Fetching the paper…
Reading the bibliography…
As a successful deep model applied in image super-resolution (SR), the Super-Resolution Convolutional Neural Network (SRCNN) has demonstrated superior performance to the previous hand-crafted models either in speed and restoration quality.
Anchored neighborhood regression for fast example-based super-resolution
Timofte, R., De Smet, V., Van Gool, L.: · 1927
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.
An information fidelity criterion for image quality assessment using natural scene statistics
Sheikh, H.R., Bovik, A.C., De Veciana, G.: · 2005
Earlier work this paper cites.
An information fidelity criterion for image quality assessment using natural scene statistics
Sheikh, H.R., Bovik, A.C., De Veciana, G.: · 2005
Earlier work this paper cites.
Image super-resolution via sparse representation
Yang, J., Wright, J., Huang, T.S., Ma, Y.: · 2010
Earlier work this paper cites.
Single-image super-resolution using sparse regression and natural image prior
Kim, K.I., Kwon, Y.: · 2010
Earlier work this paper cites.
On single image scale-up using sparse-representations
Zeyde, R., Elad, M., Protter, M.: · 2012
Earlier work this paper cites.
Low-complexity single-image super-resolution based on nonnegative neighbor embedding
Bevilacqua, M., Roumy, A., Guillemot, C., Morel, M.L.A.: · 2012
Earlier work this paper cites.
Fast direct super-resolution by simple functions
Yang, C.Y., Yang, M.H.: · 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.
A+: Adjusted anchored neighborhood regression for fast super-resolution
Timofte, R., De Smet, V., Van Gool, L.: · 2014
Cited alongside, same era.
Deep network cascade for image super-resolution
Cui, Z., Chang, H., Shan, S., Zhong, B., Chen, X.: · 2014
Cited alongside, same era.
Visualizing and understanding convolutional networks
Zeiler, M.D., Fergus, R.: · 2014
Cited alongside, same era.
Deep convolutional neural network for image deconvolution
Xu, L., Ren, J.S., Liu, C., Jia, J.: · 2014
Cited alongside, same era.
Exploiting linear structure within convolutional networks for efficient evaluation
Denton, E.L., Zaremba, W., Bruna, J., LeCun, Y., Fergus, R.: · 2014
Cited alongside, same era.
Min Lin, Qiang Chen, S.Y.: · 2014
Deeply improved sparse coding for image super-resolution
Wang, Z., Liu, D., Yang, J., Han, W., Huang, T.: · 2015
Later among the works it cites.
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., Darrell, T.: · 2015
Later among the works it cites.
Learning to generate chairs with convolutional neural networks
Dosovitskiy, A., Tobias Springenberg, J., Brox, T.: · 2015
Later among the works it cites.
Accelerating very deep convolutional networks for classification and detection
Zhang, X., Zou, J., He, K., Sun, J.: · 2015
Later among the works it cites.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
He, K., Zhang, X., Ren, S., Sun, J.: · 2015
Later among the works it cites.
Single image super-resolution from transformed self-exemplars
Huang, J.B., Singh, A., Ahuja, N.: · 2015
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Single-image super-resolution: A benchmark
Yang, C.Y., Ma, C., Yang, M.H.: · 2014
Cited alongside, same era.
Caffe: Convolutional architecture for fast feature embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: · 2014
Cited alongside, same era.
Image super-resolution using deep convolutional networks
Dong, C., Loy, C.C., He, K., Tang, X.: · 2015
Cited alongside, same era.
Fast and accurate image upscaling with super-resolution forests
Schulter, S., Leistner, C., Bischof, H.: · 2015
Cited alongside, same era.
Later among the works it cites.
Deep cascaded bi-network for face hallucination
Zhu, S., Liu, S., Loy, C.C., Tang, X.: · 2016
Closest in time.
Depth map super resolution by deep multi-scale guidance
Hui, T.W., Loy, C.C., Tang, X.: · 2016
Closest in time.
Accurate image super-resolution using very deep convolutional networks
Kim, J., Lee, J.K., Lee, K.M.: · 2016
Closest in time.
Deeply-recursive convolutional network for image super-resolution
Kim, J., Lee, J.K., Lee, K.M.: · 2016
Closest in time.