关键词: 超分辨率重构, 结构分类, 多特征描述, LBP 纹理, 双边总变分

Abstract: 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．

Key words: super-resolution reconstruction, structure classification, multi-feature description, LBP texture, bilateral total variation