NTIRE 2024 Challenge on Blind Enhancement of Compressed Image: Methods and Results

Ren Yang,Radu Timofte,Bingchen Li,Xin Li,Mengxi Guo,Shijie Zhao,Li Zhang,Zhibo Chen,Dongyang Zhang, Yash Arora,Aditya Arora,Yuanbin Chen, Hui Tang,Tao Wang,Longxuan Zhao, Bin Chen, Tong, Qiao Mo, Jingwei Bao, Jinhua Hao,Yukang Ding, Hantang Li,Ming Sun, Chao Zhou,Shuyuan Zhu,Zhi Jin,Wei Wang,Dandan Zhan,Jiawei Wu, Jiahao Wu, Luwei Tu,Hongyu An,Xinfeng Zhang,Woon-Ha Yeo,Wang-Taek Oh,Young-Il Kim, Han-Cheol Ryu,Long Sun, Mingjun Zhen,Jinshan Pan,Jiangxin Dong,Jinhui Tang, Yapeng Du,Ao Li, Ziyang He,Lei Luo,Ce Zhu, Xin Yao,Sunder Ali Khowaja,Ikhyun Lee, Jaeho Lee, Seongwan Kim,Sharif S. M. A, Nodirkhuja Khujaev, Roman Tsoy

Computer Vision and Pattern Recognition（2024）

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关键词

Image Compression,Quality Factor,JPEG Images,Learning Rate,Local Information,Local Context,Batch Size,Adam Optimizer,Super-resolution,Global Context,Feed-forward Network,Local Details,Image Enhancement,Unified Representation,Channel Attention,L1 Loss,Discrete Cosine Transform,Attention Block,Vertical Flip,Random Horizontal Flipping,JPEG Compression,Compression Artifacts,AdamW Optimizer,Training Data,Progressive Learning,Test Phase,Patch Size,Input Image,Input Image Size

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