A new Super-resolution Reconstruction Algorithm Based on Block Sparse Representation

摘要：

　　In this paper,we propose a new single super-resolution (SR) reconstruction algorithm via block sparse representation and regularization constraint.Firstly,discrete K-L transform is used to learn compression sub-dictionary according to the specific image block.Combined with threshold choice of training data,the transform bases are generated adaptively corresponding to the sparse domain.Secondly,Non-local Self-similarity (NLSS,) regularization term is introduced into sparse reconstruction objective function as a prior knowledge to optimize reconstruction result.Simulation results validate that the proposed algorithm achieves much better results in PSNR and SSIM.It can both enhance edge and suppress noise effectively,which proves better robustness.

关键词： Block Sparse Representation Discrete K-L Transform Non-local Self-similarity Super-resolution

作者: Xiaodong Zhao Jianzhong Cao Hui Zhang Guangsen Liu Hua Wang Qing Liu

作者单位: Xian Institute of Optics and Precision Mechanics, CAS, Xian 710119, China ;University of Chinese A Xian Institute of Optics and Precision Mechanics, CAS, Xian 710119, China

会议类型: 国际会议

会议名称: 2nd International Conference on Polymer Composities and Polymer Testing (2013第二届高分子复合材料与高分子测试国际会议暨先进工业技术与方案国际会议)(ISPCPT2013)

会议地点: 太原

会议语种:英文

页码: 755-759

在线出版日期: 2013-03-22（万方平台首次上网日期，不代表论文的发表时间）