novel noise suppression method for the LADAR system via eigenvalue-based adaptive filtering. Firstly, an efficient noise level estimation method is developed. The distributions of the eigenvalues of the sample covariance matrix are analyzed statistically after one-dimensional echo data are transformed into matrix format. Based on the boundedness and asymptotic properties of the noise eigenvalue spectrum, an estimation method for noise variances in high dimensional settings is proposed. Secondly, based on the estimated noise level, an adaptive guided filtering algorithm is designed within the gradient domain. The optimized parameters of the guided filtering are set according to an estimated noise level. Through simulation analysis and testing experiments on echo waves, it is proven that our algorithm can suppress the noise reliably and has advantages over the existing relevant methods.