模式识别与人工智能
Sunday, Apr. 13, 2025 Home      About Journal      Editorial Board      Instructions      Ethics Statement      Contact Us                   中文
  2012, Vol. 25 Issue (4): 642-647    DOI:
Orignal Article Current Issue| Next Issue| Archive| Adv Search |
Anisotropic Diffusion Model Combined with Local Entropy
ZHAO De, HE Chuan-Jiang, CHEN Qiang
College of Mathematics and Statistics,Chongqing University,Chongqing 401331

Download: PDF (2358 KB)   HTML (1 KB) 
Export: BibTeX | EndNote (RIS)      
Abstract  Perona-Malik (P-M) model is a classical anisotropic diffusion denoising model, but it is not able to preserve the important details effectively such as the texture of the image. To address this problem, an improved P-M model based on local entropy is proposed. The diffusion coefficient of the model not only depends on the image gradient, but also depends on the local region information described by local entropy. The experimental results show that the proposed model removes noises effectively, preserves the boundaries better, and maintains important details of the image well.
Key wordsImage Filtering      Anisotropic Diffusion      Perona-Malik Model      Local Entropy      Partial Differential Equation     
Received: 05 May 2011     
ZTFLH: TP391.4  
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
ZHAO De
HE Chuan-Jiang
CHEN Qiang
Cite this article:   
ZHAO De,HE Chuan-Jiang,CHEN Qiang. Anisotropic Diffusion Model Combined with Local Entropy[J]. , 2012, 25(4): 642-647.
URL:  
http://manu46.magtech.com.cn/Jweb_prai/EN/      OR     http://manu46.magtech.com.cn/Jweb_prai/EN/Y2012/V25/I4/642
Copyright © 2010 Editorial Office of Pattern Recognition and Artificial Intelligence
Address: No.350 Shushanhu Road, Hefei, Anhui Province, P.R. China Tel: 0551-65591176 Fax:0551-65591176 Email: bjb@iim.ac.cn
Supported by Beijing Magtech  Email:support@magtech.com.cn