摘要
压缩感知(compressed sensing,CS)图像重建算法是CS图像获取问题的一个研究重点。针对传统基于稀疏性先验的重建算法不能有效重建图像的各种结构特征,为了在测量值数量不变的情况下进一步提高图像的重建质量,在稀疏性先验的基础上,引入局部自回归模型和非局部自相似性作为图像额外的先验信息,建立了非局部正则化的CS图像重建模型,并给出了相应的数值求解算法。此外,对于重建模型中图像的自回归参数,给出一种基于非局部相似点的估计方法。实验结果表明,较之传统的稀疏性正则化重建算法和同类的MARX(model-based adaptive recovery of compressive sensing)算法,所提算法能获得更高的图像重建质量。
Compressed sensing （CS） image reconstruction algorithm is a key point in the CS image acquisi tion problem. The conventional sparsity based reconstruction algorithms cannot effectively reconstruct various image structures. In order to improve the reconstruction precision with the same number of measurements, the local autoregressive （AR） model and non-local self-similarity as the additional prior information of image are in- troduced, and a non-local regularized CS image reconstruction model and the relative numerical algorithm are de- veloped. Furthermore, a non-local similarity based estimation method for AR parameters is proposed, which are considered in the present reconstruction model. Compared with the traditional sparsity regularized algorithms and related MARX（model-based adaptive recovery of compressive sensing） algorithm, the proposed algorithm can achieve a higher image reconstruction performance.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2013年第1期196-202,共7页
Systems Engineering and Electronics
基金
国家自然科学基金(61101194
61071146)
江苏省自然科学基金(BK2011701)
高等学校博士学科点专项科研基金(20123219120043)资助课题
关键词
压缩感知
图像重建
自回归模型
非局部自相似性
compressed sensing （CS）
image self-similarity reconstruction
autoregressive （AR） model
non-local
