Image denoising using hybrid model with edge preserving capability

Bo Chen*, Jianhuang Lai, Pong Chi YUEN

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

1 Citation (Scopus)

Abstract

The use of partial differential equations in image processing and computer vision has increased dramatically in recent years. The paper address to image denoising. A new model is introduced by extending αβω (ABO)-model in order to get high fidelity of the denoised images. To solve the model efficiently and reliably, we suggest a simple and symmetrical difference schemes and incorporate them with the essentially nondissipative difference (ENoD) schemes. We remove the impulse and Gaussian noises from different images and compare the PSNR values of the results with traditional filters. Numerical experimental results have shown the new model's effectiveness in restoring images, especially in edge preservation and enhancement.

Original languageEnglish
Title of host publication2006 International Conference on Computational Intelligence and Security, ICCIAS 2006
PublisherIEEE Computer Society
Pages1779-1784
Number of pages6
ISBN (Print)1424406056, 9781424406050
DOIs
Publication statusPublished - 2006
Event2006 International Conference on Computational Intelligence and Security, CIS 2006 - Guangzhou, China
Duration: 3 Oct 20066 Oct 2006
https://ieeexplore.ieee.org/xpl/conhome/4072023/proceeding
https://link.springer.com/book/10.1007/978-3-540-74377-4

Publication series

Name2006 International Conference on Computational Intelligence and Security, ICCIAS 2006
Volume2

Conference

Conference2006 International Conference on Computational Intelligence and Security, CIS 2006
Country/TerritoryChina
CityGuangzhou
Period3/10/066/10/06
Internet address

Scopus Subject Areas

  • Computer Science(all)
  • Control and Systems Engineering

Fingerprint

Dive into the research topics of 'Image denoising using hybrid model with edge preserving capability'. Together they form a unique fingerprint.

Cite this