计算机工程与应用2019，Vol.55Issue(3)：191-195,5.DOI:10.3778/j.issn.1002-8331.1711-0086
基于递归残差网络的遥感图像超分辨率重建
Super-Resolution Reconstruction of Remote Sensing Image Based on Recursive Residual Network
摘要
Abstract
The deep network can effectively improve the accuracy of the reconstructed image, but it has a large number of parameters, which makes the training time too long. Therefore, this paper improves the super-resolution reconstruction algorithm of remote sensing image based on recursive residual network. The global residual learning and local residual learning are combined to effectively reduce the difficulty of training deep network and control the network parameters through recursive learning. The experimental results show that the recursive residual network is effective in the super-reso-lution reconstruction of the remote sensing image, and the improved network can obtain better subjective visual effect and objective evaluation index.关键词
递归残差网络/遥感图像超分辨率重建/残差学习/递归学习Key words
recursive residual network/remote sensing image super-resolution reconstruction/residual learning/recursive learning分类
信息技术与安全科学引用本文复制引用
王爱丽,宋晓莹,陈雨时..基于递归残差网络的遥感图像超分辨率重建[J].计算机工程与应用,2019,55(3):191-195,5.基金项目
国家重点研发计划项目(No.2016YFC0104505) （No.2016YFC0104505）
国家自然科学基金(No.61701492,No.61201117) （No.61701492,No.61201117）
江苏省自然科学基金(No. BK20170392,No.BK20151232) （No. BK20170392,No.BK20151232）
中国科学院青年创新促进会(No.2014281) （No.2014281）
苏州市前瞻性应用研究项目(No.SYG201608). （No.SYG201608）
