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Proceedings Paper

Spatially adaptive local-feature-driven total variation minimizing image restoration
Author(s): David M. Strong; Peter Blomgren; Tony F. Chan
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Paper 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.

Paper Details

Date Published: 14 October 1997
PDF: 12 pages
Proc. SPIE 3167, Statistical and Stochastic Methods in Image Processing II, (14 October 1997); doi: 10.1117/12.279642
Show Author Affiliations
David M. Strong, Univ. of California/Los Angeles (United States)
Peter Blomgren, Univ. of California/Los Angeles (United States)
Tony F. Chan, Univ. of California/Los Angeles (United States)

Published in SPIE Proceedings Vol. 3167:
Statistical and Stochastic Methods in Image Processing II
Francoise J. Preteux; Jennifer L. Davidson; Edward R. Dougherty, Editor(s)

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