3 citations · 4 across the 5 of their papers we have counts for
5 papers
Cuboid-Net: A Multi-Branch Convolutional Neural Network for Joint Space-Time Video Super Resolution
Congrui Fu, Hui Yuan, Hongji Xu +2
The demand for high-resolution videos has been consistently rising across various domains, propelled by continuous advancements in science, technology, and societal. Nonetheless, c…
Global Spatial-Temporal Information-based Residual ConvLSTM for Video Space-Time Super-Resolution
Congrui Fu, Hui Yuan, Shiqi Jiang +3
By converting low-frame-rate, low-resolution videos into high-frame-rate, high-resolution ones, space-time video super-resolution techniques can enhance visual experiences and faci…
RFD-ECNet: Extreme Underwater Image Compression with Reference to Feature Dictionar
Mengyao Li, Liquan Shen, Peng Ye +2
Thriving underwater applications demand efficient extreme compression technology to realize the transmission of underwater images (UWIs) in very narrow underwater bandwidth. Howeve…
Multi-Modality Deep Network for JPEG Artifacts Reduction
Xuhao Jiang, Weimin Tan, Qing Lin +3
In recent years, many convolutional neural network-based models are designed for JPEG artifacts reduction, and have achieved notable progress. However, few methods are suitable for…
Multi-Modality Deep Network for Extreme Learned Image Compression
Xuhao Jiang, Weimin Tan, Tian Tan +2
Image-based single-modality compression learning approaches have demonstrated exceptionally powerful encoding and decoding capabilities in the past few years , but suffer from blur…