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Deep Convolutional Neural Networks (CNNs) have achieved remarkable results on Single Image Super-Resolution (SISR).
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2016
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Y. S. Tai, J. X. Yang, and X. Liu, “Image super-resolution via deep recursive residual network,” in
2017
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Y. S. Tai, J. X. Yang, X. Liu, and C. Xu, “Memnet: A persistent memory network for image restoration,” in
2017
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B. Lim, S. Son, H. Kim, S. Nah, and K. M. Lee, “Enhanced deep residual networks for single image super-resolution,” in
2017
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C. Ledig, L. Theis, F. Huszar, 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
2017
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M. Haris, G. Shakhnarovich, and N. Ukita, “Deep back-projection networks for super-resolution,” in
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2018
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2018
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2018
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2018
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K. Zhang, W. Zuo, S. Gu, and L. Zhang, “Learning deep cnn denoiser prior for image restoration,” in
2017
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K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, “Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising,”
2017
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S. Bako, T. Vogels, B. McWilliams, M. Meyer, J. Novák, A. Harvill, P. Sen, T. DeRose, and F. Rousselle, “Kernel-predicting convolutional networks for denoising monte carlo renderings,”
2017
Cited alongside, same era.
B. Mildenhall, J. T. Barron, J. Chen, D. Sharlet, R. Ng, and R. Carroll, “Burst denoising with kernel prediction networks,” in
2017
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S. Niklaus, L. Mai, and F. Liu, “Video frame interpolation via adaptive convolution,” in
2017
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S. Niklaus, L. Mai, and F. Liu, “Video frame interpolation via adaptive separable convolution,” in
2017
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E. Agustsson and R. Timofte, “Ntire 2017 challenge on single image super-resolution: Dataset and study,” in
2017
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M. I. Assaf Shocher, Nadav Cohen, “”zero-shot” super-resolution using deep internal learning,” in
2018
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Y. Yuan, S. Liu, J. Zhang, Y. Zhang, C. Dong, and L. Lin, “Unsupervised image super-resolution using cycle-in-cycle generative adversarial networks,” in
2018
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T. Vogels, F. Rousselle, B. McWilliams, G. Röthlin, A. Harvill, D. Adler, M. Meyer, and J. Novák, “Denoising with kernel prediction and asymmetric loss functions,”
2018
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Y. Jo, S. W. Oh, J. Kang, and S. J. Kim, “Deep video super-resolution network using dynamic upsampling filters without explicit motion compensation,” in
2018
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F. Zhao, J. Zhao, S. Yan, and J. Feng, “Dynamic conditional networks for few-shot learning,” in
2018
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2018
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S. Bell-Kligler, A. Shocher, and M. Irani, “Blind super-resolution kernel estimation using an internal-gan,” in
2019
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J. Gu, H. Lu, W. Zuo, and C. Dong, “Blind super-resolution with iterative kernel correction,” in
2019
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X. Xu, M. Li, and W. Sun, “Learning deformable kernels for image and video denoising,”
2019
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