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Convolutional Sparse and Low-Rank Coding-Based Image Decomposition.

Authors
  • Zhang, He
  • Patel, Vishal M
Type
Published Article
Journal
IEEE Transactions on Image Processing
Publisher
Institute of Electrical and Electronics Engineers
Publication Date
May 01, 2018
Volume
27
Issue
5
Pages
2121–2133
Identifiers
DOI: 10.1109/TIP.2017.2786469
PMID: 29432095
Source
Medline
License
Unknown

Abstract

We propose novel convolutional sparse and low-rank coding-based methods for cartoon and texture decomposition. In our method, we first learn a set of generic filters that can efficiently represent cartoon-and texture-type images. Then, using these learned filters, we propose two optimization frameworks to decompose a given image into cartoon and texture components: convolutional sparse coding-based image decomposition; and convolutional low-rank coding-based image decomposition. By working directly on the whole image, the proposed image separation algorithms do not need to divide the image into overlapping patches for leaning local dictionaries. The shift-invariance property is directly modeled into the objective function for learning filters. Extensive experiments show that the proposed methods perform favorably compared with state-of-the-art image separation methods.

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