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Transformer_Low_Level_Vision

Transformer for low-level vision applications, such as image restoration (denosing, super resolution, deblur)

2021

image

  • [Peking University] Hanting Chen, Yunhe Wang, Tianyu Guo, Chang Xu, Yiping Deng, Zhenhua Liu, Siwei Ma, Chunjing Xu, Chao Xu, Wen Gao: Pre-Trained Image Processing Transformer. [paper][code]

  • [ETH Zurich] Jingyun Liang Jiezhang Cao, Guolei Sun, Kai Zhang, Luc Van Gool, Radu Timofte: SwinIR: Image Restoration Using Swin Transformer. [paper][code]

  • [Peking University] Zhisheng Lu, Hong Liu, Juncheng Li, Linlin Zhang: Efficient Transformer for Single Image Super-Resolution. [paper]

  • [USTC] Zhendong Wang, Xiaodong Cun, Jianmin Bao, Jianzhuang Liu: Uformer: A General U-Shaped Transformer for Image Restoration. [paper][code]

  • [National University of Defense Technology] Zhengyu Liang, Yingqian Wang, Longguang Wang, Jungang Yang, Shilin Zhou: Light Field Image Super-Resolution with Transformers. [paper][code]

  • [Wuhan Institute of Technology] Yuanzhi Wang, Tao Lu, Yanduo Zhang, Junjun Jiang, Jiaming Wang, Zhongyuan Wang, Jiayi Ma: TANet: A new Paradigm for Global Face Super-resolution via Transformer-CNN Aggregation Network. [paper]

  • [UESTC] Jin-Fan Hu, Ting-Zhu Huang, Liang-Jian Deng: Fusformer: A Transformer-based Fusion Approach for Hyperspectral Image Super-resolution. [paper]

  • [USTB] Chao Yao; Shuaiyong Zhang; Mengyao Yang; Meiqin Liu; Junpeng Qi: Depth Super-Resolution by Texture-Depth Transformer. [paper]

  • [University of Massachusetts Lowell] Dayang Wang, Zhan Wu, Hengyong Yu:TED-net: Convolution-free T2T Vision Transformer-based Encoder-decoder Dilation network for Low-dose CT Denoising. [paper]

  • [Inception Institute of AI] Syed Waqas Zamir, Aditya Arora1 Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang: Restormer: Efficient Transformer for High-Resolution Image Restoration. [paper]

  • [None] Haobo Ji, Xin feng, Wenjie Pei, Jinxing Li, Guangming Lu: U2-Former: A Nested U-shaped Transformer for Image Restoration. [paper]

  • [University of Macau] Zhendong Wang, Xiaodong Cun, Jianmin Bao, Wengang Zhou, Jianzhuang Liu, Houqiang Li: Uformer: A General U-Shaped Transformer for Image Restoration. [paper][code]

video

  • [ETH Zurich] Jiezhang Cao, Yawei Li, Kai Zhang, Luc Van Gool: Video Super-Resolution Transformer. [paper][code]

  • [Nanjing University] Ming Lu, Peiyao Guo, Huiqing Shi, Chuntong Cao and Zhan Ma: Transformer-based Image Compression. [paper]

2022

Image

  • [Tsinghua University] Yuanhao Cai, Jing Lin, Xiaowan Hu, Haoqian Wang,Xin Yuan, Yulun Zhang, Radu Timofte, and Luc Van Gool: Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstruction. CVPR 2022. [paper][code]

  • [ETH Zurich] Kai Zhang Yawei Li Jingyun Liang Jiezhang Cao Yulun Zhang Hao Tang Radu Timofte Luc Van Gool: Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis. [paper][code]

  • [Tsinghua University] Yuanhao Cai, Jing Lin, Zudi Lin, Haoqian Wang, Yulun Zhang, Hanspeter Pfister, Radu Timofte, Luc Van Gool: MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction. [paper][code]

  • [Nanjing University of Posts and Telecommunications] Guangwei Gao, Zixiang Xu, Juncheng Li, Jian Yang, Tieyong Zeng, and Guo-Jun Qi: CTCNet: A CNN-Transformer Cooperation Network for Face Image Super-Resolution. [paper]

  • [Tampere University] Wenzhu Xing, Karen Egiazarian: Residual Swin Transformer Channel Attention Network for Image Demosaicing. [paper]

  • [The Chinese University of Hong Kong] Qing Cai1, Yiming Qian, Jinxing Li, Jun Lv, Yee-Hong Yang, Feng Wu, and David Zhang: HIPA: Hierarchical Patch Transformer for Single Image Super Resolution. [paper]

  • [Johns Hopkins University] Wele Gedara Chaminda Bandara, Vishal M. Patel: HyperTransformer: A Textural and Spectral Feature Fusion Transformer for Pansharpening. [paper]

  • [Chengdu Institute of Computer Application Chinese Academy of Sciences] Lishun Wang, Zongliang Wu, Yong Zhong and Xin Yuan: Spectral Compressive Imaging Reconstruction Using Convolution and Spectral Contextual Transformer. [paper]

  • [National Chung Hsing University] Chi-Mao Fan and Tsung-Jung Liu, Kan-Hsien Liu: SUNet: Swin Transformer UNet for Image Denoising. [paper]

  • [Technical University of Munich] A. Burakhan Koyuncu, Han Gao, Eckehard Steinbach: contextformer: A Transformer with spatio-channel attention for context modeling in learned image compression. [paper]

  • [Alibaba Group] Yichen Qian, Ming Lin, Xiuyu Sun: EnTroformer: A Transformer-based Entropy Model for Learned Image Compression. [paper]

Video

  • [Xian Jiaotong University] Chengxu Liu, Huan Yang, Jianlong Fu, Xueming Qian: Learning Trajectory-Aware Transformer for Video Super-Resolution. [paper][code]

  • [ETH Zurich] Jingyun Liang Jiezhang Cao Yuchen Fan Kai Zhang Rakesh Ranjan Yawei Li Radu Timofte Luc Van Gool: VRT: A Video Restoration Transformer. [paper][code]

  • [Alibaba Group] Meisong Zheng, Qunliang Xing, Minglang Qiao, Mai Xu, Lai Jiang, Huaida Liu and Ying Chen: Progressive Training of A Two-Stage Framework for Video Restoration. [paper]

  • [Tsinghua University] Mingden Cao, Yanbo Fan, Yong Zhang, Jue Wang, Yujiu Yang: VDTR: Video Deblurring with Transformer. [paper][code]

  • [University of Hong Kong] Zhaoyang Huang1, Xiaoyu Shi1, Chao Zhang, Qiang Wang, Ka Chun Cheung, Hongwei Qin, Jifeng Dai, and Hongsheng Li: FlowFormer: A Transformer Architecture for Optical Flow. [paper]

  • [LITIV Laboratory] Xi Ye, Guillaume-Alexandre Bilodeau: VPTR: Efficient Transformers for Video Prediction. [paper][code]

  • [University of Texas] Zhicheng Geng Luming Liang Tianyu Ding Ilya Zharkov: RSTT: Real-time Spatial Temporal Transformer for Space-Time Video Super-Resolution. [paper][code]

  • [Tsinghua University] Hai Wang, Xiaoyu Xiang, Yapeng Tian, Wenming Yang, Qingmin Liao: STDAN: Deformable Attention Network for Space-Time Video Super-Resolution. [paper]

  • [Universitat de Barcelona] Javier Selva, Anders S. Johansen, Sergio Escalera1, Kamal Nasrollahi, Thomas B. Moeslund, Albert Clapes: Video Transformers: A Survey. [paper]