most citedLimited Angle Tomography for Transmission X-Ray Microscopy Using Deep Learning

6 citations · 13 across the 5 of their papers we have counts for

collaborators

6 papers

eess.IV20203 cited

Robustness Investigation on Deep Learning CT Reconstruction for Real-Time Dose Optimization

Chang Liu, Yixing Huang, Joscha Maier +3

In computed tomography (CT), automatic exposure control (AEC) is frequently used to reduce radiation dose exposure to patients. For organ-specific AEC, a preliminary CT reconstruct…

eess.IV20203 cited

Data Consistent CT Reconstruction from Insufficient Data with Learned Prior Images

Yixing Huang, Alexander Preuhs, Michael Manhart +2

Image reconstruction from insufficient data is common in computed tomography (CT), e.g., image reconstruction from truncated data, limited-angle data and sparse-view data. Deep lea…

eess.IV20206 cited

Limited Angle Tomography for Transmission X-Ray Microscopy Using Deep Learning

Yixing Huang, Shengxiang Wang, Yong Guan +1

In transmission X-ray microscopy (TXM) systems, the rotation of a scanned sample might be restricted to a limited angular range to avoid collision to other system parts or high att…

eess.IV20191 cited

Field of View Extension in Computed Tomography Using Deep Learning Prior

Yixing Huang, Lei Gao, Alexander Preuhs +1

In computed tomography (CT), data truncation is a common problem. Images reconstructed by the standard filtered back-projection algorithm from truncated data suffer from cupping ar…

cs.CV2019

Superpixel-Based Background Recovery from Multiple Images

Lei Gao, Yixing Huang, Andreas Maier

In this paper, we propose an intuitive method to recover background from multiple images. The implementation consists of three stages: model initialization, model update, and backg…

eess.IV2019

Data Consistent Artifact Reduction for Limited Angle Tomography with Deep Learning Prior

Yixing Huang, Alexander Preuhs, Guenter Lauritsch +3

Robustness of deep learning methods for limited angle tomography is challenged by two major factors: a) due to insufficient training data the network may not generalize well to uns…