采用基于稀疏限制的鬼成像技术(GISC)框架模拟研究了散斑场中散斑强度分布对成像质量的影响以及重建过程中所需要的采样次数与散斑尺寸和目标稀疏性的关系。数值模拟结果表明，光斑强度分布对成像分辨率影响较大，高斯散斑优于相同直径平顶散斑，且高斯分布散斑更适合于实际应用。当散斑尺寸小于图像的分辨率时，所需的采样次数只跟图像的稀疏度有关，而与图像的具体分布方式无关；当散斑尺寸超出目标自身的分辨率时，获得相同成像质量的图像(以较低分辨率图像为准)所需要的采样次数与散斑直径的平方成反比。模拟验证采样次数与目标稀疏性的关系，并给出和验证了具有灰度的自然目标图像的稀疏度计算公式。

Under the framework of ghost imaging via sparsity constraints (GISC), we do numerical simulations on the influence of the speckle intensity distribution on image quality and study the relationship between the sampling number needed for information reconstruction and the speckle size and the target sparsity. Numerical simulation results show that, the speckle intensity distribution affects the imaging resolution. Gauss distribution is better than flat-topped distribution with the same speckle size, and the Gauss distribution is more suitable for practical application. In addition, when the speckle size is smaller than the resolution of the image, the sampling number required is only related to the image sparse degree, and it has nothing to do with the specific distribution of the image. When the speckle size exceeds the target resolution, to obtain the same image quality (based on the image with lower resolution), the sampling number needs to be inversely proportional to the square of the speckle diameter. The relationship between sampling number and the target sparsity is simulated and verified, while we give and verify the calculation formula of the sparse degree of the natural target with gray.