太赫兹(THz)波具有的许多独特性质，使其非常适合应用于对人体的安检成像，但是目前原始太赫兹图像的信噪比、对比度和分辨率都有待改善。为提高太赫兹安检图像的质量，研究提出一种基于马尔可夫随机场理论的被动式太赫兹图像复原算法。对原始图像进行去噪和增强的预处理之后，在贝叶斯分析的基础上增加马尔可夫约束项进行图像复原。通过改变迭代次数和正则化参数，得到了清晰度不同的处理结果，经主客观评价指标分析确定了最佳的参数。实验结果证明，本算法可以在被动式太赫兹图像的噪声滤除和边缘信息保持上取得较好的平衡，从而大幅提高太赫兹安检图像的目标分辨能力。

Terahertz (THz) wave is suitable for human body security imaging because of the unique characteristics of THz wave. But the signal noise ratio, constrast and resolution of original terahertz images need to be improved presently. In order to improve the quality of the THz images for security screening, a passive THz imaging restoration algorithm based on Markov random field is put forward. After being pre-processed by image de-noising and enhancement treatments, the original image is restored based on Bayes analysis to which Markov constraint is added. Different definition of resulting images are obtained by changing iteration and regularization parameter. Optimum parameters are determined by objective standard evaluation and subjective visual effect. Experimental results show that the proposed method achieves good balance between noise removing and edge information preserving of passive terahertz image. Thus target resolving ability of terahertz security images is improved dramatically.