期刊
SIAM JOURNAL ON IMAGING SCIENCES
卷 8, 期 4, 页码 2519-2557出版社
SIAM PUBLICATIONS
DOI: 10.1137/141002293
关键词
sparsifying transforms; inverse problems; compressed sensing; medical imaging; magnetic resonance imaging; sparse representation; dictionary learning
类别
资金
- National Science Foundation (NSF) [CCF-1018660, CCF-1320953]
- Division of Computing and Communication Foundations
- Direct For Computer & Info Scie & Enginr [1320953] Funding Source: National Science Foundation
Natural signals and images are well known to be approximately sparse in transform domains such as wavelets and discrete cosine transform. This property has been heavily exploited in various applications in image processing and medical imaging. Compressed sensing exploits the sparsity of images or image patches in a transform domain or synthesis dictionary to reconstruct images from undersampled measurements. In this work, we focus on blind compressed sensing, where the underlying sparsifying transform is a priori unknown, and propose a framework to simultaneously reconstruct the underlying image as well as the sparsifying transform from highly undersampled measurements. The proposed block coordinate descent-type algorithms involve highly efficient optimal updates. Importantly, we prove that although the proposed blind compressed sensing formulations are highly nonconvex, our algorithms are globally convergent (i.e., they converge from any initialization) to the set of critical points of the objectives defining the formulations. These critical points are guaranteed to be at least partial global and partial local minimizers. The exact point(s) of convergence may depend on initialization. We illustrate the usefulness of the proposed framework for magnetic resonance image reconstruction from highly undersampled k-space measurements. As compared to previous methods involving the synthesis dictionary model, our approach is much faster, while also providing promising reconstruction quality.
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