143 citations · 418 across the 21 of their papers we have counts for
5 papers · 1 filter
Variational Bayesian Dropout with a Hierarchical Prior
Yuhang Liu, Wenyong Dong, Lei Zhang +2
Variational dropout (VD) is a generalization of Gaussian dropout, which aims at inferring the posterior of network weights based on a log-uniform prior on them to learn these weigh…
End-to-End Diagnosis and Segmentation Learning from Cardiac Magnetic Resonance Imaging
Gerard Snaauw, Dong Gong, Gabriel Maicas +4
Cardiac magnetic resonance (CMR) is used extensively in the diagnosis and management of cardiovascular disease. Deep learning methods have proven to deliver segmentation results co…
MPTV: Matching Pursuit Based Total Variation Minimization for Image Deconvolution
Dong Gong, Mingkui Tan, Qinfeng Shi +2
Total variation (TV) regularization has proven effective for a range of computer vision tasks through its preferential weighting of sharp image edges. Existing TV-based methods, ho…
Learning Deep Gradient Descent Optimization for Image Deconvolution
Dong Gong, Zhen Zhang, Qinfeng Shi +3
As an integral component of blind image deblurring, non-blind deconvolution removes image blur with a given blur kernel, which is essential but difficult due to the ill-posed natur…
An Adaptive Markov Random Field for Structured Compressive Sensing
Suwichaya Suwanwimolkul, Lei Zhang, Dong Gong +4
Exploiting intrinsic structures in sparse signals underpins the recent progress in compressive sensing (CS). The key for exploiting such structures is to achieve two desirable prop…