Title
Image Denoising and Decomposition with Total Variation Minimization and Oscillatory Functions
Abstract
In this paper, we propose a new variational model for image denoising and decomposition, witch combines the total variation minimization model of Rudin, Osher and Fatemi from image restoration, with spaces of oscillatory functions, following recent ideas introduced by Meyer. The spaces introduced here are appropriate to model oscillatory patterns of zero mean, such as noise or texture. Numerical results of image denoising, image decomposition and texture discrimination are presented, showing that the new models decompose better a given image, possible noisy, into cartoon and oscillatory pattern of zero mean, than the standard ones. The present paper develops further the models previously introduced by the authors in Vese and Osher (Modeling textures with total variation minimization and oscillating patterns in image processing, UCLA CAM Report 02-19, May 2002, to appear in Journal of Scientific Computing, 2003). Other recent and related image decomposition models are also discussed.
Year
DOI
Venue
2004
10.1023/B:JMIV.0000011316.54027.6a
Journal of Mathematical Imaging and Vision
Keywords
Field
DocType
functional minimization,partial differential equations,image denoising,image decomposition,texture modeling
Computer vision,Mathematical optimization,Non-local means,Variational model,Image processing,Total variation minimization,Total variation denoising,Image denoising,Artificial intelligence,Image restoration,Partial differential equation,Mathematics
Journal
Volume
Issue
ISSN
20
1-2
1573-7683
Citations 
PageRank 
References 
64
4.74
20
Authors
2
Name
Order
Citations
PageRank
Luminita A. Vese15389302.64
Stanley Osher27973514.62