提出了适合光学遥感图像的快速去模糊变分模型。模型通过引入辅助二次变量代理项，将非线性变分去模糊问题解耦并转化为去模糊和去噪交替最小化过程，进而提出了一个傅里叶域线性化去模糊滤波与子空间投影正则化去噪的交错迭代快速算法。最后通过典型的去大气湍流高斯模糊和光学系统散焦模糊数值实验，证明了该算法的改进信噪比比梯度最速下降法（GD）提高约2 dB，而且算法速度提高一个数量级。

A fast de-blurring variational model for optical remote sensing image is proposed. In the proposed model, the de-blurring and de-nosing parts can be divided into two alternating minimizing processes using surrogated functional decoupling approach. Combined Fourier domain linear de-blurring filtering and subspace projection de-nosing method together, a novel alternating iterative numerical algorithm is proposed. Two classical point spread functions such as atmosphere turbulence Gaussian blurring and out-of-focus blurring are designed to demonstrate this algorithm′s performance. Experimental results show that the improved signal to noise ratio in this algorithm is about 2 dB larger than that of the gradient decreasing (GD) algorithm and the iterative convergent rate is improved more than one order of magnitude.