基于鬼成像(Ghost imaging, GI)与压缩感知(Compressed sensing, CS)理论，研究了CS重建算法对GI成像性能的影响。以离散小波变换为图像的稀疏矩阵、具有高斯线型的热光源强度分布为测量矩阵， 分析了基于增广拉格朗日法和交替方向法的全变分最小化算法(TVAL3)、正交匹配追踪算法(OMP)、压缩采样匹配追踪算法(CoSaMP)、 梯度投影算法(GPSR_Basic)下的压缩鬼成像的质量。以均方误差、峰值信噪比、匹配度、结构相似性指标等为图像质量客观评价标准， 比较了4种重建算法下压缩鬼成像的重建结果。结果表明压缩比为0.5时TVAL3算法还原度最高，CoSaMP算法重建图像失真最严重,GPSR_Basic算法获得的重建性能优于OMP算法。

Based on ghost imaging (ghost imageing, GI) and compressed sensing (compressed sensing, CS) theory, the influence of CS reconstruction algorithm on imaging performance of GI is investigated. Discrete wavelet transform is used as image sparse matrix, and the thermal light source intensity distribution with Gauss profile is used as measurement matrix. Quality of compressed GI is analyzed with total variation minimization algorithm based on augmented Lagrangian and alternating direction method (TVAL3), orthogonal matching pursuit (OMP), compressive sampling matching pursuit (CoSaMP) algorithm, gradient projection algorithm (GPSR_ basic). Mean square error, peak signal to noise ratio, matching degree and structural similarity index are taken as the objective evaluation criteria for image quality, and the reconstruction results of GI using four kinds of reconstruction algorithms are compared. Results show that when the compression ratio is 0.5, the reduction degree of TVAL3 algorithm is the highest, and the distortion of CoSaMP algorithm is the most serious. The reconstruction performance of GPSR_Basic algorithm is better than that of OMP algorithm.