计算机应用 ›› 2012, Vol. 32 ›› Issue (10): 2786-2789.DOI: 10.3724/SP.J.1087.2012.02786

• 图形图像处理 • 上一篇    下一篇

基于曲线波和稀疏表达的卡通—纹理模型

康晓东1,王昊1,2,郭宏3,郭军1   

  1. 1. 天津医科大学 医学影像学院,天津 300070
    2. 河北大学附属医院,河北 保定 071000
    3. 天津医科大学总医院,天津 300070
  • 收稿日期:2012-05-03 修回日期:2012-06-01 发布日期:2012-10-23 出版日期:2012-10-01
  • 通讯作者: 康晓东
  • 作者简介:康晓东(1964-),男,天津人,教授,博士,CCF高级会员,主要研究方向:医学图像处理、医疗信息系统集成;王昊(1984-),男,河北保定人,工程师,硕士研究生,CCF会员,主要研究方向:图像处理;郭宏(1974-),男,天津人,工程师,硕士研究生,主要研究方向:软件工程;郭军(1972-),男,四川成都人,助理实验师,主要研究方向:实验技术。
  • 基金资助:
    国家自然科学基金资助项目;国家自然科学基金资助项目;天津市应用基础研究计划项目

model on cartoon-texture decomposition based on curvelet transform and sparse representation

KANG Xiao-dong1,WANG Hao1,2,GUO Hong3,GUO Jun1   

  1. 1. School of Medical Imaging, Tianjin Medical University, Tianjin 300070, China
    2. Affiliated Hospital of Hebei University, Baoding Hebei 071000, China
    3. Tianjin Medical University General Hospital, Tianjin 300070, China
  • Received:2012-05-03 Revised:2012-06-01 Online:2012-10-23 Published:2012-10-01
  • Contact: KANG Xiao-dong

摘要: CT图像去噪恢复是医学影像图像处理的基础环节。为解决卡通—纹理模型在医学图像去噪应用中计算困难和精度低的问题,对卡通—纹理模型分解方法进行了扩展。首先,以曲线波变换描述图像卡通—纹理模型中的结构部分;其次,以更稀疏的对偶树复小波变换描述图像卡通—纹理模型中的纹理部分;最后,建立了结合曲线波和稀疏表达的图像卡通—纹理分解模型,并讨论了模型的分解算法。仿真实验结果表明,新方法可有效地解决医学影像图像去噪算法中迭代计算量大的问题,并可提高处理后图像的质量。

关键词: 图像恢复, 卡通—纹理分解, 曲线波变换, 稀疏表达, 对偶树复小波变换

Abstract: CT image denoising restoration is a basic procedure in medical image processing. Cartoon-texture decomposition method was extended in order to resolve the problems of computational difficulty and low precision while applying cartoon-texture models in medical image denoising. First, the structure of cartoon-texture model was described by curvelet transform. Second, the texture of cartoon-texture decomposition was described using more sparse dual-tree complex wavelet transform. Third, an image cartoon images-texture model was established by combining curvelet transform and sparse representation. The algorithms of cartoon-texture model were discussed at last. The simulation experimental results show that the new method can effectively resolve the problem of large amount of iterative calculation using medical image denoising algorithm, and the image quality after processing can be improved as well.

Key words: image restoration, cartoon-texture decomposition, curvelet transform, sparse representation, dual-tree complex wavelet transform