Fetching the paper…
Reading the bibliography…
This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results.
Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. Journal of Machine Learning Research 9
2010
Earlier work this paper cites.
Efrat, N., Glasner, D., Apartsin, A., Nadler, B., Levin, A.: Accurate blur models vs. image priors in single image super-resolution. In: IEEE International Conference on Computer Vision. pp. 2832–2839 (2013)
2013
Earlier work this paper cites.
Dong, C., Loy, C.C., He, K., Tang, X.: Learning a deep convolutional network for image super-resolution. In: European Conference on Computer Vision. pp. 184–199. Springer (2014)
2014
Earlier work this paper cites.
Timofte, R., De Smet, V., Van Gool, L.: A+: Adjusted anchored neighborhood regression for fast super-resolution. In: Cremers, D., Reid, I., Saito, H., Yang, M.H. (eds.) 12th Asian Conference on Computer Vision (2014)
2014
Earlier work this paper cites.
2015
Earlier work this paper cites.
Kim, J., Kwon Lee, J., Mu Lee, K.: Accurate image super-resolution using very deep convolutional networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (June 2016)
2016
Earlier work this paper cites.
Liu, D., Wang, Z., Wen, B., Yang, J., Han, W., Huang, T.S.: Robust single image super-resolution via deep networks with sparse prior. IEEE Transactions on Image Processing 25
2016
Earlier work this paper cites.
Shi, W., Caballero, J., Huszár, F., Totz, J., Aitken, A.P., Bishop, R., Rueckert, D., Wang, Z.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: IEEE conference on computer vision and pattern recognition. pp. 1874–1883 (2016)
2016
Earlier work this paper cites.
Agustsson, E., Timofte, R.: NTIRE 2017 challenge on single image super-resolution: Dataset and study. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (July 2017)
2017
Earlier work this paper cites.
Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., Shi, W.: Photo-realistic single image super-resolution using a generative adversarial network. In: IEEE conference on computer vision and pattern recognition. pp. 4681–4690 (2017)
2017
Earlier work this paper cites.
Timofte, R., Agustsson, E., Van Gool, L., Yang, M.H., Zhang, L., et al.: Ntire 2017 challenge on single image super-resolution: Methods and results. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (July 2017)
2017
Earlier work this paper cites.
Yu, F., Koltun, V., Funkhouser, T.: Dilated residual networks. In: IEEE conference on computer vision and pattern recognition. pp. 472–480 (2017)
2017
Earlier work this paper cites.
Zhang, K., Zuo, W., Gu, S., Zhang, L.: Learning deep cnn denoiser prior for image restoration. In: IEEE conference on Computer Vision and Pattern Recognition. pp. 3929–3938 (2017)
2017
Earlier work this paper cites.
2018
Earlier work this paper cites.
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: IEEE conference on computer vision and pattern recognition. pp. 7132–7141 (2018)
2018
Earlier work this paper cites.
Lefkimmiatis, S.: Universal denoising networks: a novel cnn architecture for image denoising. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 3204–3213 (2018)
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
Liu, Z., Wu, B., Luo, W., Yang, X., Liu, W., Cheng, K.T.: Bi-real net: Enhancing the performance of 1-bit cnns with improved representational capability and advanced training algorithm. In: European conference on computer vision. pp. 722–737 (2018)
2018
Earlier work this paper cites.
Roy, A.G., Navab, N., Wachinger, C.: Concurrent spatial and channel ‘squeeze & excitation’in fully convolutional networks. In: International conference on medical image computing and computer-assisted intervention. pp. 421–429. Springer (2018)
2018
Earlier work this paper cites.
Sreter, H., Giryes, R.: Learned convolutional sparse coding. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). pp. 2191–2195 (2018)
2018
Earlier work this paper cites.
Wang, X., Yu, K., Dong, C., Change Loy, C.: Recovering realistic texture in image super-resolution by deep spatial feature transform. In: IEEE conference on computer vision and pattern recognition. pp. 606–615 (2018)
2018
Earlier work this paper cites.
Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., Dong, C., Qiao, Y., Loy, C.C.: Esrgan: Enhanced super-resolution generative adversarial networks. In: The European Conference on Computer Vision Workshops (ECCVW) (September 2018)
2018
Earlier work this paper cites.
Zhang, K., Zuo, W., Zhang, L.: Learning a single convolutional super-resolution network for multiple degradations. In: IEEE Conference on Computer Vision and Pattern Recognition. pp. 3262–3271 (2018)
2018
Cited alongside, same era.
Zhang, Y., Tian, Y., Kong, Y., Zhong, B., Fu, Y.: Residual dense network for image super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2472–2481 (2018)
2018
Cited alongside, same era.
Bell-Kligler, S., Shocher, A., Irani, M.: Blind super-resolution kernel estimation using an internal-gan. In: Advances in Neural Information Processing Systems. pp. 284–293 (2019)
2019
Cited alongside, same era.
2019
Cited alongside, same era.
Ignatov, A., Timofte, R., et al.: AIM 2020 challenge on rendering realistic bokeh. In: European Conference on Computer Vision Workshops (2020)
2020
Closest in time.
Jeon, G.W., Choi, J.H., Kim, J.H., Lee, J.S.: Larvanet: Hierarchical super-resolution via multi-exit architecture. In: European Conference on Computer Vision Workshops (2020)
2020
Closest in time.
Li, Y., Gu, S., Mayer, C., Gool, L.V., Timofte, R.: Group sparsity: The hinge between filter pruning and decomposition for network compression. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 8018–8027 (2020)
2020
Closest in time.
2020
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Ding, X., Ding, G., Guo, Y., Han, J.: Centripetal sgd for pruning very deep convolutional networks with complicated structure. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4943–4953 (2019)
2019
Cited alongside, same era.
Ding, X., Guo, Y., Ding, G., Han, J.: Acnet: Strengthening the kernel skeletons for powerful cnn via asymmetric convolution blocks. In: The IEEE International Conference on Computer Vision (ICCV) (October 2019)
2019
Cited alongside, same era.
2019
Cited alongside, same era.
Hui, Z., Gao, X., Yang, Y., Wang, X.: Lightweight image super-resolution with information multi-distillation network. In: ACM Multimedia (ACM MM) (2019)
2019
Cited alongside, same era.
Li, Y., Gu, S., Gool, L.V., Timofte, R.: Learning filter basis for convolutional neural network compression. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 5623–5632 (2019)
2019
Cited alongside, same era.
Li, Y., Dong, X., Wang, W.: Additive powers-of-two quantization: An efficient non-uniform discretization for neural networks. In: International Conference on Learning Representations (2019)
2019
Cited alongside, same era.
Lugmayr, A., Danelljan, M., Timofte, R.: Unsupervised learning for real-world super-resolution. In: IEEE International Conference on Computer Vision Workshop. pp. 3408–3416 (2019)
2019
Cited alongside, same era.
2019
Cited alongside, same era.
Liu, J., Tang, J., Wu, G.: Residual feature distillation network for lightweight image super-resolution. In: European Conference on Computer Vision Workshops (2020)
2020
Closest in time.
Liu, J., Zhang, W., Tang, Y., Tang, J., Wu, G.: Residual feature aggregation network for image super-resolution. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (June 2020)
2020
Closest in time.
Lugmayr, A., Danelljan, M., Gool, L.V., Timofte, R.: SRFlow: Learning the super-resolution space with normalizing flow. In: European Conference on Computer Vision (2020)
2020
Closest in time.
Lugmayr, A., Danelljan, M., Timofte, R.: Ntire 2020 challenge on real-world image super-resolution: Methods and results. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops. pp. 494–495 (2020)
2020
Closest in time.
Menon, S., Damian, A., Hu, S., Ravi, N., Rudin, C.: Pulse: Self-supervised photo upsampling via latent space exploration of generative models. In: CVPR (2020)
2020
Closest in time.
Muqeet, A., Hwang, J., Yang, S., Kang, J.H., Kim, Y., Bae, S.H.: Ultra lightweight image super-resolution with multi-attention. In: European Conference on Computer Vision Workshops (2020)
2020
Closest in time.
Ntavelis, E., Romero, A., Bigdeli, S.A., Timofte, R., et al.: AIM 2020 challenge on image extreme inpainting. In: European Conference on Computer Vision Workshops (2020)
2020
Closest in time.
Radosavovic, I., Kosaraju, R.P., Girshick, R., He, K., Dollár, P.: Designing network design spaces. In: IEEE Conference on Computer Vision and Pattern Recognition. pp. 10428–10436 (2020)
2020
Closest in time.
Son, S., Lee, J., Nah, S., Timofte, R., Lee, K.M., et al.: AIM 2020 challenge on video temporal super-resolution. In: European Conference on Computer Vision Workshops (2020)
2020
Closest in time.
Wang, H., Bhaskara, V., Levinshtein, A., Tsogkas, S., Jepson, A.: Efficient super-resolution using mobilenetv3. In: European Conference on Computer Vision Workshops (2020)
2020
Closest in time.
Wei, P., Lu, H., Timofte, R., Lin, L., Zuo, W., et al.: AIM 2020 challenge on real image super-resolution. In: European Conference on Computer Vision Workshops (2020)
2020
Closest in time.
Xiong, D., Huang, K., Jiang, H., Li, B., Chen, S., Jiang, X.: Idlesr: Efficient super-resolution network with multi-scale idleblocks. In: European Conference on Computer Vision Workshops (2020)
2020
Closest in time.
Yin, H., Molchanov, P., Alvarez, J.M., Li, Z., Mallya, A., Hoiem, D., Jha, N.K., Kautz, J.: Dreaming to distill: Data-free knowledge transfer via deepinversion. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 8715–8724 (2020)
2020
Closest in time.
Zhang, K., Danelljan, M., Li, Y., Timofte, R., et al.: AIM 2020 challenge on efficient super-resolution: Methods and results. In: European Conference on Computer Vision Workshops (2020)
2020
Closest in time.
Zhang, K., Gu, S., Timofte, R.: Ntire 2020 challenge on perceptual extreme super-resolution: Methods and results. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops. pp. 492–493 (2020)
2020
Closest in time.
Zhang, K., Li, Y., Zuo, W., Zhang, L., Van Gool, L., Timofte, R.: Plug-and-play image restoration with deep denoiser prior. arXiv preprint (2020)
2020
Closest in time.
Zhang, K., Van Gool, L., Timofte, R.: Deep unfolding network for image super-resolution. In: IEEE Conference on Computer Vision and Pattern Recognition. pp. 3217–3226 (2020)
2020
Closest in time.