电讯技术2017，Vol.57Issue(9)：981-985,5.DOI:10.3969/j.issn.1001-893x.2017.09.001
一种应用于欠采样图像的自适应稀疏重建方法
An Adaptive Sparse Reconstruction Method for Undersampling Images
管春 1陶勃宇1
作者信息
- 1. 重庆邮电大学 光电工程学院,重庆400065
- 折叠
摘要
Abstract
Considering the image detail loss and staircase effect problems caused by the fixed parameters of total variation( TV) regularization constraints in image spare reconstruction,this paper proposes an adaptive sparse image reconstruction algorithm by using second-order total generalized variation( TGV) model as the regularization constraints. The second-order TGV model is applied to balance the first and second derivative in images,and it can automatically modify the weights on the basis of each iteration solution and tensor func-tion to achieve image sparse reconstruction. Simulation results show that compared with the TV model and fixed TGV model,this algorithm can maintain both image detail information and image outline,as well as im-proving peak signal-to-noise ratio( PSNR) and structure similarity( SSIM) of the reconstructed image.关键词
图像处理/稀疏重建/压缩感知/广义全变分/自适应正则约束/分裂Bregman算法Key words
image processing/sparse reconstruction/compressed sensing/total generalized variation/adap-tive regularization term/split Bregman algorith分类
信息技术与安全科学引用本文复制引用
管春,陶勃宇..一种应用于欠采样图像的自适应稀疏重建方法[J].电讯技术,2017,57(9):981-985,5.
