中文摘要：

针对超分辨率重构字典对结构区分度不够、在最优匹配原子搜索中耗时太长的问题,提出了一种多特征联合的分级字典（MFJD）.首先,分别用边缘块梯度特征和纹理块局部二值模式（LBP）特征来构建两种分类字典,用于逼近不同类型结构;其次,采用树结构来聚类原子,实现同一字典下的快速原子匹配;最后,引入双边总变分（BTV）正则项来约束重构结果.实验表明：与经典稀疏编码超分辨率重构（SCSR）算法相比,MFJD多特征联合的分级字典使重构图像的PSNR值提高了0.2424 d B,使平均结构相似度（MSSIM）和特征相似度（FSIM）分别提高了0.0043和0.0056;由于结构分类字典维数降低,重构时间降至SCSR算法的22.77%.

英文摘要：

A multi-feature joint dictionary（ MFJD） is suggested to improve the structural distinction in dictionary training and to accelerate the atom matching in sparse reconstruction. Firstly,two dictionaries branched respectively for edge-and texture-descriptions are created using gradient and LBP operators. Secondly,tree structures are introduced to represent the hierarchical clustering of atoms,which leads to a quick atom searching. Then,bilateral total variation（ BTV） regularization is employed to achieve the optimal resolution. Experimental results show that,in comparison with the sparse coding super-resolution reconstruction（ SCSR） algorithm,MFJD averagely improves the PSNR,MSSIM and FSIM of an image by 0. 242 4 d B,0. 004 3 and 0. 005 6,respectively,and reduces the reconstruction time to approximately 22. 77% of that of SCSR algorithm owing to the reduction of dictionary dimensionality.