In order to enhance the Signal-to-Noise Ratio (SNR) of Differential Column Image Motion lidar (DCIM lidar) detetion, a hybid denoising method which combines Ensemble Empirical Mode Decomposition (EEMD) and singular value decomposition(SVD) is proposed.The multilayer mode components are obtained from EEMD firstly. The difference of cross-correlation coefficients among these mode components is then utilized to determine the main noises which should be removed. The residual noises contained in mode components are identified by SVD and then the useful signal is extracted. Both the EEMD-SVD and EEMD methods are used to denoise the simulation signals and measured DCIM lidar signals. When the standard deviation of simulated noises is between 0.05 and 0.2, the signal-to-noise ratio(SNR) of retrieved turbulence profile with EEMD-SVD denoising is increased by 2.718 7 dB to 6.921 5 dB and the SNR of corresponding EEMD method is increased by 0.168 4 dB to 3.555 4 dB compared with the retrieved profile without denoising. Turbulence profiles retrieved from the undenoised and denoised DCIM lidar measurements and radio-sounding balloons are also compared at two typical time periods. It is found that the maximum SNR of turbulence profiles can separately be increased by 2.526 5 dB and 2.155 6 dB for EEMD-SVD and EEMD method compared with undenoising retrieval profile. The results indicate that the noise reduction effect of EEMD-SVD is superior than EEMD,which it is able to identify and reduce the noises more effectively.The SNR of original signal is greatly improved through EEMD-SVD method, thereby the retrieved atmospheric turbulence profile is achieved more accurately.