Fetching the paper…
Reading the bibliography…
Convolutional neural network (CNN) depth is of crucial importance for image super-resolution (SR).
Learning low-level vision
Freeman, W.T., Pasztor, E.C., Carmichael, O.T.: · 2000
Earlier work this paper cites.
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
Earlier work this paper cites.
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: · 2004
Earlier work this paper cites.
Rectified linear units improve restricted boltzmann machines
Nair, V., Hinton, G.E.: · 2010
Earlier work this paper cites.
On single image scale-up using sparse-representations
Zeyde, R., Elad, M., Protter, M.: · 2010
Earlier work this paper cites.
Very low resolution face recognition problem
Zou, W.W., Yuen, P.C.: · 2012
Earlier work this paper cites.
Low-complexity single-image super-resolution based on nonnegative neighbor embedding
Bevilacqua, M., Roumy, A., Guillemot, C., Alberi-Morel, M.L.: · 2012
Earlier work this paper cites.
Cardiac image super-resolution with global correspondence using multi-atlas patchmatch
Shi, W., Caballero, J., Ledig, C., Zhuang, X., Bai, W., Bhatia, K., de Marvao, A.M.S.M., Dawes, T., O’Regan, D., Rueckert, D.: · 2013
Earlier work this paper cites.
Learning a deep convolutional network for image super-resolution
Dong, C., Loy, C.C., He, K., Tang, X.: · 2014
Earlier work this paper cites.
Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D., Ba, J.: · 2014
Earlier work this paper cites.
A statistical prediction model based on sparse representations for single image super-resolution
Peleg, T., Elad, M.: · 2014
Earlier work this paper cites.
Deep networks for image super-resolution with sparse prior
Wang, Z., Liu, D., Yang, J., Han, W., Huang, T.: · 2015
Earlier work this paper cites.
Single image super-resolution from transformed self-exemplars
Huang, J.B., Singh, A., Ahuja, N.: · 2015
Earlier work this paper cites.
Look and think twice: Capturing top-down visual attention with feedback convolutional neural networks
Cao, C., Liu, X., Yang, Y., Yu, Y., Wang, J., Wang, Z., Huang, Y., Wang, L., Huang, C., Xu, W., Ramanan, D., Huang, T.S.: · 2015
Earlier work this paper cites.
Spatial transformer networks
Jaderberg, M., Simonyan, K., Zisserman, A., Kavukcuoglu, K.: · 2015
Cited alongside, same era.
Image super-resolution using deep convolutional networks
Dong, C., Loy, C.C., He, K., Tang, X.: · 2016
Cited alongside, same era.
Accelerating the super-resolution convolutional neural network
Dong, C., Loy, C.C., Tang, X.: · 2016
Cited alongside, same era.
Accurate image super-resolution using very deep convolutional networks
Kim, J., Kwon Lee, J., Mu Lee, K.: · 2016
Cited alongside, same era.
Deeply-recursive convolutional network for image super-resolution
Kim, J., Kwon Lee, J., Mu Lee, K.: · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2016
Cited alongside, same era.
Memnet: A persistent memory network for image restoration
Tai, Y., Yang, J., Liu, X., Xu, C.: · 2017
Later among the works it cites.
Enhanced deep residual networks for single image super-resolution
Lim, B., Son, S., Kim, H., Nah, S., Lee, K.M.: · 2017
Later among the works it cites.
Learning deep cnn denoiser prior for image restoration
Zhang, K., Zuo, W., Gu, S., Zhang, L.: · 2017
Later among the works it cites.
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., Shi, W.: · 2017
Later among the works it cites.
Squeeze-and-excitation networks
Hu, J., Shen, L., Sun, G.: · 2017
Later among the works it cites.
Residual attention network for image classification
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Perceptual losses for real-time style transfer and super-resolution
Johnson, J., Alahi, A., Fei-Fei, L.: · 2016
Cited alongside, same era.
Joint line segmentation and transcription for end-to-end handwritten paragraph recognition
Bluche, T.: · 2016
Cited alongside, same era.
Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network
Shi, W., Caballero, J., Huszár, F., Totz, J., Aitken, A.P., Bishop, R., Rueckert, D., Wang, Z.: · 2016
Cited alongside, same era.
Seven ways to improve example-based single image super resolution
Timofte, R., Rothe, R., Van Gool, L.: · 2016
Cited alongside, same era.
Psyco: Manifold span reduction for super resolution
Pérez-Pellitero, E., Salvador, J., Ruiz-Hidalgo, J., Rosenhahn, B.: · 2016
Cited alongside, same era.
Image super-resolution via deep recursive residual network
Tai, Y., Yang, J., Liu, X.: · 2017
Cited alongside, same era.
Wang, F., Jiang, M., Qian, C., Yang, S., Li, C., Zhang, H., Wang, X., Tang, X.: · 2017
Later among the works it cites.
Learnable pooling with context gating for video classification
Miech, A., Laptev, I., Sivic, J.: · 2017
Later among the works it cites.
A learned representation for artistic style
Dumoulin, V., Shlens, J., Kudlur, M.: · 2017
Later among the works it cites.
Ntire 2017 challenge on single image super-resolution: Methods and results
Timofte, R., Agustsson, E., Van Gool, L., Yang, M.H., Zhang, L., Lim, B., Son, S., Kim, H., Nah, S., Lee, K.M., et al.: · 2017
Later among the works it cites.
Sketch-based manga retrieval using manga109 dataset
Matsui, Y., Ito, K., Aramaki, Y., Fujimoto, A., Ogawa, T., Yamasaki, T., Aizawa, K.: · 2017
Later among the works it cites.
Automatic differentiation in pytorch
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., Lerer, A.: · 2017
Later among the works it cites.
Learning a single convolutional super-resolution network for multiple degradations
Zhang, K., Zuo, W., Zhang, L.: · 2018
Closest in time.
Deep back-projection networks for super-resolution
Haris, M., Shakhnarovich, G., Ukita, N.: · 2018
Closest in time.
Residual dense network for image super-resolution
Zhang, Y., Tian, Y., Kong, Y., Zhong, B., Fu, Y.: · 2018
Closest in time.
Tell me where to look: Guided attention inference network
Li, K., Wu, Z., Peng, K.C., Ernst, J., Fu, Y.: · 2018
Closest in time.