计算机工程与应用2012，Vol.48Issue(20)：195-199,5.DOI:10.3778/j.issn.1002-8331.2012.20.041
结合NSCT和改进BP网络的超分辨率图像重建
Super-resolution reconstruction of image via NSCT and improved BP network
符立梅 1彭国华1
作者信息
- 1. 西北工业大学理学院数学系,西安710129
- 折叠
摘要
Abstract
This paper presents a new learning based super-resolution of image by introducing the Nonsubsampled Contourlet Transform (NSCT) and improved BP neural network.NSCT can recover the detail information better, as the improved BP can simulate the highly nonlinear, which is fast convergence and accuracy. For both super-resolution image and low-resolution image, it extracts the Contourlet coefficients of each sub-band training the improved BP network, then using the stable and restraining network realizes super-resolution reconstruction of image. The results show that this method is able to preserve the details of original image better and reduce the complexity of the network reconstruction at the same time, raise the accuracy, get significantly improved in reconstruction results.关键词
超分辨率重建/非下采样Contourlet变换/改进BP神经网络Key words
super-resolution reconstruction/ nonsubsampled Contourlet transform/ improved BP neural network分类
信息技术与安全科学引用本文复制引用
符立梅,彭国华..结合NSCT和改进BP网络的超分辨率图像重建[J].计算机工程与应用,2012,48(20):195-199,5.
