Underwater Acoustic Point-cloud Filtering via Adaptive Unsharp Masking

Abstract

Owing to the complex water environment, the acoustic point-cloud model formed by the detection method based on acoustic reflection mechanism is inevitably disturbed by the noise, which seriously affects the reconstruction effect of the underwater targets. Distinguishing between geometric features and noise is of paramount importance for the underwater point-cloud model filtering. Inspired by the classic image detail enhancement method of unsharp masking, we take the geometric coordinate information of the point as the research object and design a geometric feature-preserving adaptive unsharp masking filtering for the underwater point-cloud model. First, the proposed method directly performed a low-pass filtering using the neighborhood information to obtain the main structure of the input point-cloud model. Second, the detail layer was yielded by the difference between the input point-cloud model and the base layer. Third, the different scaling factors measuring the importance of the points with respect to the whole base layer were used to adaptively enhance the detail layer. Experimental results show that the proposed algorithm can effectively remove noise while maintaining the geometric characteristics of the model, which is obviously better than other comparison methods.