51 citations · 102 across the 6 of their papers we have counts for
5 papers · 1 filter
A Dataset-free Deep learning Method for Low-Dose CT Image Reconstruction
Qiaoqiao Ding, Hui Ji, Yuhui Quan +1
Low-dose CT (LDCT) imaging attracted a considerable interest for the reduction of the object's exposure to X-ray radiation. In recent years, supervised deep learning (DL) has been…
AHP-Net: adaptive-hyper-parameter deep learning based image reconstruction method for multilevel low-dose CT
Qiaoqiao Ding, Yuesong Nan, Hao Gao +1
Low-dose CT (LDCT) imaging is desirable in many clinical applications to reduce X-ray radiation dose to patients. Inspired by deep learning (DL), a recent promising direction of mo…
Deep Bilateral Retinex for Low-Light Image Enhancement
Jinxiu Liang, Yong Xu, Yuhui Quan +3
Low-light images, i.e. the images captured in low-light conditions, suffer from very poor visibility caused by low contrast, color distortion and significant measurement noise. Low…
Rethinking Medical Image Reconstruction via Shape Prior, Going Deeper and Faster: Deep Joint Indirect Registration and Reconstruction
Jiulong Liu, Angelica I. Aviles-Rivero, Hui Ji +1
Indirect image registration is a promising technique to improve image reconstruction quality by providing a shape prior for the reconstruction task. In this paper, we propose a nov…
Low-Dose CT with Deep Learning Regularization via Proximal Forward Backward Splitting
Qiaoqiao Ding, Gaoyu Chen, Xiaoqun Zhang +2
Low dose X-ray computed tomography (LDCT) is desirable for reduced patient dose. This work develops image reconstruction methods with deep learning (DL) regularization for LDCT. Ou…