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

David M. Strong; Peter Blomgren; Tony F. Chan

doi:10.1117/12.279642

14 October 1997 Spatially adaptive local-feature-driven total variation minimizing image restoration

David M. Strong, Peter Blomgren, Tony F. Chan

Proceedings Volume 3167, Statistical and Stochastic Methods in Image Processing II; (1997) https://doi.org/10.1117/12.279642
Event: Optical Science, Engineering and Instrumentation '97, 1997, San Diego, CA, United States

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.

Citation Download Citation

David M. Strong, Peter Blomgren, and Tony F. Chan "Spatially adaptive local-feature-driven total variation minimizing image restoration", Proc. SPIE 3167, Statistical and Stochastic Methods in Image Processing II, (14 October 1997); https://doi.org/10.1117/12.279642

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available