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Super-resolution (SR) is by definition ill-posed.
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: · 2004
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Image up-sampling using total-variation regularization with a new observation model
Aly, H.A., Dubois, E.: · 2005
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Patch based blind image super resolution
Wang, Q., Tang, X., Shum, H.: · 2005
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Image upsampling via imposed edge statistics
Fattal, R.: · 2007
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Image superresolution using support vector regression
Ni, K.S., Nguyen, T.Q.: · 2007
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Image super-resolution using gradient profile prior
Sun, J., Xu, Z., Shum, H.Y.: · 2008
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Non-local kernel regression for image and video restoration
Zhang, H., Yang, J., Zhang, Y., Huang, T.S.: · 2010
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Image super-resolution via sparse representation
Yang, J., Wright, J., Huang, T.S., Ma, Y.: · 2010
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Single image super-resolution using gaussian process regression
He, H., Siu, W.C.: · 2011
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Image super resolution using sparse image and singular values as priors
Ravishankar, S., Reddy, C.N., Tripathi, S., Murthy, K.: · 2011
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Fractional order total variation regularization for image super-resolution
Ren, Z., He, C., Zhang, Q.: · 2013
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A+: Adjusted anchored neighborhood regression for fast super-resolution
Timofte, R., De Smet, V., Van Gool, L.: · 2014
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: · 2014
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Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., Tang, X.: · 2015
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Image super-resolution using deep convolutional networks
Dong, C., Loy, C.C., He, K., Tang, X.: · 2015
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Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A.: · 2015
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Deeply-recursive convolutional network for image super-resolution
Kim, J., Kwon Lee, J., Mu Lee, K.: · 2016
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Perceptual losses for real-time style transfer and super-resolution
Johnson, J., Alahi, A., Fei-Fei, L.: · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2016
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Atgv-net: Accurate depth super-resolution
Riegler, G., Rüther, M., Bischof, H.: · 2016
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Depth map super-resolution by deep multi-scale guidance
Hui, T.W., Loy, C.C., Tang, X.: · 2016
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Deep depth super-resolution: Learning depth super-resolution using deep convolutional neural network
Song, X., Dai, Y., Qin, X.: · 2016
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Semantic super-resolution: When and where is it useful?
Timofte, R., De Smet, V., Van Gool, L.: · 2016
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Multi-scale context aggregation by dilated convolutions
Yu, F., Koltun, V.: · 2016
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Ntire 2017 challenge on single image super-resolution: Methods and results
Timofte, R., Agustsson, E., Van Gool, L., Yang, M.H., Zhang, L.: · 2017
Cited alongside, same era.
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
Cited alongside, same era.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: · 2017
Cited alongside, same era.
Enhancenet: Single image super-resolution through automated texture synthesis
Sajjadi, M.S., Scholkopf, B., Hirsch, M.: · 2017
Cited alongside, same era.
Density estimation using real nvp
Dinh, L., Sohl-Dickstein, J., Bengio, S.: · 2017
Cited alongside, same era.
Scene parsing through ade20k dataset
High-resolution image synthesis and semantic manipulation with conditional gans
Wang, T.C., Liu, M.Y., Zhu, J.Y., Tao, A., Kautz, J., Catanzaro, B.: · 2018
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Progressive growing of gans for improved quality, stability, and variation
Karras, T., Aila, T., Laine, S., Lehtinen, J.: · 2018
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Rethinking atrous convolution for semantic image segmentation liang-chieh
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: · 2018
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Encoder-decoder with atrous separable convolution for semantic image segmentation
Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: · 2018
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Ntire 2019 challenge on real image super-resolution: Methods and results
Cai, J., Gu, S., Timofte, R., Zhang, L.: · 2019
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Deep learning face hallucination via attributes transfer and enhancement
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Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., Torralba, A.: · 2017
Cited alongside, same era.
Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: · 2017
Cited alongside, same era.
Dilated residual networks
Yu, F., Koltun, V., Funkhouser, T.: · 2017
Cited alongside, same era.
The 2018 pirm challenge on perceptual image super-resolution
Blau, Y., Mechrez, R., Timofte, R., Michaeli, T., Zelnik-Manor, L.: · 2018
Cited alongside, same era.
Recovering realistic texture in image super-resolution by deep spatial feature transform
Wang, X., Yu, K., Dong, C., Change Loy, C.: · 2018
Cited alongside, same era.
Super-resolving very low-resolution face images with supplementary attributes
Yu, X., Fernando, B., Hartley, R., Porikli, F.: · 2018
Cited alongside, same era.
Attribute augmented convolutional neural network for face hallucination
Lee, C.H., Zhang, K., Lee, H.C., Cheng, C.W., Hsu, W.: · 2018
Cited alongside, same era.
Li, M., Sun, Y., Zhang, Z., Xie, H., Yu, J.: · 2019
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Progressive face super-resolution via attention to facial landmark
Kim, D., Kim, M., Kwon, G., Kim, D.S.: · 2019
Later among the works it cites.
Aim 2019 challenge on image extreme super-resolution: Methods and results
Gu, S., Danelljan, M., Timofte, R., Haris, M., Akita, K., Shakhnarovic, G., Ukita, N., Michelini, P.N., Chen, W., Liu, H., et al.: · 2019
Later among the works it cites.
Exemplar guided face image super-resolution without facial landmarks
Dogan, B., Gu, S., Timofte, R.: · 2019
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A style-based generator architecture for generative adversarial networks
Karras, T., Laine, S., Aila, T.: · 2019
Later among the works it cites.
Semantic image synthesis with spatially-adaptive normalization
Park, T., Liu, M.Y., Wang, T.C., Zhu, J.Y.: · 2019
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Smit: Stochastic multi-label image-to-image translation
Romero, A., Arbeláez, P., Van Gool, L., Timofte, R.: · 2019
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Div8k: Diverse 8k resolution image dataset
Gu, S., Lugmayr, A., Danelljan, M., Fritsche, M., Lamour, J., Timofte, R.: · 2019
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Perceptual extreme super-resolution network with receptive field block
Shang, T., Dai, Q., Zhu, S., Yang, T., Guo, Y.: · 2020
Closest in time.
Ntire 2020 challenge on perceptual extreme super-resolution: Methods and results
Zhang, K., Gu, S., Timofte, R.: · 2020
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Pulse: Self-supervised photo upsampling via latent space exploration of generative models
Menon, S., Damian, A., Hu, M., Ravi, N., Rudin, C.: · 2020
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Explorable super resolution
Bahat, Y., Michaeli, T.: · 2020
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Analyzing and improving the image quality of stylegan
Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., Aila, T.: · 2020
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Srflow: Learning the super-resolution space with normalizing flow
Lugmayr, A., Danelljan, M., Van Gool, L., Timofte, R.: · 2020
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Invertible image rescaling
Xiao, M., Zheng, S., Liu, C., Wang, Y., He, D., Ke, G., Bian, J., Lin, Z., Liu, T.Y.: · 2020
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Deep learning for image super-resolution: A survey
Wang, Z., Chen, J., Hoi, S.C.: · 2020
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Sean: Image synthesis with semantic region-adaptive normalization
Zhu, P., Abdal, R., Qin, Y., Wonka, P.: · 2020
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Maskgan: Towards diverse and interactive facial image manipulation
Lee, C.H., Liu, Z., Wu, L., Luo, P.: · 2020
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