Journal article
Adaptive Dictionary Learning in Sparse Gradient Domain for Image Recovery
IEEE transactions on image processing, Vol.22(12), pp.4652-4663
12/01/2013
DOI: 10.1109/TIP.2013.2277798
PMID: 23955749
Abstract
Image recovery from undersampled data has always been challenging due to its implicit ill-posed nature but becomes fascinating with the emerging compressed sensing (CS) theory. This paper proposes a novel gradient based dictionary learning method for image recovery, which effectively integrates the popular total variation (TV) and dictionary learning technique into the same framework. Specifically, we first train dictionaries from the horizontal and vertical gradients of the image and then reconstruct the desired image using the sparse representations of both derivatives. The proposed method enables local features in the gradient images to be captured effectively, and can be viewed as an adaptive extension of the TV regularization. The results of various experiments on MR images consistently demonstrate that the proposed algorithm efficiently recovers images and presents advantages over the current leading CS reconstruction approaches.
Details
- Title: Subtitle
- Adaptive Dictionary Learning in Sparse Gradient Domain for Image Recovery
- Creators
- Qiegen Liu - Shenzhen Institutes of Advanced TechnologyShanshan Wang - Shanghai Jiao Tong UniversityLeslie Ying - University at Buffalo, State University of New YorkXi Peng - Chinese Acad Sci, Shenzhen Key Lab MRI, Shenzhen Inst Adv Technol, Paul C Lauterbur Res Ctr Biomed Imaging, Shenzhen 518055, Peoples R ChinaYanjie Zhu - Shenzhen Institutes of Advanced TechnologyDong Liang - Shenzhen Institutes of Advanced Technology
- Resource Type
- Journal article
- Publication Details
- IEEE transactions on image processing, Vol.22(12), pp.4652-4663
- Publisher
- IEEE
- DOI
- 10.1109/TIP.2013.2277798
- PMID
- 23955749
- ISSN
- 1057-7149
- eISSN
- 1941-0042
- Number of pages
- 12
- Grant note
- 61102043; 61141007; 61250005; 81071147; 81120108012 / National Natural Science Foundation of China; National Natural Science Foundation of China (NSFC) 20132BAB211030 / Natural Science Foundation of Jiangxi Province KQCX20120816155710259 / Shenzhen Peacock Plan CBET-0846514 / National Science Foundation; National Science Foundation (NSF) JC201005270317A; JC201104220219A / Basic Research Program of Shenzhen 2011623084 / Chinese Scholarship Council; China Scholarship Council
- Language
- English
- Date published
- 12/01/2013
- Academic Unit
- Radiology
- Record Identifier
- 9984446449602771
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