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Abstract #3448

Integrating Subspace Learning, Manifold Learning, and Sparsity Learning to Reconstruct Image Sequences

Yudu Li1,2, Yue Guan3, Yibo Zhao1,2, Rong Guo1,2, Yao Li4, and Zhi-Pei Liang1,2
1Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 2Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 3Institute for Medical Imaging Technology, Shanghai Jiao Tong University, Shanghai, China, 4School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China


Many imaging applications, such as dynamic imaging and multi-contrast imaging, involve the acquisition of a sequence of images. This work addresses the underlying image reconstruction problem by incorporating priori information such as partial separability, image sparsity, and manifold structure jointly to enable high-quality image reconstruction from highly sparse data. To this end, we propose a new deep learning-based framework that enforces those constraints effectively and consistently. The proposed method has been validated using multi-contrast imaging data and produced impressive results. The image reconstruction framework can be extended for incorporating additional constraints and/or solving other sequential image reconstruction problems.

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