Image Super-resolution Based on Compressive Sensing

摘要：

Abstract-—Based on Compressive Sensing, we introduce sparse signal representation theory to modify the local geometric similarity model and construct sparse geometric similarity representation. Based on the modified model we can estimate the optimized reconstruct coefficients by jointing the original global and local image structure themselves, without the support of other training image database. The experimental results show that the algorithm can greatly improve the reconstruction of the edge and texture details in the high-resointion image.

关键词： CompresslveSensing imagesuper-resolution local geometrical similarity sparse representation

作者: Ying Gu Xiuchang Zhu

作者单位: College of Telecommunications & Information Engineering Nanjing University of Posts and Telecommunications Nanjing, China

会议类型: 国际会议

会议名称: 2010 International Conference on Signal and Information Processing(2010年IEEE信号与信息处理国际会议 ICSIP2010)

会议地点: 长沙

会议语种:英文

页码: 647-651

在线出版日期: 2010-12-14（万方平台首次上网日期，不代表论文的发表时间）