1 citations · 1 across the 4 of their papers we have counts for
4 papers
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…
Learning Parallax Transformer Network for Stereo Image JPEG Artifacts Removal
Xuhao Jiang, Weimin Tan, Ri Cheng +2
Under stereo settings, the performance of image JPEG artifacts removal can be further improved by exploiting the additional information provided by a second view. However, incorpor…
Perception-Oriented Stereo Image Super-Resolution
Chenxi Ma, Bo Yan, Weimin Tan +1
Recent studies of deep learning based stereo image super-resolution (StereoSR) have promoted the development of StereoSR. However, existing StereoSR models mainly concentrate on im…