为了快速、准确地在轨估算空间光学遥感成像系统的点扩展函数(PSF)，提出了基于多通道盲反卷积（MBD）的估算方法，避免了以往方法依赖靶标及时间开销大等缺点。从单幅遥感图像中，提取多幅具有局部一致背景的目标子图像，进行交替最小化的迭代盲反卷积计算。在Intel Core i5-2400 3.1 GHz主频的计算机Matlab软件平台上，使用两幅无噪子图像只需0.4917 s即可达到均方误差百分率为1.1%的PSF估算结果；使用信噪比为45 dB的两幅含噪子图像，其均方误差百分率可达到5.9%。由估算的PSF复原图像时，可以将灰度平均梯度由5.7提高至7.1，拉普拉斯能量由29提高至46。估算精度与运行速度均优于常用的倾斜刃边法。

In order to evaluate the point spread function (PSF) of optical remote sensors well and truly, a fast multichannel blind deconvolution (MBD)-based estimation method which does not need any ground target is proposed. Multiple sub-images with uniform local background are extracted from a remote sensor image and alternate minimization algorithm is used to implement the blind deconvolution. It only takes 0.4917 s to achieve a percentage mean square error of 1.1% with two noise-free sub-images on a Matlab platform. While the error is 5.9% for two sub-images with the signal-to-noise ratio of 45 dB. By applying the estimated PSF to image restoration, it can bring gray mean gradient from 5.7 to 7.1 and energy of Laplacian from 29 to 46. This method performs more accurately and efficiently than the frequently used slanted-edge method.