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.