most citedMulti-Scale Boosted Dehazing Network with Dense Feature Fusion

63 citations · 68 across the 3 of their papers we have counts for

collaborators

6 papers

cs.CV2020

Non-Local Spatial Propagation Network for Depth Completion

Jinsun Park, Kyungdon Joo, Zhe Hu +2

In this paper, we propose a robust and efficient end-to-end non-local spatial propagation network for depth completion. The proposed network takes RGB and sparse depth images as in…

cs.CV202063 cited

Multi-Scale Boosted Dehazing Network with Dense Feature Fusion

Hang Dong, Jinshan Pan, Lei Xiang +4

In this paper, we propose a Multi-Scale Boosted Dehazing Network with Dense Feature Fusion based on the U-Net architecture. The proposed method is designed based on two principles,…

cs.CV2020

Gated Fusion Network for Degraded Image Super Resolution

Xinyi Zhang, Hang Dong, Zhe Hu +3

Single image super resolution aims to enhance image quality with respect to spatial content, which is a fundamental task in computer vision. In this work, we address the task of si…

cs.CV20185 cited

Efficient Super Resolution Using Binarized Neural Network

Yinglan Ma, Hongyu Xiong, Zhe Hu +1

Deep convolutional neural networks (DCNNs) have recently demonstrated high-quality results in single-image super-resolution (SR). DCNNs often suffer from over-parametrization and l…

cs.CV2018

Gated Fusion Network for Joint Image Deblurring and Super-Resolution

Xinyi Zhang, Hang Dong, Zhe Hu +3

Single-image super-resolution is a fundamental task for vision applications to enhance the image quality with respect to spatial resolution. If the input image contains degraded pi…

cs.CV2018

Learning to Deblur Images with Exemplars

Jinshan Pan, Wenqi Ren, Zhe Hu +1

Human faces are one interesting object class with numerous applications. While significant progress has been made in the generic deblurring problem, existing methods are less effec…