Speckle-mowing anisotropic diffusion

Deyeon N. Kim, Jinmu Choi, Seongjai Kim

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This article is concerned with a mathematical denoising algorithm which can suppress speckles and preserve fine structures effectively. We introduce a noble speckle-mowing anisotropic diffusion (SMAD) model, incorporating a generalized mean-curvature and a speckle-clutch term. While the generalized mean-curvature smoothens the image and restores fine structures, the speckle-clutch term tries to stop diffusion at non-speckle pixels. It has been numerically verified that in speckle reduction, the new mathematical model restores images satisfactorily and outperforms over nonlinear median filters, measured in PSNR and visual inspection.

Original languageEnglish
Title of host publicationProceedings of the 2007 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2007
Pages282-288
Number of pages7
Publication statusPublished - 2007
Event2007 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2007 - Las Vegas, NV, United States
Duration: 25 Jun 200728 Jun 2007

Publication series

NameProceedings of the 2007 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2007

Conference

Conference2007 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2007
Country/TerritoryUnited States
CityLas Vegas, NV
Period25/06/0728/06/07

Keywords

  • Generalized mean-curvature
  • Nonlinear median filter
  • Permutation center weighted median (PCWM) filter
  • Speckle-clutch term
  • Speckle-mowing anisotropic diffusion model

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