Wasserstein Regularization of Imaging Problem
Peyré, Gabriel; Rabin, Julien (2011), Wasserstein Regularization of Imaging Problem, 18th IEEE International Conference on Image Processing (ICIP), 2011 - Proceedings, IEEE, p. 1541-1544
Type
Communication / ConférenceExternal document link
http://hal.archives-ouvertes.fr/hal-00591279/fr/Date
2011Conference title
ICIP 2011 : 2011 IEEE International Conference on Image ProcessingConference date
2011-09Conference city
BruxellesConference country
BelgiqueBook title
18th IEEE International Conference on Image Processing (ICIP), 2011 - ProceedingsPublisher
IEEE
ISBN
978-1-4577-1304-0
Pages
1541-1544
Publication identifier
Metadata
Show full item recordAbstract (EN)
This paper introduces a novel and generic framework embedding statistical constraints for variational problems. We resort to the theory of Monge-Kantorovich optimal mass transport to define penalty terms depending on statistics from images. To cope with the computation time issue of the corresponding Wasserstein distances involved in this approach, we propose an approximate variational formulation for statistics represented as point clouds. We illustrate this framework on the problem of regularized color specification. This is achieved by combining the proposed approximate Wasserstein constraint on color statistics with a generic geometric-based regularization term in a unified variational minimization problem. We believe that this methodology may lead to some other interesting applications in image processing, such as medical imaging modification, texture synthesis, etc.Subjects / Keywords
color and contrast modification; Gradient descent; Image regularization; Energy minimization; Variational modelRelated items
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