Image Denoising Method with Unknown Noise Model
DOI:
CSTR:
Author:
Affiliation:

Clc Number:

Fund Project:

  • Article
  • |
  • Figures
  • |
  • Metrics
  • |
  • Reference
  • |
  • Related
  • |
  • Cited by
  • |
  • Materials
  • |
  • Comments
    Abstract:

    Noise of image does not only reduce the quality of image but also interferes with the validity of correlative processing arithmetic seriously. Therefore, effective and robust methods of removing noise are very important for various signal processing. To improve quality of the actual distance remote control image in the paper, MeanShift algorithm of no parameter estimation is introduced and five methods of removing image noise are compared. Firstly, based on the reasonable assume to be noise model to remove image noise. Kalman filtering is used to remove noise. Then median filtering, mean filtering and Wiener filtering are performed separately. Finally, MeanShift algorithm is applied to remove noise. Experimental results show the five methods which are used in this paper reduce the noise in the night sky image to varying degrees. Moreover MeanShift algorithm in image denoising keeps the detail information and edge character of the image better. Compared with the five traditional filtering methods, MeanShift algorithm shows the advantage in image denoising of the actual night sky image background noise.

    Reference
    Related
    Cited by
Get Citation

彭宏,赵海英,黄甜甜.一类未知噪声模型的图像去噪方法.计算机系统应用,2011,20(12):205-210

Copy
Share
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
History
  • Received:April 11,2011
  • Revised:June 15,2011
  • Adopted:
  • Online:
  • Published:
Article QR Code
You are the firstVisitors
Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-3
Address:4# South Fourth Street, Zhongguancun,Haidian, Beijing,Postal Code:100190
Phone:010-62661041 Fax: Email:csa (a) iscas.ac.cn
Technical Support:Beijing Qinyun Technology Development Co., Ltd.

Beijing Public Network Security No. 11040202500063