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Image decomposition using optimally sparse representations and a variational approach.
- Source :
- Signal, Image & Video Processing; Oct2007, Vol. 1 Issue 4, p287-292, 6p
- Publication Year :
- 2007
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 18631703
- Volume :
- 1
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- Signal, Image & Video Processing
- Publication Type :
- Academic Journal
- Accession number :
- 49453853
- Full Text :
- https://doi.org/10.1007/s11760-007-0020-9