文章基本信息

标题：Image decomposition using a second-order variational model and wavelet shrinkage
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作者：Minh-Phuong Tran
期刊名称：ELCVIA: electronic letters on computer vision and image analysis
印刷版ISSN：1577-5097
出版年度：2019
卷号：18
期号：1
页码：92-107
DOI：10.5565/rev/elcvia.1162
出版社：Centre de Visió per Computador
摘要：The paper is devoted to the new model for image decomposition, that splits an image $f$ into three components $u+v+\omega$ , with $u$ a piecewise -smooth or the ``cartoon'' component, $v$ a texture component and $\omega$ the noise part in variational approach. This decomposition model is in fact incorporates the advantages of two preceding models: the second-order total variation minimization of Rudin - Osher - Fatemi ( ROF2 ), and wavelet shrinkage for oscillatory functions. This decomposition model is presented as an extension of the three components decomposition algorithm of Aujol et al. in \cite {JAC}. It also continues the idea introduced previously by authors in \cite {TPB}, for two components decomposition model. The ROF2 model was first proposed by Bergounioux et al. in \cite {BP}, it is an improved regularization method to overcome the undesirable staircasing effect. The wavelet shrinkage is well combined to separate the oscillating part due to texture from that due to noise. Experimental results validate the proposed algorithm and demonstrate that the image decomposition model presents effective and comparable performance to other state-of-the-art models.
关键词：Image decomposition model; second-order total variation; ROF2 model; wavelet shrinkage