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Deep convolutional networks have become a popular tool for image generation and restoration.
Graphics gems pp. 147–165 (1990)
Turkowski, K.: Filters for common resampling-tasks · 1990
Earlier work this paper cites.
In: Proceedings of the Eleventh Annual International Conference of the Center for Nonlinear Studies on Experimental Mathematics : Computational Issues in Nonlinear Science: Computational Issues in Nonlinear Science, pp. 259–268. Elsevier North-Holland, Inc., New York, NY, USA (1992)
Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms · 1992
Earlier work this paper cites.
In: NIPS, pp. 551–558. Morgan Kaufmann (1993)
Ruderman, D.L., Bialek, W.: Statistics of natural images: Scaling in the woods · 1993
Earlier work this paper cites.
In: ICIP (1), pp. 379–382. IEEE Computer Society (1996)
Simoncelli, E.P., Adelson, E.H.: Noise removal via bayesian wavelet coring · 1996
Earlier work this paper cites.
In: NIPS, pp. 836–842. The MIT Press (1997)
Turiel, A., Mato, G., Parga, N., Nadal, J.: Self-similarity properties of natural images · 1997
Earlier work this paper cites.
IEEE Trans. Pattern Anal. Mach. Intell
Zhu, S.C., Mumford, D.: Prior learning and gibbs reaction-diffusion · 1997
Earlier work this paper cites.
In: CVPR, pp. 1541–1547. IEEE Computer Society (1999)
Huang, J., Mumford, D.: Statistics of natural images and models · 1999
Earlier work this paper cites.
In: Scale-Space and Morphology in Computer Vision, Third International Conference, pp. 317–325 (2001)
Scherzer, O., Groetsch, C.W.: Inverse scale space theory for inverse problems · 2001
Earlier work this paper cites.
ACM Trans. Graph
Petschnigg, G., Szeliski, R., Agrawala, M., Cohen, M.F., Hoppe, H., Toyama, K.: Digital photography with flash and no-flash image pairs · 2004
Earlier work this paper cites.
In: Proc. CVPR, vol. 2, pp. 60–65. IEEE Computer Society (2005)
Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising · 2005
Earlier work this paper cites.
In: Variational, Geometric, and Level Set Methods in Computer Vision, Third International Workshop, VLSM, pp. 25–36 (2005)
Burger, M., Osher, S.J., Xu, J., Gilboa, G.: Nonlinear inverse scale space methods for image restoration · 2005
Earlier work this paper cites.
IEEE Transactions on image processing
Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3-d transform-domain collaborative filtering · 2007
Earlier work this paper cites.
In: UAI, pp. 149–158. AUAI Press (2007)
Grosse, R.B., Raina, R., Kwong, H., Ng, A.Y.: Shift-invariance sparse coding for audio classification · 2007
Earlier work this paper cites.
Tech. Rep. Technical Report 1341, University of Montreal (2009)
Erhan, D., Bengio, Y., Courville, A., Vincent, P.: Visualizing higher-layer features of a deep network · 2009
Earlier work this paper cites.
In: Proc. ICCV, pp. 349–356 (2009)
Glasner, D., Bagon, S., Irani, M.: Super-resolution from a single image · 2009
Earlier work this paper cites.
SIAM J. Imaging Sciences
Marquina, A.: Nonlinear inverse scale space methods for total variation blind deconvolution · 2009
Earlier work this paper cites.
Journal of Machine Learning Research
Mairal, J., Bach, F., Ponce, J., Sapiro, G.: Online learning for matrix factorization and sparse coding · 2010
Earlier work this paper cites.
In: Proc. CVPR, pp. 2528–2535. IEEE Computer Society (2010)
Zeiler, M.D., Krishnan, D., Taylor, G.W., Fergus, R.: Deconvolutional networks · 2010
Earlier work this paper cites.
In: Curves and Surfaces,
Zeyde, R., Elad, M., Protter, M.: On single image scale-up using sparse-representations · 2010
Earlier work this paper cites.
In: BMVC, pp. 1–10 (2012)
Bevilacqua, M., Roumy, A., Guillemot, C., Alberi-Morel, M.: Low-complexity single-image super-resolution based on nonnegative neighbor embedding · 2012
Earlier work this paper cites.
In: CVPR, pp. 2392–2399 (2012)
Burger, H.C., Schuler, C.J., Harmeling, S.: Image denoising: Can plain neural networks compete with bm3d? · 2012
Cited alongside, same era.
In: F. Pereira, C.J.C. Burges, L. Bottou, K.Q. Weinberger (eds.) Advances in Neural Information Processing Systems 25, pp. 1097–1105. Curran Associates, Inc. (2012)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks · 2012
Cited alongside, same era.
In: CVPR, pp. 391–398. IEEE Computer Society (2013)
Bristow, H., Eriksson, A.P., Lucey, S.: Fast convolutional sparse coding · 2013
Cited alongside, same era.
In: Proc. ECCV, pp. 184–199 (2014)
Dong, C., Loy, C.C., He, K., Tang, X.: Learning a deep convolutional network for image super-resolution · 2014
Cited alongside, same era.
In: Proc. NIPS, pp. 2672–2680 (2014)
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets · 2014
Cited alongside, same era.
IJCV (2016)
Mahendran, A., Vedaldi, A.: Visualizing deep convolutional neural networks using natural pre-images · 2016
Later among the works it cites.
In: Proc. CVPR, pp. 3286–3294. IEEE Computer Society (2017)
Bahat, Y., Efrat, N., Irani, M.: Non-uniform blind deblurring by reblurring · 2017
Closest in time.
CoRR (2017)
Bojanowski, P., Joulin, A., Lopez-Paz, D., Szlam, A.: Optimizing the latent space of generative networks · 2017
Closest in time.
ACM Transactions on Graphics (Proc. of SIGGRAPH)
Iizuka, S., Simo-Serra, E., Ishikawa, H.: Globally and Locally Consistent Image Completion · 2017
Closest in time.
CoRR (2017)
Ilyas, A., Jalal, A., Asteri, E., Daskalakis, C., Dimakis, A.G.: The robust manifold defense: Adversarial training using generative models · 2017
Closest in time.
In: CVPR. IEEE Computer Society (2017)
Lai, W.S., Huang, J.B., Ahuja, N., Yang, M.H.: Deep laplacian pyramid networks for fast and accurate super-resolution · 2017
Closest in time.
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Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization · 2014
Cited alongside, same era.
In: Proc. ICLR (2014)
Kingma, D.P., Welling, M.: Auto-encoding variational bayes · 2014
Cited alongside, same era.
CoRR (2014)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition · 2014
Cited alongside, same era.
In: Proc. CVPR, pp. 1538–1546 (2015)
Dosovitskiy, A., Tobias Springenberg, J., Brox, T.: Learning to generate chairs with convolutional neural networks · 2015
Cited alongside, same era.
In: ICCV, pp. 1823–1831. IEEE Computer Society (2015)
Gu, S., Zuo, W., Xie, Q., Meng, D., Feng, X., Zhang, L.: Convolutional sparse coding for image super-resolution · 2015
Cited alongside, same era.
In: CVPR, pp. 1026–1034. IEEE Computer Society (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: Surpassing human-level performance on imagenet classification · 2015
Cited alongside, same era.
In: CVPR, pp. 5135–5143. IEEE Computer Society (2015)
Heide, F., Heidrich, W., Wetzstein, G.: Fast and flexible convolutional sparse coding · 2015
Cited alongside, same era.
In: CVPR. IEEE Computer Society (2017)
Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., Shi, W.: Photo-realistic single image super-resolution using a generative adversarial network · 2017
Closest in time.
Journal of Machine Learning Research
Papyan, V., Romano, Y., Elad, M.: Convolutional neural networks analyzed via convolutional sparse coding · 2017
Closest in time.
In: ICCV. IEEE Computer Society (2017)
Papyan, V., Romano, Y., Sulam, J., Elad, M.: Convolutional dictionary learning via local processing · 2017
Closest in time.
In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1586–1595 (2017)
Plotz, T., Roth, S.: Benchmarking denoising algorithms with real photographs · 2017
Closest in time.
In: The IEEE International Conference on Computer Vision (ICCV) (2017)
Sajjadi, M.S.M., Scholkopf, B., Hirsch, M.: Enhancenet: Single image super-resolution through automated texture synthesis · 2017
Closest in time.
In: CVPR. IEEE Computer Society (2017)
Tai, Y., Yang, J., Liu, X.: Image super-resolution via deep recursive residual network · 2017
Closest in time.
In: ICLR (2017)
Zhang, C., Bengio, S., Hardt, M., Recht, B., Vinyals, O.: Understanding deep learning requires rethinking generalization · 2017
Closest in time.
In: CVPR. IEEE Computer Society (2018)
Assaf Shocher Nadav Cohen, M.I.: ”zero-shot” super-resolution using deep internal learning · 2018
Closest in time.
CoRR (2018)
Athar, S., Burnaev, E., Lempitsky, V.S.: Latent convolutional models · 2018
Closest in time.
CoRR (2018)
Boominathan, L., Maniparambil, M., Gupta, H., Baburajan, R., Mitra, K.: Phase retrieval for fourier ptychography under varying amount of measurements · 2018
Closest in time.
CoRR (2018)
Shedligeri, P.A., Shah, K., Kumar, D., Mitra, K.: Photorealistic image reconstruction from hybrid intensity and event based sensor · 2018
Closest in time.
In: CVPR. IEEE Computer Society (2018)
Ulyanov, D., Vedaldi, A., Lempitsky, V.: Deep image prior · 2018
Closest in time.
CoRR (2018)
Veen, D.V., Jalal, A., Price, E., Vishwanath, S., Dimakis, A.G.: Compressed sensing with deep image prior and learned regularization · 2018
Closest in time.