Undersampling image reconstruction method based on second order total generalized variation model

doi:10.11772/j.issn.1001-9081.2014.10.2953

Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (10): 2953-2956.DOI: 10.11772/j.issn.1001-9081.2014.10.2953

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Undersampling image reconstruction method based on second order total generalized variation model

WEI Jinjin¹,JIN Zhigang¹,WANG Ying¹,²

1. School of Electronic Information Engineering, Tianjin University, Tianjin 300072, China;
2. Unit 61660, Beijing 100089, China

Received:2014-05-08 Revised:2014-06-16 Online:2014-10-01 Published:2014-10-30
Contact: WEI Jinjin

基于二阶总广义变差的欠采样图像重构方法

卫津津¹,金志刚¹,王颖¹,²

1. 天津大学电子信息工程学院, 天津 300072；
2. 61660部队，北京 100089

通讯作者: 卫津津
作者简介:卫津津(1987-)，女，山西临汾人，博士研究生，主要研究方向：图像/视频处理；金志刚(1972-)，男，上海人，教授，博士，主要研究方向：网络与信息安全、图像/视频处理；王颖（1977-），女，河北张家口人，博士，主要研究方向：网络安全。
基金资助:
教育部人文社会科学研究项目

Abstract

Abstract:

Aiming at convex optimization problem of undersampling image reconstruction, a new image reconstruction algorithm based on the second order Total Generalized Variation (TGV) model was proposed. In the new model, the second-order TGV semi-norm of images was used as the regularization term, which could automatically balance the first order and second order derivative. The characteristics of the TGV made the new model recover the image edge information better, smooth noise and avoid the staircasing effect. For computing the new model effectively, the orthogonal projection and the adjustment of weight threshold were presented to adaptively amend the iteration results of each step in order to obtain accurate image reconstruction results. The experimental results show that the proposed model can get better results with large value of Peak Signal-to-Noise Ratio (PSNR) and Structure SIMilarity (SSIM) in image reconstruction compared with Orthogonal Matching Pursuit (OMP) and Total Variation (TV) models.

摘要：

针对欠采样图像重构的凸优化问题，提出一种基于二阶总广义变差(TGV)范数最小化的算法。利用图像的二阶TGV半范作为正则约束项，自动地平衡一、二阶导数项,使得该算法可以更好地恢复图像边缘，有利于平滑噪声，避免阶梯效应。为了有效地计算该模型，通过正交投影和调整权重阈值对每一步迭代结果进行修正，最终获得更准确的重构结果。实验结果表明，与正交匹配追踪(OMP)模型和全变差(TV)模型比对，该算法重构的图像其峰值信噪比(PSNR)及结构相似度(SSIM)都有明显的提高，重构效果较好。

CLC Number:

TP391.413

WEI Jinjin JIN Zhigang WANG Ying. Undersampling image reconstruction method based on second order total generalized variation model[J]. Journal of Computer Applications, 2014, 34(10): 2953-2956.

卫津津金志刚王颖. 基于二阶总广义变差的欠采样图像重构方法[J]. 计算机应用, 2014, 34(10): 2953-2956.

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Undersampling image reconstruction method based on second order total generalized variation model

基于二阶总广义变差的欠采样图像重构方法

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