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Maintaining natural image statistics is a crucial factor in restoration and generation of realistic looking images.
Parametric correspondence and chamfer matching: Two new techniques for image matching
Barrow, H.G., Tenenbaum, J.M., Bolles, R.C., Wolf, H.C.: · 1977
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The statistics of natural images
Ruderman, D.L.: · 1994
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Statistics of range images
Huang, J., Lee, A.B., Mumford, D.: · 2000
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A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics
Martin, D., Fowlkes, C., Tal, D., Malik, J.: · 2001
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Learning how to inpaint from global image statistics
Levin, A., Zomet, A., Weiss, Y.: · 2003
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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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K-svd: An algorithm for designing overcomplete dictionaries for sparse representation
Aharon, M., Elad, M., Bruckstein, A.: · 2006
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Example based 3d reconstruction from single 2d images
Hassner, T., Basri, R.: · 2006
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What makes a good model of natural images?
Weiss, Y., Freeman, W.T.: · 2007
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Blind motion deblurring using image statistics
Levin, A.: · 2007
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Surface reconstruction using local shape priors
Gal, R., Shamir, A., Hassner, T., Pauly, M., Cohen-Or, D.: · 2007
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Fields of experts
Roth, S., Black, M.J.: · 2009
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Super-resolution from a single image
Glasner, D., Bagon, S., Irani, M.: · 2009
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Online learning for matrix factorization and sparse coding
Mairal, J., Bach, F., Ponce, J., Sapiro, G.: · 2010
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Kernel density estimation via diffusion
Botev, Z.I., Grotowski, J.F., Kroese, D.P., et al.: · 2010
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From learning models of natural image patches to whole image restoration
Zoran, D., Weiss, Y.: · 2011
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Internal statistics of a single natural image
Zontak, M., Irani, M.: · 2011
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Vision-based hand pose estimation through similarity search using the earth mover’s distance
de Villiers, H.A., van Zijl, L., Niesler, T.R.: · 2012
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Indoor segmentation and support inference from rgbd images
Nathan Silberman, Derek Hoiem, P.K., Fergus, R.: · 2012
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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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Blind deblurring using internal patch recurrence
Michaeli, T., Irani, M.: · 2014
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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
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Image-to-image translation with conditional adversarial networks
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: · 2017
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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., et al.: · 2017
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Enhanced deep residual networks for single image super-resolution
Lim, B., Son, S., Kim, H., Nah, S., Lee, K.M.: · 2017
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Image super-resolution using dense skip connections
Tong, T., Li, G., Liu, X., Gao, Q.: · 2017
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Enhancenet: Single image super-resolution through automated texture synthesis
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Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A.: · 2014
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Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., Chintala, S.: · 2015
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Designing deep networks for surface normal estimation
Wang, X., Fouhey, D., Gupta, A.: · 2015
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Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture
Eigen, D., Fergus, R.: · 2015
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Improved techniques for training gans
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: · 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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Sajjadi, M.S., Scholkopf, B., Hirsch, M.: · 2017
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Ntire 2017 challenge on single image super-resolution: Dataset and study
Agustsson, E., Timofte, R.: · 2017
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Learning a no-reference quality metric for single-image super-resolution
Ma, C., Yang, C.Y., Yang, X., Yang, M.H.: · 2017
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Photographic image synthesis with cascaded refinement networks
Chen, Q., Koltun, V.: · 2017
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Surface normals in the wild
Chen, W., Xiang, D., Deng, J.: · 2017
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The contextual loss for image transformation with non-aligned data
Mechrez, R., Talmi, I., Zelnik-Manor, L.: · 2018
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Pix3d: Dataset and methods for single-image 3d shape modeling
Sun, X., Wu, J., Zhang, X., Zhang, Z., Zhang, C., Xue, T., Tenenbaum, J.B., Freeman, W.T.: · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: · 2018
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The perception-distortion tradeoff
Blau, Y., Michaeli, T.: · 2018
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