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Cited by 777Advanced Image Processing TechniquesGenerative Adversarial Networks and Image SynthesisImage Enhancement Techniques

The DOI matches its Crossref record; 33 of the 42 references it cites were found in a registry or an index.

SRDiff: Single image super-resolution with diffusion probabilistic models

Open access
Li, Haoying, Yang, Yifan, Chang, Meng, Chen, Shiqi, Feng, Huajun, Xu, Zhihai, Li, Qi, Chen, Yueting·2022·Neurocomputing
DOI 10.1016/j.neucom.2022.01.029
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Crossref · 10.1016/j.neucom.2022.01.029

Cites

33 of 33 references verified0 citation errors · 0 not checkable

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Citations

Cites

Research works printed but not linked: 26

  1. D. Kim, M. Kim, G. Kwon, D.-S. Kim, Progressive face super-resolution via attention to facial landmark, arXiv preprint arXiv:1908.08239. · arXiv
  2. C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A.P. Aitken, A. Tejani, J. Totz, Z. Wang, et al., Photo-realistic single image super-resolution using a generative adversarial network, CVPR.
  3. X. Wang, K. Yu, S. Wu, J. Gu, Y. Liu, C. Dong, C.C. Loy, Y. Qiao, X. Tang, Esrgan: Enhanced super-resolution generative adversarial networks, ECCV.
  4. J. Ho, A. Jain, P. Abbeel, Denoising diffusion probabilistic models, NeurIPS.
  5. Z. Kong, W. Ping, J. Huang, K. Zhao, B. Catanzaro, Diffwave: A versatile diffusion model for audio synthesis, arXiv preprint arXiv:2009.09761. · arXiv
  6. J. Liu, C. Li, Y. Ren, F. Chen, P. Liu, Z. Zhao, Diffsinger: Diffusion acoustic model for singing voice synthesis, arXiv preprint arXiv:2105.02446. · arXiv
  7. G. Mittal, J. Engel, C. Hawthorne, I. Simon, Symbolic music generation with diffusion models, arXiv preprint arXiv:2103.16091. · arXiv
  8. S. Luo, W. Hu, Diffusion probabilistic models for 3d point cloud generation, arXiv preprint arXiv:2103.01458. · arXiv
  9. R. Timofte, S. Gu, J. Wu, L. Van Gool, Ntire 2018 challenge on single image super-resolution: methods and results, in: CVPR Workshops, 2018.
  10. M.C. Bühler, A. Romero, R. Timofte, Deepsee: Deep disentangled semantic explorative extreme super-resolution, arXiv preprint arXiv:2004.04433. · arXiv
  11. M. Mathieu, C. Couprie, Y. LeCun, Deep multi-scale video prediction beyond mean square error, arXiv preprint arXiv:1511.05440. · arXiv
  12. D. Patel, A.A. Oberai, Bayesian inference with generative adversarial network priors, arXiv preprint arXiv:1907.09987. · arXiv
  13. D. Yang, S. Hong, Y. Jang, T. Zhao, H. Lee, Diversity-sensitive conditional generative adversarial networks, arXiv preprint arXiv:1901.09024. · arXiv
  14. L. Dinh, D. Krueger, Y. Bengio, Nice: Non-linear independent components estimation, arXiv preprint arXiv:1410.8516. · arXiv
  15. L. Dinh, J. Sohl-Dickstein, S. Bengio, Density estimation using real nvp, arXiv preprint arXiv:1605.08803. · arXiv
  16. D.P. Kingma, P. Dhariwal, Glow: Generative flow with invertible 1x1 convolutions, in: Advances in neural information processing systems, 2018, pp. 10215–10224.
  17. N. Chen, Y. Zhang, H. Zen, R.J. Weiss, M. Norouzi, W. Chan, Wavegrad: Estimating gradients for waveform generation, in: ICLR, 2021. URL:https://openreview.net/forum?id=NsMLjcFaO8O
  18. D. Misra, Mish: A self regularized non-monotonic neural activation function, arXiv preprint arXiv:1908.08681. · arXiv
  19. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, I. Polosukhin, Attention is all you need, in: NeurIPS, 2017, pp. 5998–6008.
  20. Z. Liu, P. Luo, X. Wang, X. Tang, Deep learning face attributes in the wild, in: ICCV, 2015. · DOI · Paper page
  21. A.Q. Nichol, P. Dhariwal, Improved denoising diffusion probabilistic models (2021). URL:https://openreview.net/forum?id=-NEXDKk8gZ.
  22. D.P. Kingma, J. Ba, Adam: A method for stochastic optimization, arXiv preprint arXiv:1412.6980. · arXiv
  23. R. Zhang, P. Isola, A.A. Efros, E. Shechtman, O. Wang, The unreasonable effectiveness of deep features as a perceptual metric, CVPR.
  24. Z. Kong, W. Ping, On fast sampling of diffusion probabilistic models, arXiv preprint arXiv:2106.00132. · arXiv
  25. D. Watson, J. Ho, M. Norouzi, W. Chan, Learning to efficiently sample from diffusion probabilistic models, arXiv preprint arXiv:2106.03802. · arXiv
  26. A. Jolicoeur-Martineau, K. Li, R. Piché-Taillefer, T. Kachman, I. Mitliagkas, Gotta go fast when generating data with score-based models, arXiv preprint arXiv:2105.14080. · arXiv

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Metadata

Authors
Li, H., Yang, Y., Chang, M., Chen, S., Feng, H., Xu, Z., Li, Q., & Chen, Y.
Affiliations
State Key Laboratory of Modern Optical Instruments (CN)
Corresponding author
Feng, Huajun
ORCID
Venue
Neurocomputing
Publisher
Elsevier BV
Published
2022-03
Volume
479
Pages
47-59
ISSN
0925-2312 (ISSN-L)
DOI
10.1016/j.neucom.2022.01.029
OpenAlex
W4226014430
Cited by
777
References
42
Citations by year
2026: 162 · 2025: 237 · 2024: 268 · 2023: 91 · 2022: 15
FWCI
48.22
Type
journal article
Language
en
Open access
Licence
Locations
  • Neurocomputing (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
National Natural Science Foundation of China (61975175)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Enhancement Techniques
Keywords
single image super-resolution, diffusion probabilistic models, Markov chains, residual prediction, over-smoothing, mode collapse, model size reduction, latent space interpolation

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