We develop a regularization-based algorithm for reconstructing the Cn2 profile using the profile of Fried’s transverse coherent length (r0) of differential column image motion (DCIM) lidar. This algorithm consists of fitting the set of measured data to a spline function and a two-stage inversion method based on regularized least squares QR-factorization (LSQR) in combination with an adaptive selection method. The performance of this algorithm is analyzed by a simulated profile generated from the HV5/7 model and experimental DCIM lidar data. Both the simulation and experiment support the presented approach. It is shown that the algorithm can be applied to estimate a reliable Cn2 profile from DCIM lidar.