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We consider image transformation problems, where an input image is transformed into an output image.
Learning hierarchical features for scene labeling
Farabet, C., Couprie, C., Najman, L., LeCun, Y.: · 1929
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Improving resolution by image registration
Irani, M., Peleg, S.: · 1991
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Example-based super-resolution
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Image hallucination with primal sketch priors
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Chang, H., Yeung, D.Y., Xiong, Y.: · 2004
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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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Image up-sampling using total-variation regularization with a new observation model
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A statistical evaluation of recent full reference image quality assessment algorithms
Sheikh, H.R., Sabir, M.F., Bovik, A.C.: · 2006
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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., Sun, J., Xu, Z., Shum, H.Y.: · 2008
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Fast image/video upsampling
Shan, Q., Li, Z., Jia, J., Tang, C.K.: · 2008
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Image super-resolution as sparse representation of raw image patches
Yang, J., Wright, J., Huang, T., Ma, Y.: · 2008
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Scope of validity of psnr in image/video quality assessment
Huynh-Thu, Q., Ghanbari, M.: · 2008
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Super-resolution from a single image
Glasner, D., Bagon, S., Irani, M.: · 2009
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Mean squared error: love it or leave it? a new look at signal fidelity measures
Wang, Z., Bovik, A.C.: · 2009
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Single-image super-resolution using sparse regression and natural image prior
Kim, K.I., Kwon, Y.: · 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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Non-local kernel regression for image and video restoration
Zhang, H., Yang, J., Zhang, Y., Huang, T.S.: · 2010
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On single image scale-up using sparse-representations
Zeyde, R., Elad, M., Protter, M.: · 2010
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Image and video upscaling from local self-examples
Freedman, G., Fattal, R.: · 2011
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Torch7: A matlab-like environment for machine learning
Collobert, R., Kavukcuoglu, K., Farabet, C.: · 2011
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Beyond bits: Reconstructing images from local binary descriptors
d’Angelo, E., Alahi, A., Vandergheynst, P.: · 2012
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Low-complexity single-image super-resolution based on nonnegative neighbor embedding
Bevilacqua, M., Roumy, A., Guillemot, C., Alberi-Morel, M.L.: · 2012
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Simonyan, K., Vedaldi, A., Zisserman, A.: · 2013
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Recurrent convolutional neural networks for scene parsing
Pinheiro, P.H., Collobert, R.: · 2013
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Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: · 2013
Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture
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Understanding deep image representations by inverting them
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Understanding neural networks through deep visualization
Yosinski, J., Clune, J., Nguyen, A., Fuchs, T., Lipson, H.: · 2015
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Texture synthesis using convolutional neural networks
Gatys, L.A., Ecker, A.S., Bethge, M.: · 2015
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A neural algorithm of artistic style
Gatys, L.A., Ecker, A.S., Bethge, M.: · 2015
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Hoggles: Visualizing object detection features
Vondrick, C., Khosla, A., Malisiewicz, T., Torralba, A.: · 2013
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Fast image super-resolution based on in-place example regression
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Beta process joint dictionary learning for coupled feature spaces with application to single image super-resolution
He, L., Qi, H., Zaretzki, R.: · 2013
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Benchmarking of quality metrics on ultra-high definition video sequences
Hanhart, P., Korshunov, P., Ebrahimi, T.: · 2013
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Depth map prediction from a single image using a multi-scale deep network
Eigen, D., Puhrsch, C., Fergus, R.: · 2014
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Learning a deep convolutional network for image super-resolution
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Single-image super-resolution: a benchmark
Yang, C.Y., Ma, C., Yang, M.H.: · 2014
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Conditional random fields as recurrent neural networks
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Deep convolutional neural fields for depth estimation from a single image
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Designing deep networks for surface normal estimation
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Nguyen, A., Yosinski, J., Clune, J.: · 2015
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Inverting visual representations with convolutional networks
Dosovitskiy, A., Brox, T.: · 2015
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Fast and accurate image upscaling with super-resolution forests
Schulter, S., Leistner, C., Bischof, H.: · 2015
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Single image super-resolution from transformed self-exemplars
Huang, J.B., Singh, A., Ahuja, N.: · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., Chintala, S.: · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S., Szegedy, C.: · 2015
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ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: · 2015
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Full-reference visual quality assessment for synthetic images: A subjective study
Kundu, D., Evans, B.L.: · 2015
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Training and investigating residual nets
Gross, S., Wilber, M.: · 2016
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Robust web image/video super-resolution
Xiong, Z., Sun, X., Wu, F.: · 2028
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