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目前THz自由空间成像面临的挑战主要有大气损耗和水分吸收，辐射功率低，成像要获得高的信噪比，需要有更高功率的辐射源；数据获取时间长；图像质量仍需改善。分析了THz成像技术的最新发展趋势及国内外发展现状。阐述了利用THz辐射进行合成孔径成像、THz压缩感知成像的基本原理，并对两种种成像方法形成的THz图像的特点进行了分析。应用Wiener2，基于熵标准的ddencmp选定小波系数阈值降噪法、Donoho提出的小波系数阈值降噪法以及基于小波系数幅值渐近最优降噪法等图像降噪算法对THz图像进行处理效果从均方根误差、信噪比、相关系数等方面进行了定性、定量的比较。提出将小波域马尔可夫随机场应用于THz图像降噪中。主要完成了以下几个方面：对每个小波系数引入两个状态，一个状态对应图像的非平稳区域，如边缘；另一个状态对应图像平稳区。每个状态下的小波系数用高斯分布函数来描述，虽然每个状态下的小波系数服从高斯分布，但每个小波系数的两个状态混合模型服从非高斯分布。然后利用EM（Expectation Maximization）算法估计混合模型中的参数，采用贝叶斯准则初步确定理想图像小波系数的收缩因子。最后将小波域隐马尔可夫模型的降噪算法进行对比试验，仿真结果表明小波域隐马尔可夫模型的降噪算法更具有效性和优异性。“,”The main challenges for free-space terahertz (THz) imaging are known to be atmospheric loss, moisture absorption, low radiation power; and concequently, low signal-to-noise ratio (SNR). The need to have higher power radiation sources; faster data acquisition times remain major obstacles for high image quality. In this paper, the current state of research and applications was analyzed, as well as the future development of THz imaging technology was predicted. The basic principle of synthetic aperture radar (SAR) imaging and THz compressed sensing (CS) imaging was expounded. The THz image features of the two imaging methods were analyzed. The denoising effects of THz simulation images among the Wiener2, ddencmp, Donoho and the wavelet coefficients Amplitude Asymptotically Optimal (AAO) algorithm were also compared, qualitatively. A Markov random field(MRF) model for THz image denoising was presented, in order to capture the characteristics of scale space, with better scale wavelet coefficients in the wavelet domain. The image′s MRF model was established and the energy functions which were used for image denoising and the two states of each wavelet coefficient were introduced, in non-stationary regions: one state corresponded to the image features such as edge, while another state was related to the stationary region image. The Expectation Maximization (EM) algorithm was used to estimate the parameters of the mixture model, along with the Bayes Preliminary rule to determine the ideal image wavelet coefficients contraction factor. The denoising algorithm of the Hidden Markov Models in Wavelet Domain (HMMWD) was tested, with excellent simulation results that show the WDHMM to be more effective.