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 0．2424dB，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%．

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 intro-duced 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． 2424dB，0． 0043 and 0． 0056，respectively，and reduces the recon-struction time to approximately 22．77% of that of SCSＲ algorithm owing to the reduction of dictionary dimensionality．