电子学报 ›› 2012, Vol. 40 ›› Issue (3): 512-517.DOI: 10.3969/j.issn.0372-2112.2012.03.017

• 学术论文 • 上一篇    下一篇

自适应非局部patch正则化图像恢复

刘红毅1, 韦志辉2, 张峥嵘1   

  1. 1. 南京理工大学理学院,江苏南京210094;2. 南京理工大学计算机科学与技术学院,江苏南京 210094
  • 收稿日期:2011-05-03 修回日期:2011-12-02 出版日期:2012-03-25 发布日期:2012-03-25

Adaptive Nonlocal Patch Regularization for Image Restoration

LIU Hong-yi1, WEI Zhi-hui2, ZHANG Zheng-rong1   

  1. 1. School of Science,Nanjing University of Science and Technology,Nanjing,Jiangsu 210094,China;2. School of Compute Science and Technology,Nanjing University of Science and Technology,Nanjing,Jiangsu 210094,China
  • Received:2011-05-03 Revised:2011-12-02 Online:2012-03-25 Published:2012-03-25

摘要: 非局部均值利用图像自相似性,有效保持了图像的几何结构信息.提出了非局部patch正则和TV正则结合的图像恢复模型,利用改进的结构张量矩阵构造自适应非局部权函数,根据像素的局部结构计算图像中patch的相似性,提高了图像结构信息的保持性能.在数值解法上,采用分裂Bregman算法迭代求解模型,得到简单快速的迭代形式.数值实验证明所提出方法在提高恢复图像质量和算法效率上都有显著改进.

关键词: 图像恢复, 非局部, 正则化, 分裂Bregman迭代

Abstract: Nonlocal means exploits the spatial correlation in an image,and preserves the structure information effectively.Combining the nonlocal patch regularization with TV regularization,we propose a nonlocal patch regularized image restoration model.The improved structure tensor matrix can be used to achieve a data-adaptive weigh function,which can then adjust the similarity match process based on the local structure of a pixel.A more simple and effective algorithm -Split Bregman,is used to solve the model iteratively.Compared with other regularization models,our method performs better in improving the quality of restoration image and the efficiency of the algorithm.

Key words: image restoration, nonlocal means, regularization, split Bregman

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