Spatially adaptive local-feature-driven total variation minimizing image restoration

David M. Strong, Peter Blomgren, Tony Chan

Research output: Contribution to journalConference articlepeer-review

40 Scopus citations

Abstract

Total variation (TV) minimizing image restoration is a fairly new approach to image restoration, and has been shown both analytically and empirically to be quite effective. Our primary concern here is to develop a spatially adaptive TV minimizing restoration scheme. One way of accomplishing this is to locally weight the measure or computation of the total variation of the image. The weighting factor is chosen to be inversely proportional to the likelihood of the presence of an edge at each discrete location. This allows for less regularization where edges are present and more regularization where there are no edges, which results in a spatially varying balance between noise removal and detail preservation, leading to better overall image restoration. In this paper, the likelihood of edge presence if determined from a partially restored image. The results are best for images with piecewise constant image features.

Original languageEnglish (US)
Pages (from-to)222-233
Number of pages12
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume3167
DOIs
StatePublished - Dec 1 1997
EventStatistical and Stochastic Methods in Image Processing II - San Diego, CA, United States
Duration: Jul 31 1997Jul 31 1997

Keywords

  • Adaptive
  • Image restoration
  • Total variation

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Spatially adaptive local-feature-driven total variation minimizing image restoration'. Together they form a unique fingerprint.

Cite this