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Convolutional neural network has recently achieved great success for image restoration (IR) and also offered hierarchical features.
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2015
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S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in Proc. Int. Conf. Mach. Learn. , 2015
2015
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2015
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G. Chen, F. Zhu, and P. Ann Heng, “An efficient statistical method for image noise level estimation,” in Proc. IEEE Int. Conf. Comput. Vis. , 2015
2015
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J. Kim, J. Kwon Lee, and K. Mu Lee, “Accurate image super-resolution using very deep convolutional networks,” in Proc. IEEE Conf. Comput. Vis. Pattern Recog. , 2016
2016
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X. Mao, C. Shen, and Y.-B. Yang, “Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections,” in Proc. Adv. Neural Inf. Process. Syst. , 2016
2016
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J. Kim, J. Kwon Lee, and K. Mu Lee, “Deeply-recursive convolutional network for image super-resolution,” in Proc. IEEE Conf. Comput. Vis. Pattern Recog. , 2016
2016
Cited alongside, same era.
C. Dong, C. C. Loy, and X. Tang, “Accelerating the super-resolution convolutional neural network,” in Proc. Eur. Conf. Comput. Vis. , 2016
2016
Cited alongside, same era.
C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi, “Photo-realistic single image super-resolution using a generative adversarial network,” in Proc. IEEE Conf. Comput. Vis. Pattern Recog. , 2017
2017
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Y. Matsui, K. Ito, Y. Aramaki, A. Fujimoto, T. Ogawa, T. Yamasaki, and K. Aizawa, “Sketch-based manga retrieval using manga109 dataset,” Multimedia Tools and Applications , 2017
2017
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2017
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2017
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T. Karras, T. Aila, S. Laine, and J. Lehtinen, “Progressive growing of gans for improved quality, stability, and variation,” in Proc. International Conference on Learning Representations , 2018
2018
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K. Zhang, W. Zuo, and L. Zhang, “Learning a single convolutional super-resolution network for multiple degradations,” in Proc. IEEE Conf. Comput. Vis. Pattern Recog. , 2018
2018
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Y. Zhang, Y. Tian, Y. Kong, B. Zhong, and Y. Fu, “Residual dense network for image super-resolution,” in Proc. IEEE Conf. Comput. Vis. Pattern Recog. , 2018
2018
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H. Zhang and V. M. Patel, “Density-aware single image de-raining using a multi-stream dense network,” in Proc. IEEE Conf. Comput. Vis. Pattern Recog. , 2018
2018
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——, “Densely connected pyramid dehazing network,” in Proc. IEEE Conf. Comput. Vis. Pattern Recog. , 2018
2018
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2018
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M. Haris, G. Shakhnarovich, and N. Ukita, “Deep back-projection networks for super-resolution,” in Proc. IEEE Conf. Comput. Vis. Pattern Recog. , 2018
2018
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C. Ancuti, C. O. Ancuti, R. Timofte, L. Van Gool, L. Zhang, M.-H. Yang, V. M. Patel, H. Zhang, V. A. Sindagi, R. Zhao et al. , “Ntire 2018 challenge on image dehazing: Methods and results,” in Proc. IEEE Conf. Comput. Vis. Pattern Recog. Workshop , 2018
2018
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Y. Blau, R. Mechrez, R. Timofte, T. Michaeli, and L. Zelnik-Manor, “2018 pirm challenge on perceptual image super-resolution,” in Proc. Eur. Conf. Comput. Vis. Workshop , 2018
2018
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K. Yu, C. Dong, L. Lin, and C. C. Loy, “Crafting a toolchain for image restoration by deep reinforcement learning,” in Proc. IEEE Conf. Comput. Vis. Pattern Recog. , 2018, pp. 2443–2452
2018
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X. Wang, K. Yu, S. Wu, J. Gu, Y. Liu, C. Dong, C. C. Loy, Y. Qiao, and X. Tang, “Esrgan: Enhanced super-resolution generative adversarial networks,” in Proc. Eur. Conf. Comput. Vis. Workshop , 2018
2018
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Y. Zhang, K. Li, K. Li, L. Wang, B. Zhong, and Y. Fu, “Image super-resolution using very deep residual channel attention networks,” in Proc. Eur. Conf. Comput. Vis. , 2018
2018
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P. Liu, H. Zhang, K. Zhang, L. Lin, and W. Zuo, “Multi-level wavelet-cnn for image restoration,” in Proc. IEEE Conf. Comput. Vis. Pattern Recog. Workshop , 2018
2018
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T. Plötz and S. Roth, “Neural nearest neighbors networks,” in Proc. Adv. Neural Inf. Process. Syst. , 2018
2018
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D. Liu, B. Wen, Y. Fan, C. C. Loy, and T. S. Huang, “Non-local recurrent network for image restoration,” in Proc. Adv. Neural Inf. Process. Syst. , 2018
2018
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W.-S. Lai, J.-B. Huang, N. Ahuja, and M.-H. Yang, “Fast and accurate image super-resolution with deep laplacian pyramid networks,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. PP, no. 99, pp. 1–14, 2018
2018
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W. Dong, P. Wang, W. Yin, G. Shi, F. Wu, and X. Lu, “Denoising prior driven deep neural network for image restoration,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 41, no. 10, pp. 2305–2318, 2019
2019
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