Image enhancement using fourth order partial differential equations

Yu Li You, M. Kaveh

Research output: Contribution to journalConference articlepeer-review

14 Scopus citations

Abstract

A class of fourth order partial differential equations (PDE) axe proposed to optimize the trade-off between noise removal and edge preservation. The time evolution of these PDEs seeks to minimize a functional which is an increasing function of the absolute value of the Laplacian of the image intensity function, hence it generates a family of images of increasing degree of smoothness. Since the Laplacian of a plane image is zero, the stationary points of this functional or this class of PDEs are images whose intensity functions are a union of plane images of various boundaries. This kind of images look more natural than step images which are the stationary points of anisotropic diffusion (second order PDEs), so the proposed PDEs are able to achieve comparable degree of noise removal while avoiding the blocky effects widely seen in images processed by anisotropic diffusion. However, the proposed fourth order PDEs tend to develop speckle artifacts which may be characterized as isolated white and/or black dots, but they can be easily alleviated by simple despeckle algorithms such as the one shown in this paper.

Original languageEnglish (US)
Pages (from-to)1677-1681
Number of pages5
JournalConference Record of the Asilomar Conference on Signals, Systems and Computers
Volume2
StatePublished - Dec 1 1998
EventProceedings of the 1998 32nd Asilomar Conference on Signals, Systems & Computers. Part 1 (of 2) - Pacific Grove, CA, USA
Duration: Nov 1 1998Nov 4 1998

Fingerprint Dive into the research topics of 'Image enhancement using fourth order partial differential equations'. Together they form a unique fingerprint.

Cite this