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Article Dans Une Revue Computer Vision and Image Understanding Année : 2015

High order structural image decomposition by using non-linear and non-convex regularizing objectives

Résumé

The paper addresses structural decomposition of images by using a family of non-linear and non-convex objective functions. These functions rely on p quasi-norm estimation costs in a piecewise constant regularization framework. These objectives make image decomposition into constant cartoon levels and rich textural patterns possible. The paper shows that these regularizing objectives yield image texture-versus-cartoon decompositions that cannot be reached by using standard penalized least square regularizations associated with smooth and convex objectives.
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Dates et versions

hal-01159214 , version 1 (02-06-2015)

Identifiants

Citer

Abdourrahmane Atto, Grégoire Mercier. High order structural image decomposition by using non-linear and non-convex regularizing objectives. Computer Vision and Image Understanding, 2015, http://dx.doi.org/10.1016/j.cviu.2015.04.002. ⟨10.1016/j.cviu.2015.04.002⟩. ⟨hal-01159214⟩
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