针对一些超分辨率重建算法鲁棒性差、边缘保持能力有限、降噪效果不理想等不足，提出一种基于最大后验概率估计的边缘增强型非局部模型超分辨率重建算法。算法引入了非局部模型，并将图像的边缘信息加入模型系数的计算中，是对基于BTV(bilateral total variance)模型超分辨率重建和基于MRF(Markov random field)模型超分辨率重建的有效改进，提高了算法的鲁棒性、边缘保持能力和降噪能力。实验结果表明，该算法性能稳定，在信噪比较低情况下也能保持图像的边缘信息，取得比较好的重建效果。

In order to overcome the weak robustness，the weak reservation of edges, and the high sensitivity to noise in some super-resolution methods, we propose a nonlocal-means super-resolution reconstruction with enhanced edges based on the MAP frame. This method adopts the nonlocal-means model, and computes the modulus of the model together with the edges of the image. The proposed method mends effectively super-resolution reconstruction based on the bilateral total variance (BTV) model and based on the Markov random field (MRF) model. Our method is more robust and it is more able to reserve edges and to remove noise. Experimental results show that the proposed method is robust, and can reserves the edges well under low signal to noise ratio, getting a better reconstruction result.