The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) can obtain underwater elevation due to its strong penetration ability. However, the photons recorded by ICESat-2 include a large amount of noise that needs to be removed. Although density-based clustering methods can finish signal photon extraction, heterogeneous density and weak connectivity in photon data distribution impede their denoising performance, especially for sparse signals in deep water and drastic topographic change areas. In this paper, a novel fused denoising method based on the local outlier factor and inverse distance metric is proposed to overcome the above problems. The local outlier factor and inverse distance metric are calculated based on K-nearest neighbors (KNNs), taking into account not only the difference in density but also the directional uniformity of the data distribution. Using six trajectories under various seabed topographies, the proposed method is compared with state-of-the-art ICESat-2 photon denoising algorithms and official ATL03 results. The results indicate that the overall accuracy of the proposed method can surpass 96%, and the proposed method maintains higher recall but also has a lower false positive rate. Compared with the results of other methods, the proposed method can better adopt areas with abrupt topographic changes and deep water. The extracted signal strips are more unbroken and continuous. This study can contribute to pioneering a new perspective for ICESat-2 photon-counting data denoising research that is limited to using only density-based algorithms.