针对计算鬼成像采样过程中出现的数据扰动及采样次数不易较多所引起的鬼图像质量较差的问题,提出了一种基于Tikhonov正则化的计算鬼成像方法。该方法利用一个表征噪声强度的约束项,将计算鬼成像问题转化为信号误差与噪声强度最小化的数学问题,并利用广义交叉验证法选取合适的正则参数来重构待测物体的鬼像。实验结果表明,所提算法在干扰情况下明显优于传统鬼成像、差分鬼成像和伪逆鬼成像,具有较强的稳定性;在无干扰情况下,也明显优于传统鬼成像、差分鬼成像,且不差于伪逆鬼成像。

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.