遥感根本目的就是获得清晰的高空间分辨率的图像，从而可以进一步地分析处理。为了在遥感测量中获得更高空间分辨率、更高信噪比、更清晰的图像，本文对图像处理领域超分辨算法进行了研究。建立了一套拟合模拟现实的成像系统模型，在这种模型的基础之上，利用最大后验概率系统理论，讨论了现实情况中的运动模糊，噪声等情况，改进了MAP超分辨算法。实验结果表明：使用本文改进的基于MAP理论的Markov随机场约束的多帧超分辨重建算法，可以较好提高超分辨效果，与三次立方插值方法相比，PSNR至少提高约5.1 dB左右，与未改进的MAP方法相比，PSNR提高约0.2 dB左右。本文提出了动态的先验约束方法，给约束函数添加与迭代次数相关的约束项，该改进创新可以加快收敛并且更加逼近真实图像，实验表明该方法收敛速度更快，约束效果良好，更适合实际应用。

The fundamental purpose of remote sensing is to obtain clear images with high spatial resolution, which can be further analyzed and processed. In order to obtain higher spatial resolution, higher signal-to-noise ratio and clearer images in remote sensing measurement, this paper studies the superresolution algorithm in image processing field. In the paper we set up a model fitting the real imaging system, use the theory of maximum a posteriori probability system on this model, discuss the motion blur, noise and other degrading factors, and finally improve the MAP superresolution algorithm. The experimental results show that the improved multi-frame superresolution reconstruction algorithm in the paper based on the Markov constraint field which is originated from the MAP theory, can improve the superresolution performance significantly. Using our method, PSNR is at least about 5.1 dB higher than the cubic interpolation method, and is about 0.2 dB higher than the unimproved MAP method. In our paper a dynamic prior constraint method is proposed, by adding constraints associated with the number of iterations to constraint function, the improvement innovation can accelerate convergence and more close to real images. And the experiments show that this method can fasten the convergence, and its constraint effect is good. Above all, it is more suitable for practical application.