This study proposes a computational ghost imaging method based on Tikhonov regularization to solve the problem of poor ghost image quality caused by data perturbation and few sampling times during ghost imaging sampling. The proposed method uses a constraint term that characterizes the noise intensity to transform the computational ghost imaging problem into a mathematical problem for minimizing the signal error and noise intensity. Subsequently, the ghost image of the unknown object is reconstructed by selecting appropriate regular parameters using the generalized cross-validation method. The experimental results denote that the proposed algorithm is superior to traditional, differential, and pseudo-reverse ghost imaging methods when interference is present and that it exhibits considerable stability. Furthermore, in the absence of interference, the proposed method is superior to traditional and differential ghost imaging methods and exhibits similar performance when compared with that exhibited by pseudo-reverse ghost imaging at the same time.