低采样率下的高质量鬼成像（GI）对于科学研究和实际应用具有重要意义，为了在低采样率条件下重建高质量图像，提出了一种高质量的被动式压缩鬼成像重构算法（PCGI-LRC）。基于图像的非局域相似块堆叠而成的矩阵具有低秩和稀疏奇异值的假设，从理论和实验上证明了一种对最小二乘问题与非局域相似块低秩近似问题进行联合迭代求解的方法，能够在低采样率（6.25%~50%）条件下实现高质量鬼成像。实验结果表明：与基于稀疏基约束的GI（GI-SBC）和基于全变分约束的GI（GI-TVC）相比，PCGI-LRC在峰值信噪比、结构相似性系数和视觉观测等方面均更优，在抑制重构噪声的同时保持了目标的细节信息，其中PSNR提升效果优于1.1 dB，SSIM提升效果优于0.04。

High-quality ghost imaging （GI） at low sampling rate is of great importance for scientific research and practical applications. Therefore， the reconstruction of high-quality images under low sampling rate conditions remains the focus of GI research. In this paper， a high-quality passive compressive ghost image reconstruction algorithm was proposed， called PCGI-LRC. Based on the assumption that the matrices stacked with nonlocal similar blocks of an image have low-rank and sparse singular values， a joint iterative solution of the least squares problem was demonstrated theoretically and experimentally and the low-rank approximation problem of the nonlocal similar blocks can achieve high-quality ghost images under low sampling rate conditions （6.25%-50%）. Moreover， the experimental results show that the proposed algorithm outperforms the GI based on sparse basis constraints （GI-SBC） and GI based on full variational constraints （GI-TVC） algorithms regarding peak signal-to-noise ratio （PSNR）， structural similarity coefficient （SSIM）， and visual observation. Information of the target is preserved while the reconstruction noise is suppressed； the PSNR is improved by more than 1.1 dB and the SSIM improvement is higher than 0.04 dB.