Fetching the paper…
Reading the bibliography…
Single Image Super-Resolution (SISR) tasks have achieved significant performance with deep neural networks.
D. Martin, C. Fowlkes, D. Tal, and J. Malik, “A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics,” in Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 , vol. 2. IEEE, 2001, pp. 416–423
2001
Earlier work this paper cites.
Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE transactions on image processing , vol. 13, no. 4, pp. 600–612, 2004
2004
Earlier work this paper cites.
S. Ravishankar and Y. Bresler, “Mr image reconstruction from highly undersampled k-space data by dictionary learning,” IEEE transactions on medical imaging , vol. 30, no. 5, pp. 1028–1041, 2010
2010
Earlier work this paper cites.
J. Yang, J. Wright, T. S. Huang, and Y. Ma, “Image super-resolution via sparse representation,” IEEE transactions on image processing , vol. 19, pp. 2861–2873, 2010
2010
Earlier work this paper cites.
M. Bevilacqua, A. Roumy, C. Guillemot, and M. L. Alberi-Morel, “Low-complexity single-image super-resolution based on nonnegative neighbor embedding,” BMVA press , 2012
2012
Earlier work this paper cites.
2014
Earlier work this paper cites.
C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE transactions on pattern analysis and machine intelligence , vol. 38, no. 2, pp. 295–307, 2015
2015
Earlier work this paper cites.
J.-B. Huang, A. Singh, and N. Ahuja, “Single image super-resolution from transformed self-exemplars,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 5197–5206
2015
Earlier work this paper cites.
J. Kim, J. Kwon Lee, and K. Mu Lee, “Accurate image super-resolution using very deep convolutional networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2016
2016
Earlier work this paper cites.
J. Kim, J. K. Lee, and K. M. Lee, “Deeply-recursive convolutional network for image super-resolution,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 1637–1645
2016
Earlier work this paper cites.
C. Dong, C. C. Loy, and X. Tang, “Accelerating the super-resolution convolutional neural network,” in European conference on computer vision . Springer, 2016, pp. 391–407
2016
Earlier work this paper cites.
W. Shi, J. Caballero, F. Huszár, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert, and Z. Wang, “Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 1874–1883
2016
Earlier work this paper cites.
B. Lim, S. Son, H. Kim, S. Nah, and K. M. Lee, “Enhanced deep residual networks for single image super-resolution,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops , 2017, pp. 136–144
2017
Earlier work this paper cites.
C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang et al. , “Photo-realistic single image super-resolution using a generative adversarial network,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 4681–4690
2017
Earlier work this paper cites.
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 4700–4708
2017
Earlier work this paper cites.
T. Tong, G. Li, X. Liu, and Q. Gao, “Image super-resolution using dense skip connections,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 4799–4807
2017
Earlier work this paper cites.
Y. Tai, J. Yang, X. Liu, and C. Xu, “Memnet: A persistent memory network for image restoration,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 4539–4547
2017
Earlier work this paper cites.
Y. Tai, J. Yang, and X. Liu, “Image super-resolution via deep recursive residual network,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 3147–3155
2017
Earlier work this paper cites.
F. Yu, V. Koltun, and T. Funkhouser, “Dilated residual networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 472–480
2017
Cited alongside, same era.
C. R. A. Chaitanya, A. S. Kaplanyan, C. Schied, M. Salvi, A. Lefohn, D. Nowrouzezahrai, and T. Aila, “Interactive reconstruction of monte carlo image sequences using a recurrent denoising autoencoder,” ACM Transactions on Graphics (TOG) , vol. 36, no. 4, pp. 1–12, 2017
2017
Cited alongside, same era.
E. Agustsson and R. Timofte, “Ntire 2017 challenge on single image super-resolution: Dataset and study,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops , 2017, pp. 126–135
2017
Cited alongside, same era.
R. Timofte, E. Agustsson, L. Van Gool, M.-H. Yang, and L. Zhang, “Ntire 2017 challenge on single image super-resolution: Methods and results,” in Proceedings of the IEEE conference on computer vision and pattern recognition workshops , 2017, pp. 114–125
2017
D. Song, C. Xu, X. Jia, Y. Chen, C. Xu, and Y. Wang, “Efficient residual dense block search for image super-resolution,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, 2020, pp. 12 007–12 014
2020
Later among the works it cites.
R. Lee, Ł. Dudziak, M. Abdelfattah, S. I. Venieris, H. Kim, H. Wen, and N. D. Lane, “Journey towards tiny perceptual super-resolution,” in European Conference on Computer Vision . Springer, 2020, pp. 85–102
2020
Later among the works it cites.
Z. Pan, B. Li, T. Xi, Y. Fan, G. Zhang, J. Liu, J. Han, and E. Ding, “Real image super resolution via heterogeneous model ensemble using gp-nas,” in European Conference on Computer Vision . Springer, 2020, pp. 423–436
2020
Later among the works it cites.
J. Liu, J. Tang, and G. Wu, “Residual feature distillation network for lightweight image super-resolution,” in Computer Vision – ECCV 2020 Workshops , A. Bartoli and A. Fusiello, Eds. Cham: Springer International Publishing, 2020, pp. 41–55
2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Y. Zhang, K. Li, K. Li, L. Wang, B. Zhong, and Y. Fu, “Image super-resolution using very deep residual channel attention networks,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 286–301
2018
Cited alongside, same era.
Y. Zhang, Y. Tian, Y. Kong, B. Zhong, and Y. Fu, “Residual dense network for image super-resolution,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 2472–2481
2018
Cited alongside, same era.
N. Ahn, B. Kang, and K.-A. Sohn, “Fast, accurate, and lightweight super-resolution with cascading residual network,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 252–268
2018
Cited alongside, same era.
G. Seif and D. Androutsos, “Large receptive field networks for high-scale image super-resolution,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops , 2018, pp. 763–772
2018
Cited alongside, same era.
2018
Cited alongside, same era.
X. Wang, K. Yu, S. Wu, J. Gu, Y. Liu, C. Dong, Y. Qiao, and C. Change Loy, “Esrgan: Enhanced super-resolution generative adversarial networks,” in Proceedings of the European Conference on Computer Vision (ECCV) Workshops , 2018, pp. 0–0
2018
Cited alongside, same era.
K. Zhang, W. Zuo, and L. Zhang, “Learning a single convolutional super-resolution network for multiple degradations,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 3262–3271
2018
Cited alongside, same era.
Z. Hui, X. Wang, and X. Gao, “Fast and accurate single image super-resolution via information distillation network,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 723–731
2018
Cited alongside, same era.
Later among the works it cites.
T. Shang, Q. Dai, S. Zhu, T. Yang, and Y. Guo, “Perceptual extreme super-resolution network with receptive field block,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops , 2020, pp. 440–441
2020
Later among the works it cites.
K. Zhang, L. V. Gool, and R. Timofte, “Deep unfolding network for image super-resolution,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 3217–3226
2020
Later among the works it cites.
X. Luo, Y. Xie, Y. Zhang, Y. Qu, C. Li, and Y. Fu, “Latticenet: Towards lightweight image super-resolution with lattice block,” in Computer Vision – ECCV 2020 , A. Vedaldi, H. Bischof, T. Brox, and J.-M. Frahm, Eds. Cham: Springer International Publishing, 2020, pp. 272–289
2020
Later among the works it cites.
K. Zhang, M. Danelljan, Y. Li, R. Timofte, J. Liu, J. Tang, G. Wu, Y. Zhu, X. He, W. Xu et al. , “Aim 2020 challenge on efficient super-resolution: Methods and results,” in European Conference on Computer Vision . Springer, 2020, pp. 5–40
2020
Later among the works it cites.
C. He, H. Ye, L. Shen, and T. Zhang, “Milenas: Efficient neural architecture search via mixed-level reformulation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 11 993–12 002
2020
Later among the works it cites.
Y. Guo, Y. Luo, Z. He, J. Huang, and J. Chen, “Hierarchical neural architecture search for single image super-resolution,” IEEE Signal Processing Letters , vol. 27, pp. 1255–1259, 2020
2020
Later among the works it cites.
H. Zhang, Y. Li, H. Chen, and C. Shen, “Memory-efficient hierarchical neural architecture search for image denoising,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 3657–3666
2020
Later among the works it cites.
H. Zhao, X. Kong, J. He, Y. Qiao, and C. Dong, “Efficient image super-resolution using pixel attention,” in Computer Vision – ECCV 2020 Workshops , A. Bartoli and A. Fusiello, Eds. Cham: Springer International Publishing, 2020, pp. 56–72
2020
Later among the works it cites.
X. Chu, B. Zhang, H. Ma, R. Xu, and Q. Li, “Fast, accurate and lightweight super-resolution with neural architecture search,” in 2020 25th International Conference on Pattern Recognition (ICPR) , 2021, pp. 59–64
2021
Closest in time.
X. Zhu, K. Guo, S. Ren, B. Hu, M. Hu, and H. Fang, “Lightweight image super-resolution with expectation-maximization attention mechanism,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 32, no. 3, pp. 1273–1284, 2022
2022
Closest in time.
J. Pan, D. Sun, J. Zhang, J. Tang, J. Yang, Y.-W. Tai, and M.-H. Yang, “Dual convolutional neural networks for low-level vision,” International Journal of Computer Vision , vol. 130, no. 6, pp. 1440–1458, 2022
2022
Closest in time.
Z. Du, D. Liu, J. Liu, J. Tang, G. Wu, and L. Fu, “Fast and memory-efficient network towards efficient image super-resolution,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 853–862
2022
Closest in time.
Z. Li, Y. Liu, X. Chen, H. Cai, J. Gu, Y. Qiao, and C. Dong, “Blueprint separable residual network for efficient image super-resolution,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 833–843
2022
Closest in time.
Z. Hui, X. Gao, Y. Yang, and X. Wang, “Lightweight image super-resolution with information multi-distillation network,” in Proceedings of the 27th ACM International Conference on Multimedia (ACM MM) , 2019, pp. 2024–2032
2032
Closest in time.