Image decomposition using optimally sparse representations and a variational approach

被引：2

作者：

Jiang L. ^{[1
]}

Feng X. ^{[1
]}

Yin H. ^{[1
]}

机构：

[1] Department of Mathematics, Xidian University, Xi'an, Shaanxi 710071

来源：

Signal, Image and Video Processing | 2007年 / 1卷 / 4期

关键词：

BPDN; Cartoon; Curvelet; DT CWT; Texture; TV;

D O I：

10.1007/s11760-007-0020-9

中图分类号：

学科分类号：

摘要：

In this paper, a new method which combines the basis pursuit denoising algorithm (BPDN) and the total variation (TV) regularization scheme is presented for separating images into texture and cartoon parts. It is a modification of the model [1]. In this process, two appropriate dictionaries are used, one for the representation of texture parts-the dual tree complex wavelet transform (DT CWT) and the other for the cartoon parts-the second generation of curvelet transform. To direct the separation process and reduce the pseudo-Gibbs phenomenon, the curvelet transform is followed by a projected regularization method for cartoon parts. Experimental results show that new method cannot only decompose better for a given image but also reduce the runtime, in comparison to the MCA approach. © 2007 Springer-Verlag London Limited.

引用

页码：287 / 292

页数：5

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