- Yoseob Han and Jong Chul Ye,"Framing U-Net via Deep Convolutional Framelets: Application to Sparse-view CT", Special Issue on Machine Learning for Image Reconstruction, IEEE Trans. on Medical Imaging (in press), 2018.
Inspired by the recent theory of {\em deep convolutional framelets}, the main goal of this paper is, therefore, to reveal the
limitation of U-Net and propose new multi-resolution deep learning schemes. In particular, we show that the alternative U-Net variants such as dual frame and the tight frame U-Nets satisfy the so-called frame condition which make them better for effective recovery of high frequency edges in sparse view-CT. Using extensive experiments with real patient data set, we demonstrate that the new network architectures provide better reconstruction performance.
- Eunhee Kang, Won Chang, Jaejun Yoo, and Jong Chul Ye, "Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network", Special Issue on Machine Learning for Image Reconstruction, IEEE Trans. on Medical Imaging (in press), 2018.
- Dongwook Lee, Jaejun Yoo, Sungho Tak and Jong Chul Ye, "Deep Residual Learning for Accelerated MRI using Magnitude and Phase Networks", IEEE Trans on Biomedical Engineering (in press), Invited Paper for Special Section on Deep Learning, 2018.