The constituents and structures of the atmosphere directly or indirectly affect the radiative energy budget of the Earth; thus, there is an urgent need to measure these components. Space-borne lidar is a powerful instrument for depicting the global atmosphere. Several space-borne lidars with spectral discrimination filters are proposed and even currently being developed, including the Chinese Aerosol-Cloud High-Spectral-Resolution Lidar (ACHSRL) onboard the Aerosol Carbon Detection Lidar satellite. However, the long distance from the satellite to the atmosphere near the Earth surface weakens the signal strength and debilitates the detection accuracy of space-borne lidar. Furthermore, due to absorption of Rayleigh scattering when it passes through the spectral discrimination filter, the signal-to-noise ratio in the molecular channel decreases. The traditional denoising method is to average the echo signals both vertically and horizontally, but the high speed of the satellite (7.5 km/s) and the varying atmosphere structure will blur detected layer features. A novel method to reduce the signal noise level of ACHSRL is proposed in this paper. A state-of-the-art algorithm for imaging denoising, block matching 3D filtering (BM3D), is employed. As ACHSRL has not been launched, a simulation study is performed. In the simulation experiment, we connect adjacent lidar signal profiles into one 2D matrix and treat it as an image. Unlike the existing lidar denoising algorithm which uses neighboring profiles to smooth, BM3D performs frequency domain transformation of the signal image and then searches for a similar patch in a given block to conduct collaborative filtering. This algorithm not only achieves denoising, but also preserves aerosol/cloud feature details. After denoising by BM3D, the peak signal-to-noise ratios of echo signals in all channels are improved and the retrieval accuracy of particulate optical properties is also refined, especially for the retrieval of the extinction coefficient.