中文摘要：

针对单一冗余字典在稀疏表示图像超分辨率重建结果出现不清晰、伪影以及重建过程编码效率不高、运算时间过长的问题,提出一种基于多字典学习和图像块映射的超分辨率重建方法。该方法在传统稀疏表示的框架下,首先探索局部图像块的梯度结构信息,按梯度角度将训练样本块分类;然后为每个子类样本集学习高低分辨率字典对,再结合最近邻思想应用生成的字典,为每个子类计算从低分辨率块到高分辨率块映射的函数;最后将重建过程简化为输入块和映射函数的乘积,在保证提高重建质量的同时减少了图像重建的时间。实验结果表明,所提算法在视觉效果有较大的提升,同时与锚点邻域回归算法相比,评价参数峰值信噪比（PSNR）平均提高约0.4 d B。

英文摘要：

To overcome the disadvantages of the unclear results and time consuming in the sparse representation of image super-resolution reconstruction with single redundant dictionary,a single image super-resolution reconstruction method based on multi-dictionary learning and image patches mapping was proposed. In the framework of the traditional sparse representation,firstly the gradient structure information of local image patches was explored,and a large number of training image patches were clustered into several groups by their gradient angles,from those clustered patches the corresponding dictionary pairs were learned. And then the mapping function was computed from low resolution patch to high resolution patch in each clustered group via learned dictionary pairs with the idea of neighbor embedding. Finally the reconstruction process was reduced to a projection of each input patch into the high resolution space by multiplying with the corresponding precomputed mapping function,which improved the images quality with less running time. The experimental results show that the proposed method improves the visual quality significantly,and increases the PSNR（ Peak Signal-to-Noise Ratio） at least0. 4 d B compared with the anchored neighborhood regression algorithm.