Recently, LiDAR sensors have become indispensable in autonomous driving research. Despite continuous improvements in performance and price reductions, noise generated under adverse weather conditions remains a serious challenge. Most of the noise generated under such conditions is due to particles such as fog, rain, and snow. These particles are extremely fine; therefore, they have a very low reflectance compared to the targets that the laser should detect. In this study, we propose a method to distinguish particles by restoring the reflectance from LiDAR sensing data based on the reflectance characteristics of the particles. In addition, we propose a method to make additional judgments based on the geometrical shapes of adjacent particles to distinguish the particles more accurately. The proposed method is accurate enough to be compared to state-of-the-art deep learning methods. Moreover, the execution time is less than 2 ms on a single-core CPU, demonstrating a remarkable efficiency, being more than three times faster than that of methods performed on a GPU. Because noise removal is a preprocessing step, the proposed method is expected to allow more resources to be allocated to other, more important processes for autonomous driving.