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Diffusion models have shown promising results on single-image super-resolution and other image- to-image translation tasks.
Real-esrgan: Training real-world blind super-resolution with pure synthetic data
Wang, X., Xie, L., Dong, C., and Shan, Y · 1914
Earlier work this paper cites.
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., and Simoncelli, E. P · 2004
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
Earlier work this paper cites.
Image super-resolution using deep convolutional networks
Dong, C., Loy, C. C., He, K., and Tang, X · 2015
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S · 2015
Earlier work this paper cites.
Accelerating the super-resolution convolutional neural network
Dong, C., Loy, C. C., and Tang, X · 2016
Earlier work this paper cites.
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., and Wang, Z · 2016
Earlier work this paper cites.
Ntire 2017 challenge on single image super-resolution: Dataset and study
Agustsson, E. and Timofte, R · 2017
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A deep convolutional neural network with selection units for super-resolution
Choi, J.-S. and Kim, M · 2017
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Pixel recursive super resolution
Dahl, R., Norouzi, M., and Shlens, J · 2017
Earlier work this paper cites.
Balanced two-stage residual networks for image super-resolution
Fan, Y., Shi, H., Yu, J., Liu, D., Han, W., Yu, H., Wang, Z., Wang, X., and Huang, T. S · 2017
Earlier work this paper cites.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S · 2017
Earlier work this paper cites.
Deep laplacian pyramid networks for fast and accurate super-resolution
Lai, W.-S., Huang, J.-B., Ahuja, N., and Yang, M.-H · 2017
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Enhanced deep residual networks for single image super-resolution
Lim, B., Son, S., Kim, H., Nah, S., and Mu Lee, K · 2017
Earlier work this paper cites.
Image super-resolution via deep recursive residual network
Tai, Y., Yang, J., and Liu, X · 2017
Cited alongside, same era.
Fast, accurate, and lightweight super-resolution with cascading residual network
Ahn, N., Kang, B., and Sohn, K.-A · 2018
Cited alongside, same era.
Fsrnet: End-to-end learning face super-resolution with facial priors
Chen, Y., Tai, Y., Liu, X., Shen, C., and Yang, J · 2018
Cited alongside, same era.
“zero-shot” super-resolution using deep internal learning
Shocher, A., Cohen, N., and Irani, M · 2018
Cited alongside, same era.
Image super-resolution using very deep residual channel attention networks
Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., and Fu, Y · 2018
Cited alongside, same era.
Toward real-world single image super-resolution: A new benchmark and a new model
Blind image super-resolution: A survey and beyond
Liu, A., Liu, Y., Gu, J., Qiao, Y., and Dong, C · 2021
Later among the works it cites.
End-to-end alternating optimization for blind super resolution
Luo, Z., Huang, Y., Li, S., Wang, L., and Tan, T · 2021
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Cinc-gan for effective f 0 prediction for whisper-to-normal speech conversion
Patel, M., Purohit, M., Shah, J., and Patil, H. A · 2021
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Fine-grained attention and feature-sharing generative adversarial networks for single image super-resolution
Yan, Y., Liu, C., Chen, C., Sun, X., Jin, L., Peng, X., and Zhou, X · 2021
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Conditional meta-network for blind super-resolution with multiple degradations
Yin, G., Wang, W., Yuan, Z., Yu, D., Sun, S., and Wang, C · 2021
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Cai, J., Zeng, H., Yong, H., Cao, Z., and Zhang, L · 2019
Cited alongside, same era.
Generative modeling by estimating gradients of the data distribution
Song, Y. and Ermon, S · 2019
Cited alongside, same era.
Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
Cited alongside, same era.
Pulse: Self-supervised photo upsampling via latent space exploration of generative models
Menon, S., Damian, A., Hu, S., Ravi, N., and Rudin, C · 2020
Cited alongside, same era.
Score-based generative modeling through stochastic differential equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B · 2020
Cited alongside, same era.
Catastrophic forgetting and mode collapse in gans
Thanh-Tung, H. and Tran, T · 2020
Cited alongside, same era.
Component divide-and-conquer for real-world image super-resolution
Wei, P., Xie, Z., Lu, H., Zhan, Z., Ye, Q., Zuo, W., and Lin, L · 2020
Cited alongside, same era.
Later among the works it cites.
Designing a practical degradation model for deep blind image super-resolution
Zhang, K., Liang, J., Van Gool, L., and Timofte, R · 2021
Later among the works it cites.
Classifier-free diffusion guidance
Ho, J. and Salimans, T · 2022
Later among the works it cites.
Cascaded diffusion models for high fidelity image generation
Ho, J., Saharia, C., Chan, W., Fleet, D. J., Norouzi, M., and Salimans, T · 2022
Later among the works it cites.
Srdiff: Single image super-resolution with diffusion probabilistic models
Li, H., Yang, Y., Chang, M., Chen, S., Feng, H., Xu, Z., Li, Q., and Chen, Y · 2022
Later among the works it cites.
High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
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Palette: Image-to-image diffusion models
Saharia, C., Chan, W., Chang, H., Lee, C., Ho, J., Salimans, T., Fleet, D., and Norouzi, M · 2022
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Deblurring via stochastic refinement
Whang, J., Delbracio, M., Talebi, H., Saharia, C., Dimakis, A. G., and Milanfar, P · 2022
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Rzsr: Reference-based zero-shot super-resolution with depth guided self-exemplars
Yoo, J.-S., Kim, D.-W., Lu, Y., and Jung, S.-W · 2022
Later among the works it cites.