Multi-Modality Deep Network for JPEG Artifacts Reduction

no code implementations • 4 May 2023 • Xuhao Jiang, Weimin Tan, Qing Lin, Chenxi Ma, Bo Yan, Liquan Shen

In recent years, many convolutional neural network-based models are designed for JPEG artifacts reduction, and have achieved notable progress.

Contrastive Learning Image Compression +1

Geometry-Aware Reference Synthesis for Multi-View Image Super-Resolution

no code implementations • 18 Jul 2022 • Ri Cheng, Yuqi Sun, Bo Yan, Weimin Tan, Chenxi Ma

To address these problems, we propose the MVSRnet, which uses geometry information to extract sharp details from all LR multi-view to support the SR of the LR input view.

Image Super-Resolution Video Super-Resolution

Perception-Oriented Stereo Image Super-Resolution

no code implementations • 14 Jul 2022 • Chenxi Ma, Bo Yan, Weimin Tan, Xuhao Jiang

Recent studies of deep learning based stereo image super-resolution (StereoSR) have promoted the development of StereoSR.

Disparity Estimation Stereo Image Super-Resolution

Rethinking Super-Resolution as Text-Guided Details Generation

no code implementations • 14 Jul 2022 • Chenxi Ma, Bo Yan, Qing Lin, Weimin Tan, Siming Chen

To enhance the semantic accuracy and the visual quality of the reconstructed image, we explore the multi-modal fusion learning in SISR by proposing a Text-Guided Super-Resolution (TGSR) framework, which can effectively utilize the information from the text and image modalities.

Image Super-Resolution