Search Results for author: Jun Lv

Found 11 papers, 5 papers with code

Transformer-empowered Multi-scale Contextual Matching and Aggregation for Multi-contrast MRI Super-resolution

1 code implementation CVPR 2022 Guangyuan Li, Jun Lv, Yapeng Tian, Qi Dou, Chengyan Wang, Chenliang Xu, Jing Qin

However, existing methods still have two shortcomings: (1) they neglect that the multi-contrast features at different scales contain different anatomical details and hence lack effective mechanisms to match and fuse these features for better reconstruction; and (2) they are still deficient in capturing long-range dependencies, which are essential for the regions with complicated anatomical structures.

Super-Resolution

HIPA: Hierarchical Patch Transformer for Single Image Super Resolution

no code implementations19 Mar 2022 Qing Cai, Yiming Qian, Jinxing Li, Jun Lv, Yee-Hong Yang, Feng Wu, David Zhang

Transformer-based architectures start to emerge in single image super resolution (SISR) and have achieved promising performance.

Image Super-Resolution Single Image Super Resolution

SAGCI-System: Towards Sample-Efficient, Generalizable, Compositional, and Incremental Robot Learning

no code implementations29 Nov 2021 Jun Lv, Qiaojun Yu, Lin Shao, Wenhai Liu, Wenqiang Xu, Cewu Lu

We apply our system to perform articulated object manipulation tasks, both in the simulation and the real world.

High-Resolution Pelvic MRI Reconstruction Using a Generative Adversarial Network with Attention and Cyclic Loss

no code implementations21 Jul 2021 Guangyuan Li, Jun Lv, Xiangrong Tong, Chengyan Wang, Guang Yang

Magnetic resonance imaging (MRI) is an important medical imaging modality, but its acquisition speed is quite slow due to the physiological limitations.

MRI Reconstruction Super-Resolution

Transfer Learning Enhanced Generative Adversarial Networks for Multi-Channel MRI Reconstruction

1 code implementation17 May 2021 Jun Lv, Guangyuan Li, Xiangrong Tong, Weibo Chen, Jiahao Huang, Chengyan Wang, Guang Yang

The transfer learning results for the knee and liver were superior to those of the PI-GAN model trained with its own dataset using a smaller number of training cases.

MRI Reconstruction Transfer Learning

Generative Adversarial Networks (GAN) Powered Fast Magnetic Resonance Imaging -- Mini Review, Comparison and Perspectives

no code implementations4 May 2021 Guang Yang, Jun Lv, Yutong Chen, Jiahao Huang, Jin Zhu

However, one drawback of MRI is its comparatively slow scanning and reconstruction compared to other image modalities, limiting its usage in some clinical applications when imaging time is critical.

Compressive Sensing MRI Reconstruction

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