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Image super-resolution (SR) with generative adversarial networks (GAN) has achieved great success in restoring realistic details.
Real-esrgan: Training real-world blind super-resolution with pure synthetic data
Wang, X., Xie, L., Dong, C., and Shan, Y · 1914
Earlier work this paper cites.
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., and Simoncelli, E. P · 2004
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
Earlier work this paper cites.
Dual-camera super-resolution with aligned attention modules
Wang, T., Xie, J., Sun, W., Yan, Q., and Chen, Q · 2010
Earlier work this paper cites.
Making a “completely blind” image quality analyzer
Mittal, A., Soundararajan, R., and Bovik, A. C · 2012
Earlier work this paper cites.
Learning a deep convolutional network for image super-resolution
Dong, C., Loy, C. C., He, K., and Tang, X · 2014
Earlier work this paper cites.
Image super-resolution using deep convolutional networks
Dong, C., Loy, C. C., He, K., and Tang, X · 2015
Earlier work this paper cites.
Accelerating the super-resolution convolutional neural network
Dong, C., Loy, C. C., and Tang, X · 2016
Earlier work this paper cites.
Perceptual losses for real-time style transfer and super-resolution
Johnson, J., Alahi, A., and Fei-Fei, L · 2016
Earlier work this paper cites.
Video super-resolution via bidirectional recurrent convolutional networks
Huang, Y., Wang, W., and Wang, L · 2017
Earlier work this paper cites.
Photo-realistic single image super-resolution using a generative adversarial network
Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al · 2017
Earlier work this paper cites.
Enhanced deep residual networks for single image super-resolution
Lim, B., Son, S., Kim, H., Nah, S., and Mu Lee, K · 2017
Earlier work this paper cites.
Toward real-world single image super-resolution: A new benchmark and a new model
Cai, J., Zeng, H., Yong, H., Cao, Z., and Zhang, L · 2019
Earlier work this paper cites.
Second-order attention network for single image super-resolution
Dai, T., Cai, J., Zhang, Y., Xia, S.-T., and Zhang, L · 2019
Cited alongside, same era.
Blind super-resolution with iterative kernel correction
Gu, J., Lu, H., Zuo, W., and Dong, C · 2019
Cited alongside, same era.
Srobb: Targeted perceptual loss for single image super-resolution
Rad, M. S., Bozorgtabar, B., Marti, U.-V., Basler, M., Ekenel, H. K., and Thiran, J.-P · 2019
Cited alongside, same era.
Ranksrgan: Generative adversarial networks with ranker for image super-resolution
Zhang, W., Liu, Y., Dong, C., and Qiao, Y · 2019
Cited alongside, same era.
Unfolding the alternating optimization for blind super resolution
Huang, Y., Li, S., Wang, L., Tan, T., et al · 2020
Cited alongside, same era.
Ntire 2020 challenge on real-world image super-resolution: Methods and results
Lugmayr, A., Danelljan, M., and Timofte, R · 2020
Fourier space losses for efficient perceptual image super-resolution
Fuoli, D., Van Gool, L., and Timofte, R · 2021
Later among the works it cites.
On efficient transformer and image pre-training for low-level vision, 2021
Li, W., Lu, X., Lu, J., Zhang, X., and Jia, J · 2021
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Swinir: Image restoration using swin transformer
Liang, J., Cao, J., Sun, G., Zhang, K., Van Gool, L., and Timofte, R · 2021
Later among the works it cites.
Designing a practical degradation model for deep blind image super-resolution
Zhang, K., Liang, J., Van Gool, L., and Timofte, R · 2021
Later among the works it cites.
Accelerating the training of video super-resolution
Lin, L., Wang, X., Qi, Z., and Shan, Y · 2022
Later among the works it cites.
Metric learning based interactive modulation for real-world super-resolution
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Cited alongside, same era.
Structure-preserving super resolution with gradient guidance
Ma, C., Rao, Y., Cheng, Y., Chen, C., Lu, J., and Zhou, J · 2020
Cited alongside, same era.
Single image super-resolution via a holistic attention network
Niu, B., Wen, W., Ren, W., Zhang, X., Yang, L., Wang, S., Zhang, K., Cao, X., and Shen, H · 2020
Cited alongside, same era.
Deep unfolding network for image super-resolution
Zhang, K., Gool, L. V., and Timofte, R · 2020
Cited alongside, same era.
Basicvsr: The search for essential components in video super-resolution and beyond
Chan, K. C., Wang, X., Yu, K., Dong, C., and Loy, C. C · 2021
Cited alongside, same era.
Pre-trained image processing transformer
Chen, H., Wang, Y., Guo, T., Xu, C., Deng, Y., Liu, Z., Ma, S., Xu, C., Xu, C., and Gao, W · 2021
Cited alongside, same era.
A continual learning survey: Defying forgetting in classification tasks
De Lange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., and Tuytelaars, T · 2021
Cited alongside, same era.
Mou, C., Wu, Y., Wang, X., Dong, C., Zhang, J., and Shan, Y · 2022
Later among the works it cites.
Rethinking alignment in video super-resolution transformers
Shi, S., Gu, J., Xie, L., Wang, X., Yang, Y., and Dong, C · 2022
Later among the works it cites.
Repsr: Training efficient vgg-style super-resolution networks with structural re-parameterization and batch normalization
Wang, X., Dong, C., and Shan, Y · 2022
Later among the works it cites.
Mitigating artifacts in real-world video super-resolution models
Xie, L., Wang, X., Shi, S., Gu, J., Dong, C., and Shan, Y · 2022
Later among the works it cites.
Maniqa: Multi-dimension attention network for no-reference image quality assessment
Yang, S., Wu, T., Shi, S., Lao, S., Gong, Y., Cao, M., Wang, J., and Yang, Y · 2022
Later among the works it cites.
Perceptual artifacts localization for inpainting
Zhang, L., Zhou, Y., Barnes, C., Amirghodsi, S., Lin, Z., Shechtman, E., and Shi, J · 2022
Later among the works it cites.
Activating more pixels in image super-resolution transformer
Chen, X., Wang, X., Zhou, J., Qiao, Y., and Dong, C · 2023
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