173 citations · 260 across the 10 of their papers we have counts for
12 papers
End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT
Yoseob Han, Dufan Wu, Kyungsang Kim +1
Objective: There exist several X-ray computed tomography (CT) scanning strategies to reduce a radiation dose, such as (1) sparse-view CT, (2) low-dose CT, and (3) region-of-interes…
Semi-supervised learning for continuous emotional intensity controllable speech synthesis with disentangled representations
Yoori Oh, Juheon Lee, Yoseob Han +1
Recent text-to-speech models have reached the level of generating natural speech similar to what humans say. But there still have limitations in terms of expressiveness. The existi…
AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results
Pengxu Wei, Hannan Lu, Radu Timofte +68
This paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020. This ch…
Differentiated Backprojection Domain Deep Learning for Conebeam Artifact Removal
Yoseob Han, Junyoung Kim, Jong Chul Ye
Conebeam CT using a circular trajectory is quite often used for various applications due to its relative simple geometry. For conebeam geometry, Feldkamp, Davis and Kress algorithm…
One Network to Solve All ROIs: Deep Learning CT for Any ROI using Differentiated Backprojection
Yoseob Han, Jong Chul Ye
Computed tomography for region-of-interest (ROI) reconstruction has advantages of reducing X-ray radiation dose and using a small detector. However, standard analytic reconstructio…
k-Space Deep Learning for Reference-free EPI Ghost Correction
Juyoung Lee, Yoseob Han, Jae-Kyun Ryu +2
Nyquist ghost artifacts in EPI are originated from phase mismatch between the even and odd echoes. However, conventional correction methods using reference scans often produce erro…
Deep Learning Reconstruction for 9-View Dual Energy CT Baggage Scanner
Yoseob Han, Jingu Kang, Jong Chul Ye
For homeland and transportation security applications, 2D X-ray explosive detection system (EDS) have been widely used, but they have limitations in recognizing 3D shape of the hid…
Deep Learning Interior Tomography for Region-of-Interest Reconstruction
Yoseob Han, Jawook Gu, Jong Chul Ye
Interior tomography for the region-of-interest (ROI) imaging has advantages of using a small detector and reducing X-ray radiation dose. However, standard analytic reconstruction s…
Framing U-Net via Deep Convolutional Framelets: Application to Sparse-view CT
Yoseob Han, Jong Chul Ye
X-ray computed tomography (CT) using sparse projection views is a recent approach to reduce the radiation dose. However, due to the insufficient projection views, an analytic recon…
Deep Convolutional Framelets: A General Deep Learning Framework for Inverse Problems
Jong Chul Ye, Yoseob Han, Eunju Cha
Recently, deep learning approaches with various network architectures have achieved significant performance improvement over existing iterative reconstruction methods in various im…
Deep Learning with Domain Adaptation for Accelerated Projection-Reconstruction MR
Yo Seob Han, Jaejun Yoo, Jong Chul Ye
Purpose: The radial k-space trajectory is a well-established sampling trajectory used in conjunction with magnetic resonance imaging. However, the radial k-space trajectory require…
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…