24 citations · 44 across the 3 of their papers we have counts for
5 papers
Gaussian Kernel Mixture Network for Single Image Defocus Deblurring
Yuhui Quan, Zicong Wu, Hui Ji
Defocus blur is one kind of blur effects often seen in images, which is challenging to remove due to its spatially variant amount. This paper presents an end-to-end deep learning a…
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…