计算机应用 ›› 2013, Vol. 33 ›› Issue (05): 1416-1419.DOI: 10.3724/SP.J.1087.2013.01416

• 多媒体处理技术 • 上一篇    下一篇

特征保留的稀疏表示图像去噪

马路1,邓承志2,汪胜前2,刘娟娟1   

  1. 1. 江西科技师范大学 通信与电子学院,南昌 330013
    2. 南昌工程学院 信息工程学院,南昌 330099
  • 收稿日期:2012-11-26 修回日期:2013-01-07 出版日期:2013-05-01 发布日期:2013-05-08
  • 通讯作者: 马路
  • 作者简介:马路(1986-),男,湖北黄冈人,硕士研究生,主要研究方向:图像稀疏表示;邓承志(1980-),男,江西赣州人,副教授,博士,主要研究方向:图像稀疏表示;汪胜前(1965-),男,江西浮梁人,教授,博士,主要研究方向:图像处理、模式识别;刘娟娟(1989-),女,安徽亳州人,硕士研究生,主要研究方向:图像处理。
  • 基金资助:

    国家自然科学基金资助项目(61162022);江西省自然科学基金资助项目(2009GZW0020);江西省教育厅科技项目(GJJ12632)

Feature-retained image de-noising via sparse representation

MA Lu1,DENG Chengzhi2,WANG Shengqian2,LIU Juanjuan1   

  1. 1. School of Communication and Electronics, Jiangxi Science and Technology Normal University, Nanchang Jiangxi 330013,China
    2. School of Information Engineering, Nanchang Institute of Technology, Nanchang Jiangxi 330099, China
  • Received:2012-11-26 Revised:2013-01-07 Online:2013-05-08 Published:2013-05-01
  • Contact: MA Lu

摘要: 稀疏表示理论认为在合适的冗余字典下,图像存在最为稀疏的表示,字典的过完备性,使得通过提取很少量的大系数便能捕获到图像中的重要信息,而且对噪声更加鲁棒。针对图像去噪,为了更好地保留图像特征信息,考虑人眼视觉特性,研究过完备字典对噪声图像特征和边缘信息的有效表示,提出以结构相似为信息保真度的特征保留的稀疏表示去噪算法。实验结果表明,该算法能更好地对图像去噪,对特征和边缘等信息的保留能力更强,得到的图像视觉效果更佳。

关键词: 稀疏表示, 图像去噪, 特征保留, 结构相似

Abstract: According to the theory of sparse representation, images can be sparse-represented by using an appropriately redundant dictionary. The completeness can enable using very few big coefficients to capture the important information of images, and cause more robust to noise. Regarding image de-noising, considering the human visual characteristics, this paper studied the effective representation of characteristics and edge information of noisy image based on complete dictionary. For more effective feature retaining of images, a method of feature-retaining de-noising via sparse representation was proposed, which made the Structural SIMilarity (SSIM) as fidelity measure of the information. The experimental results indicate that the proposed algorithm has a better efficiency of de-noising, enhances the capacity of retaining feature, and gets a better visual effect of de-noised image.

Key words: sparse representation, image de-noising, feature retaining, structural similarity

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