Due to the high sensitivity of photon counting LiDAR, there is a lot of noise in the collected echo data. Signal identification and removal of relevant noise are the prerequisites for the subsequent application of three-dimensional point cloud data. As a result, a quick extraction of the signal photons is extremely important in practice. Shanghai Institute of Technical Physics independently developed multichannel photon counting LiDAR to collect data near sea surface. In this experiment, after preprocessing, we first enhanced the local distance statistical denoising technique. The enhanced algorithm was then compared with the original algorithm and two classical clustering-based algorithms. The findings demonstrate that the proposed algorithm has a noise removal accuracy of 97.80% and running time of 0.46 s, which are superior to that of the original algorithm and two clustering-based algorithms; it, thus, meets the practical requirements for the fast extraction of multichannel photon counting LiDAR information.