Iterative directional total variation refinement for compressive sensing image reconstruction

被引：16
作者：
Fei, Xuan [1 ]
Wei, Zhihui [1 ]
Xiao, Liang [1 ]
机构：
[1] School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu Province
来源：
IEEE Signal Processing Letters | 2013年 / 20卷 / 11期
关键词：
Compressive sensing; directional total variation; orientation field estimation; total variation;
D O I：
10.1109/LSP.2013.2280571
中图分类号：
学科分类号：
摘要：
We propose a novel compressive sensing (CS) image reconstruction method based on iterative directional total variation (TV) refinement. As is generally known, classical TV-based CS reconstruction methods tend to produce over-smoothed image edges and texture details, since they favor piece-wise constant solutions. Hence, directional TV is introduced to describe the sparsity of the image gradient in order to overcome this drawback. However, it is difficult to estimate orientation field robustly and accurately from CS measurements. Inspired by vectorial ROF model, orientation field refinement model is presented and introduced into CS reconstruction. Extended experiments show that the proposed CS reconstruction method has a better improvement in the quality of the reconstructed image details over related TV-based CS reconstruction methods. © 2013 IEEE.
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收藏
页码：1070 / 1073
页数：3
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