计算机应用 ›› 2012, Vol. 32 ›› Issue (12): 3357-3360.DOI: 10.3724/SP.J.1087.2012.03357

• 图形图像技术 • 上一篇    下一篇

基于小波收缩和正逆扩散结合的优质中值先验图像重建算法

李晓红,张权,刘祎,桂志国   

  1. 中北大学 电子测试技术国家重点实验室, 太原 030051
  • 收稿日期:2012-06-10 修回日期:2012-07-19 发布日期:2012-12-29 出版日期:2012-12-01
  • 通讯作者: 桂志国
  • 作者简介:李晓红(1986-),女,河南商丘人,硕士研究生,主要研究方向:图像处理、医学图像重建;〓张权(1974-),男,山西大同人,讲师,硕士,主要研究方向:图像处理、图像重建;〓刘祎(1987-),女,河南睢县人,博士研究生,主要研究方向:图像处理、医学图像重建;〓桂志国(1972-),男,天津人,教授,博士,主要研究方向:无损检测、图像处理、图像重建。
  • 基金资助:
    国家自然科学基金资助项目;山西省自然科学基金资助项目

High quality median prior image reconstruction algorithm based on wavelet shrinkage and forward-and-backward diffusion

LI Xiao-hong,ZHANG Quan,LIU Yi,GUI Zhi-guo   

  1. National Key Laboratory for Electronic Measurement Technology, North University of China, Taiyuan Shanxi 030051,China
  • Received:2012-06-10 Revised:2012-07-19 Online:2012-12-29 Published:2012-12-01
  • Contact: GUI Zhi-guo

摘要: 针对最大后验(MAP)法对重建图像造成的过度平滑或出现阶梯状边缘伪影等问题,提出了一种基于混合模型的中值先验图像重建算法。首先在中值先验分布的MAP重建的基础上,在每次中值滤波之前引入结合小波收缩和正逆各向异性扩散的滤波器。另外,对于背景区域仍残留有少量噪声的情况下,可以在迭代间的最后,选择加入只针对图像较小梯度阈值区域进行非线性扩散平滑的优良滤波器,从而进一步优化图像。仿真结果表明,该算法在抑制噪声和保持边缘效果方面具有很好的表现,与其他经典传统算法相比,信噪比(SNR)可提高0.9dB~3.8dB。

关键词: 最大后验, 中值先验, 图像重建, 小波收缩, 各向异性扩散

Abstract: A median priori image reconstruction algorithm based on mixed model was put forward to solve the problems of over-smoothness and stepladder edge of reconstructed image by Maximum A Posterior (MAP). First,in the median priori distribution of MAP reconstruction method,the combination of wavelet shrinkage and forward-and-backward anisotropic diffusion filter was introduced before each of median filtering. In addition, if the background area still kept a small amount of noise, the fine filter with a nonlinear diffusion that smoothed the smaller image gradient threshold region could be chosen to join in the last of iteration,so as to optimize the image.The simulation results show that the algorithm has good performance in both lowering noise effect and preserving edges. Compared with other classical algorithms,the Signal-to-Noise Ratio (SNR) can be improved by 0.9dB to 3.8dB.

Key words: Maximum A Posterior (MAP), median priori, image reconstruction, wavelet shrinkage, anisotropic diffusion

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