4 citations · 4 across the 1 of their papers we have counts for
5 papers
Learning to Zoom-in via Learning to Zoom-out: Real-world Super-resolution by Generating and Adapting Degradation
Dong Gong, Wei Sun, Qinfeng Shi +2
Most learning-based super-resolution (SR) methods aim to recover high-resolution (HR) image from a given low-resolution (LR) image via learning on LR-HR image pairs. The SR methods…
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…
Adaptive Importance Learning for Improving Lightweight Image Super-resolution Network
Lei Zhang, Peng Wang, Chunhua Shen +4
Deep neural networks have achieved remarkable success in single image super-resolution (SISR). The computing and memory requirements of these methods have hindered their applicatio…
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…
Pushing the Limits of Deep CNNs for Pedestrian Detection
Qichang Hu, Peng Wang, Chunhua Shen +2
Compared to other applications in computer vision, convolutional neural networks have under-performed on pedestrian detection. A breakthrough was made very recently by using sophis…