Improved Sparse Representation Super-Resolution algorithm for Remote Sensing Image

摘要：

　　In order to obtain higher quality super-resolution reconstruction(SRR)of remote sensing images,an improved sparse representation remote sensing images SRR method is proposed in this paper.First,lowresolution image is processed by improved feature extract operator.The high-resolution image and lowresolution image blocks have the same sparse representation coefficient,so the SRR image with higher spatial resolution can be derived from the sparse representation coefficients which have been obtained from low-resolution image.The improved feature extraction operator is a method to get more detail and texture information from the training images.Experiment results show that more texture details can be obtained in the result of SRR remote sensing images subjectively.At the same time,the objective evaluation parameters are improved greatly.The peak PSNR is increased about 2.50dB and 0.50 dB,RMSE is decreased about 2.80 and 0.3 compared with bicubic interpolation algorithm and Ref8 algorithm respectively.

作者: Zhu Fuzhen Huang Xin Liu Yue Zhu Haitao

作者单位: College of Electronic Engineering,Heilongjiang University,Harbin,150080,China Heilongjiang Duobaoshan Copper Industry lnc.,Heihe,164300,China

会议类型: 国际会议

会议名称: 2018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)(2018第二届电子信息技术与计算机工程国际会议)(EITCE2018)

会议地点: 上海

会议语种:英文

页码: 1-5

在线出版日期: 2018-10-12（万方平台首次上网日期，不代表论文的发表时间）