Computer Engineering and Applications ›› 2011, Vol. 47 ›› Issue (13): 204-207.

• 图形、图像、模式识别 • Previous Articles     Next Articles

Image denoising via adaptive kernel regression and multiframe processing

DONG Xiaoming1,CUI Jing1,2,LIU Benyong1,2   

  1. 1.College of Computer Science and Information,Guizhou University,Guiyang 550025,China
    2.Institute of Intelligent Information Processing,Guizhou University,Guiyang 550025,China
  • Received:1900-01-01 Revised:1900-01-01 Online:2011-05-01 Published:2011-05-01

多帧融合自适应核回归图像去噪

董晓明1,崔 静1,2,刘本永1,2   

  1. 1.贵州大学 计算机科学与信息学院,贵阳 550025
    2.贵州大学 智能信息处理研究所,贵阳 550025

Abstract: Image denoising is an important step in many systems involving image processing.Traditionally,median filtering and Wiener filtering are two of the most popular image denoising methods.In recent years,methods based on wavelet transform and kernel regression draw much attention in the related study.Generally,the kernel regression method performs better than methods based on wavelet transform.This study shows that the kernel regression method may be easily extended to multiframe processing,and the performance is further improved.

Key words: image denoising, multiframe processing, adaptive kernel regression

摘要: 图像去噪是数字图像处理的重要内容,常用的传统方法包括空域中值滤波和维纳滤波,近年来基于小波变换、核回归等的去噪方法备受关注,基于单帧处理的实验发现核回归方法有更好的去噪效果。在理论上将核回归方法推广到多帧情况,并进行了对比实验,结果表明多帧处理能够进一步改进去噪效果。

关键词: 图像去噪, 多帧处理, 自适应核回归