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In recent years, Vision Transformer-based approaches for low-level vision tasks have achieved widespread success.
A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics
D. Martin, C. Fowlkes, D. Tal, and J. Malik · 2001
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Deng Jia et al · 2009
Earlier work this paper cites.
Low-complexity single-image super-resolution based on nonnegative neighbor embedding
Marco Bevilacqua, Aline Roumy, Christine Guillemot, and Marie Line Alberi-Morel · 2012
Earlier work this paper cites.
On single image scale-up using sparse-representations
Roman Zeyde, Michael Elad, and Matan Protter · 2012
Earlier work this paper cites.
Going deeper with convolutions, 2014
Christian Szegedy et al · 2014
Earlier work this paper cites.
Image super-resolution using deep convolutional networks, 2015
Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang · 2015
Earlier work this paper cites.
Single image super-resolution from transformed self-exemplars
Jia-Bin Huang, Abhishek Singh, and Narendra Ahuja · 2015
Earlier work this paper cites.
Deeply-Supervised Nets
Chen-Yu Lee, Saining Xie, Patrick Gallagher, Zhengyou Zhang, and Zhuowen Tu · 2015
Earlier work this paper cites.
Seven ways to improve example-based single image super resolution, 2015
Radu Timofte, Rasmus Rothe, and Luc Van Gool · 2015
Earlier work this paper cites.
Deep learning and the information bottleneck principle
Naftali Tishby and Noga Zaslavsky · 2015
Earlier work this paper cites.
Inception-v4, inception-resnet and the impact of residual connections on learning, 2016
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alex Alemi · 2016
Earlier work this paper cites.
Ntire 2017 challenge on single image super-resolution: Dataset and study
Eirikur Agustsson and Radu Timofte · 2017
Earlier work this paper cites.
Image super-resolution via deep recursive residual network
Tai Ying et al · 2017
Earlier work this paper cites.
Boundary-aware instance segmentation, 2017
Zeeshan Hayder, Xuming He, and Mathieu Salzmann · 2017
Earlier work this paper cites.
Enhanced deep residual networks for single image super-resolution
Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee · 2017
Earlier work this paper cites.
Memnet: A persistent memory network for image restoration
Ying Tai, Jian Yang, Xiaoming Liu, and Chunyan Xu · 2017
Earlier work this paper cites.
Ntire 2017 challenge on single image super-resolution: Methods and results
Radu Timofte, Eirikur Agustsson, Luc Van Gool, Ming-Hsuan Yang, Lei Zhang, Bee Lim, et al · 2017
Cited alongside, same era.
Image super-resolution using dense skip connections
Tong Tong, Gen Li, Xiejie Liu, and Qinquan Gao · 2017
Cited alongside, same era.
Densely connected convolutional networks, 2018
Gao Huang et al · 2018
Cited alongside, same era.
Esrgan: Enhanced super-resolution generative adversarial networks
Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Yu Qiao, and Chen Change Loy · 2018
Cited alongside, same era.
Residual dense network for image super-resolution
Yulun Zhang, Yapeng Tian, Yu Kong, Bineng Zhong, and Yun Fu · 2018
Cited alongside, same era.
Cspnet: A new backbone that can enhance learning capability of cnn, 2019
Chien-Yao Wang, Hong-Yuan Mark Liao, I-Hau Yeh, Yueh-Hua Wu, Ping-Yang Chen, and Jun-Wei Hsieh · 2019
A lightweight dense connected approach with attention on single image super-resolution
Lei Zha, Yu Yang, Zicheng Lai, Ziwei Zhang, and Juan Wen · 2021
Later among the works it cites.
Monodtr: Monocular 3d object detection with depth-aware transformer, 2022
Kuan-Chih Huang, Tsung-Han Wu, Hung-Ting Su, and Winston H. Hsu · 2022
Later among the works it cites.
Swinfusion: Cross-domain long-range learning for general image fusion via swin transformer
Jiayi Ma, Linfeng Tang, Fan Fan, Jun Huang, Xiaoguang Mei, and Yong Ma · 2022
Later among the works it cites.
Designing network design strategies through gradient path analysis
Chien-Yao Wang, Hong-Yuan Mark Liao, and I-Hau Yeh · 2022
Later among the works it cites.
Restormer: Efficient transformer for high-resolution image restoration
Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang · 2022
Later among the works it cites.
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Cross-scale internal graph neural network for image super-resolution
Shangchen Zhou et al · 2020
Cited alongside, same era.
Residual feature aggregation network for image super-resolution
Jie Liu, Wenjie Zhang, Yuting Tang, Jie Tang, and Gangshan Wu · 2020
Cited alongside, same era.
Single image super-resolution via a holistic attention network, 2020
Ben Niu, Weilei Wen, Wenqi Ren, Xiangde Zhang, Lianping Yang, Shuzhen Wang, Kaihao Zhang, Xiaochun Cao, and Haifeng Shen · 2020
Cited alongside, same era.
Eca-net: Efficient channel attention for deep convolutional neural networks, 2020
Qilong Wang, Banggu Wu, Pengfei Zhu, Peihua Li, Wangmeng Zuo, and Qinghua Hu · 2020
Cited alongside, same era.
Interpreting super-resolution networks with local attribution maps
Jinjin Gu and Chao Dong · 2021
Cited alongside, same era.
On efficient transformer and image pre-training for low-level vision
Wenbo Li, Xin Lu, Shengju Qian, Jiangbo Lu, Xiangyu Zhang, and Jiaya Jia · 2021
Cited alongside, same era.
Efficient long-range attention network for image super-resolution, 2022
Xindong Zhang, Hui Zeng, Shi Guo, and Lei Zhang · 2022
Later among the works it cites.
Understanding the robustness in vision transformers, 2022
Daquan Zhou, Zhiding Yu, Enze Xie, Chaowei Xiao, Anima Anandkumar, Jiashi Feng, and Jose M. Alvarez · 2022
Later among the works it cites.
Ntire 2023 challenge on image super-resolution (×4): Methods and results
Yulun Zhang et al · 2023
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Feature modulation transformer: Cross-refinement of global representation via high-frequency prior for image super-resolution
Ao Li, Le Zhang, Yun Liu, and Ce Zhu · 2023
Later among the works it cites.
Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
Chien-Yao Wang, Alexey Bochkovskiy, and Hong-Yuan Mark Liao · 2023
Later among the works it cites.
Swinfir: Revisiting the swinir with fast fourier convolution and improved training for image super-resolution, 2023
Dafeng Zhang, Feiyu Huang, Shizhuo Liu, Xiaobing Wang, and Zhezhu Jin · 2023
Later among the works it cites.
Srformer: Permuted self-attention for single image super-resolution
Yupeng Zhou, Zhen Li, Chun-Le Guo, Song Bai, Ming-Ming Cheng, and Qibin Hou · 2023
Later among the works it cites.
Attention retractable frequency fusion transformer for image super resolution
Qiang Zhu, Pengfei Li, and Qianhui Li · 2023
Later among the works it cites.
Ntire 2024 challenge on image super-resolution (x4): Methods and results
Zheng Chen, Zongwei Wu, Eduard-Sebastian Zamfir, Kai Zhang, Yulun Zhang, Radu Timofte, Xiaokang Yang, et al · 2024
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Real-time compressed sensing for joint hyperspectral image transmission and restoration for cubesat
Chih-Chung Hsu et al · 2024
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YOLOv9: Learning what you want to learn using programmable gradient information
Chien-Yao Wang and Hong-Yuan Mark Liao · 2024
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