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基于改进广义全变分的稀疏图像重建算法

Sparse Image Reconstruction Based on Improved Total Generalized Variation

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摘要

为了提升稀疏采样环境下的图像重建质量,针对广义全变分模型重建图像时不能充分利用图像本身结构自相似性信息的不足,建立了一个非局部约束下的改进广义全变分图像重建模型。该模型引入了变化域非局部自相似性作为图像重建的先验信息,同时在八邻域空间计算多方向的广义全变分正则化约束,从而更好地保护了图像的结构特征,进一步地,使用增广拉格朗日理论对模型进行去约束化、求解,提出了基于改进广义全变分的图像重建算法。仿真实验结果表明,所提出的重建模型和图像重建算法可以有效地去除图像中的伪影和噪声,满足稀疏采样情形下对图像重建质量的要求。与其他重建算法进行比较可知,本文算法所重建的图像不论是主观视觉效果,还是各个客观评价指标均有不同程度的改善和提高。

Abstract

Regarding to the total generalized variational model cannot fully utilize the self-similarity information of the image structure when reconstructing images, an improved generalized variational image reconstruction model under non-local constraints is established to improve the quality of image reconstruction in the sparse sampling situation. This model introduces a non-local self-similarity of the transform domain as a priori information for image reconstruction. And the multi-directional total generalized variational regularization constraint is calculated in the eight-neighborhood space to protect the structural characteristics of the image. Further, the augmented Lagrangian theory is used to remove the constraint and solve the model, and an image reconstruction algorithm based on the improved total generalized variation is proposed. Simulation experimental results show that the proposed reconstruction model and image reconstruction algorithm can effectively remove the artifacts and noise in the image and meet the requirements of image reconstruction quality under sparse sampling condition. Compared with the famous reconstruction algorithms, the images reconstructed by proposed algorithm has significant improvement in both subjective visual effects and all objective evaluation indicators.

Newport宣传-MKS新实验室计划
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中图分类号:TP393.0

DOI:10.3788/lop55.111103

所属栏目:成像系统

基金项目:江苏省科技厅产学研联合创新基金(BY2013015-23)

收稿日期:2018-04-23

修改稿日期:2018-05-30

网络出版日期:2018-06-08

作者单位    点击查看

班晓征:江南大学物联网工程学院, 江苏 无锡 214122
李志华:江南大学物联网工程学院, 江苏 无锡 214122
李贝贝:江南大学物联网工程学院, 江苏 无锡 214122
徐敏达:江南大学物联网工程学院, 江苏 无锡 214122

联系人作者:李志华(jswxzhli@aliyun.com)

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引用该论文

Ban Xiaozheng,Li Zhihua,Li Beibei,Xu Minda. Sparse Image Reconstruction Based on Improved Total Generalized Variation[J]. Laser & Optoelectronics Progress, 2018, 55(11): 111103

班晓征,李志华,李贝贝,徐敏达. 基于改进广义全变分的稀疏图像重建算法[J]. 激光与光电子学进展, 2018, 55(11): 111103

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