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Unified Theory for Recovery of Sparse Signals in a General Transform Domain
Kiryung Lee, Yanjun Li, Kyong Hwan Jin +1
Compressed sensing provided a data-acquisition paradigm for sparse signals. Remarkably, it has been shown that practical algorithms provide robust recovery from noisy linear measur…
Deep Residual Learning for Compressed Sensing CT Reconstruction via Persistent Homology Analysis
Yo Seob Han, Jaejun Yoo, Jong Chul Ye
Recently, compressed sensing (CS) computed tomography (CT) using sparse projection views has been extensively investigated to reduce the potential risk of radiation to patient. How…
Beyond Deep Residual Learning for Image Restoration: Persistent Homology-Guided Manifold Simplification
Woong Bae, Jaejun Yoo, Jong Chul Ye
The latest deep learning approaches perform better than the state-of-the-art signal processing approaches in various image restoration tasks. However, if an image contains many pat…
A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction
Eunhee Kang, Junhong Min, Jong Chul Ye
Due to the potential risk of inducing cancers, radiation dose of X-ray CT should be reduced for routine patient scanning. However, in low-dose X-ray CT, severe artifacts usually oc…
A Joint Sparse Recovery Framework for Accurate Reconstruction of Inclusions in Elastic Media
Jaejun Yoo, Younghoon Jung, Mikyoung Lim +2
A robust algorithm is proposed to reconstruct the spatial support and the Lamé parameters of multiple inclusions in a homogeneous background elastic material using a few measuremen…
Two-Dimensional Elastic Scattering Coefficients and Enhancement of Nearly Elastic Cloaking
Tasawar Abbas, Habib Ammari, Guanghui Hu +2
The concept of scattering coefficients has played a pivotal role in a broad range of inverse scattering and imaging problems in acoustic, and electromagnetic media. In view of thei…