Fetching the paper…
Reading the bibliography…
Diffusion Models (DMs) have disrupted the image Super-Resolution (SR) field and further closed the gap between image quality and human perceptual preferences.
R. Keys, “Cubic convolution interpolation for digital image processing,” IEEE Transactions on Acoustics, Speech, and Signal Processing , vol. 29, no. 6, 1981
1981
Earlier work this paper cites.
G. Parisi, “Correlation functions and computer simulations,” Nuclear Physics B , vol. 180, no. 3, 1981
1981
Earlier work this paper cites.
B. D. Anderson, “Reverse-time diffusion equation models,” Stochastic Processes and their Applications , vol. 12, no. 3, 1982
1982
Earlier work this paper cites.
M. Irani and S. Peleg, “Improving resolution by image registration,” CVGIP: Graphical Models and Image Processing , vol. 53, no. 3, 1991
1991
Earlier work this paper cites.
D. Martin, C. Fowlkes, D. Tal, and J. Malik, “A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics,” in ICCV , vol. 2. IEEE, 2001
2001
Earlier work this paper cites.
W. Freeman, T. Jones, and E. Pasztor, “Example-based super-resolution,” IEEE Computer Graphics and Applications , vol. 22, no. 2, 2002
2002
Earlier work this paper cites.
H. Chang, D.-Y. Yeung, and Y. Xiong, “Super-resolution through neighbor embedding,” in Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. , vol. 1. IEEE, 2004
2004
Earlier work this paper cites.
A. Hyvärinen and P. Dayan, “Estimation of non-normalized statistical models by score matching.” Journal of Machine Learning Research , vol. 6, no. 4, 2005
2005
Earlier work this paper cites.
J. Sun, Z. Xu, and H.-Y. Shum, “Image super-resolution using gradient profile prior,” in CVPR , 2008
2008
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in CVPR , 2009
2009
Earlier work this paper cites.
R. Zeyde, M. Elad, and M. Protter, “On single image scale-up using sparse-representations,” in International conference on curves and surfaces . Springer, 2010
2010
Earlier work this paper cites.
K. I. Kim and Y. Kwon, “Single-image super-resolution using sparse regression and natural image prior,” IEEE TPAMI , vol. 32, no. 6, 2010
2010
Earlier work this paper cites.
J. Yang, J. Wright, T. S. Huang, and Y. Ma, “Image super-resolution via sparse representation,” IEEE TIP , vol. 19, no. 11, 2010
2010
Earlier work this paper cites.
G. Freedman and R. Fattal, “Image and video upscaling from local self-examples,” ACM Trans. Graph. , vol. 30, no. 2, apr 2011
2011
Earlier work this paper cites.
P. Vincent, “A connection between score matching and denoising autoencoders,” Neural computation , vol. 23, no. 7, 2011
2011
Earlier work this paper cites.
M. Bevilacqua, A. Roumy, C. Guillemot, and M. L. Alberi-Morel, “Low-complexity single-image super-resolution based on nonnegative neighbor embedding,” 2012
2012
Earlier work this paper cites.
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, “The PASCAL voc2012 Results,” http://www.pascal-network.org/challenges/VOC/voc2012/workshop/index.html, 2012
2012
Earlier work this paper cites.
A. Mittal, A. K. Moorthy, and A. C. Bovik, “No-reference image quality assessment in the spatial domain,” IEEE TIP , vol. 21, no. 12, 2012
2012
Earlier work this paper cites.
A. Mittal, R. Soundararajan, and A. C. Bovik, “Making a “completely blind” image quality analyzer,” IEEE Signal processing letters , vol. 20, no. 3, 2012
2012
Earlier work this paper cites.
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” arXiv:1312.6114 , 2013
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” NeurIPS , vol. 27, 2014
2014
Earlier work this paper cites.
J.-B. Huang, A. Singh, and N. Ahuja, “Single image super-resolution from transformed self-exemplars,” in CVPR , 2015
2015
Earlier work this paper cites.
C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE TPAMI , vol. 38, no. 2, 2015
2015
Earlier work this paper cites.
N. Ponomarenko, L. Jin, O. Ieremeiev, V. Lukin, K. Egiazarian, J. Astola, B. Vozel, K. Chehdi, M. Carli, F. Battisti et al. , “Image database tid2013: Peculiarities, results and perspectives,” Signal processing: Image communication , vol. 30, 2015
2015
Earlier work this paper cites.
J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli, “Deep unsupervised learning using nonequilibrium thermodynamics,” in ICML . PMLR, 2015
2015
Earlier work this paper cites.
D. Rezende and S. Mohamed, “Variational inference with normalizing flows,” in ICML . PMLR, 2015
2015
Earlier work this paper cites.
C. Dong, C. C. Loy, and X. Tang, “Accelerating the super-resolution convolutional neural network,” in ECCV . Springer, 2016
2016
Earlier work this paper cites.
W. Shi, J. Caballero, F. Huszár, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert, and Z. Wang, “Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network,” in CVPR , 2016
2016
Earlier work this paper cites.
J. Kim, J. K. Lee, and K. M. Lee, “Deeply-recursive convolutional network for image super-resolution,” in CVPR , 2016
2016
Earlier work this paper cites.
MATLAB , The Mathworks, Inc., Natick, Massachusetts, 2017
2017
Earlier work this paper cites.
E. Agustsson and R. Timofte, “Ntire 2017 challenge on single image super-resolution: Dataset and study,” in CVPRW , July 2017
2017
Earlier work this paper cites.
Y. Matsui, K. Ito, Y. Aramaki, A. Fujimoto, T. Ogawa, T. Yamasaki, and K. Aizawa, “Sketch-based manga retrieval using manga109 dataset,” Multimedia Tools and Applications , vol. 76, no. 20, 2017
2017
Earlier work this paper cites.
E. Agustsson and R. Timofte, “Ntire 2017 challenge on single image super-resolution: Dataset and study,” in CVPRW , 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang et al. , “Photo-realistic single image super-resolution using a generative adversarial network,” in CVPR , 2017
2017
Earlier work this paper cites.
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in CVPR , 2017
2017
Earlier work this paper cites.
T. Tong, G. Li, X. Liu, and Q. Gao, “Image super-resolution using dense skip connections,” in ICCV , 2017
2017
Earlier work this paper cites.
Y. Tai, J. Yang, and X. Liu, “Image super-resolution via deep recursive residual network,” in CVPR , 2017
2017
Earlier work this paper cites.
C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang et al. , “Photo-realistic single image super-resolution using a generative adversarial network,” in CVPR , 2017
2017
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Communications of the ACM , vol. 60, no. 6, 2017
2017
Earlier work this paper cites.
J. Kim and S. Lee, “Deep learning of human visual sensitivity in image quality assessment framework,” in CVPR , 2017
2017
Earlier work this paper cites.
Z. Dai, Z. Yang, F. Yang, W. W. Cohen, and R. R. Salakhutdinov, “Good semi-supervised learning that requires a bad gan,” NeurIPS , vol. 30, 2017
2017
Earlier work this paper cites.
T. Guo, H. Seyed Mousavi, T. Huu Vu, and V. Monga, “Deep wavelet prediction for image super-resolution,” in CVPRW , 2017
2017
Earlier work this paper cites.
B. Lim, S. Son, H. Kim, S. Nah, and K. Mu Lee, “Enhanced deep residual networks for single image super-resolution,” in CVPRW , 2017
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” NeurIPS , vol. 30, 2017
2017
Earlier work this paper cites.
X. Liu, J. Van De Weijer, and A. D. Bagdanov, “Rankiqa: Learning from rankings for no-reference image quality assessment,” in ICCV , 2017
2017
Earlier work this paper cites.
K. Ma, W. Liu, T. Liu, Z. Wang, and D. Tao, “dipiq: Blind image quality assessment by learning-to-rank discriminable image pairs,” IEEE TIP , vol. 26, no. 8, 2017
2017
Earlier work this paper cites.
N. Ahn, B. Kang, and K.-A. Sohn, “Fast, accurate, and lightweight super-resolution with cascading residual network,” in ECCV , 2018
2018
Earlier work this paper cites.
X. Wang, K. Yu, S. Wu, J. Gu, Y. Liu, C. Dong, Y. Qiao, and C. Change Loy, “Esrgan: Enhanced super-resolution generative adversarial networks,” 2018
2018
Earlier work this paper cites.
H. Talebi and P. Milanfar, “Nima: Neural image assessment,” IEEE TIP , vol. 27, no. 8, 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
Y. Chen, Y. Tai, X. Liu, C. Shen, and J. Yang, “Fsrnet: End-to-end learning face super-resolution with facial priors,” in CVPR , 2018
2018
Earlier work this paper cites.
A. Shocher, N. Cohen, and M. Irani, ““zero-shot” super-resolution using deep internal learning,” in CVPR , 2018
2018
Earlier work this paper cites.
R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang, “The unreasonable effectiveness of deep features as a perceptual metric,” in CVPR , 2018
2018
Earlier work this paper cites.
Y. Song and S. Ermon, “Generative modeling by estimating gradients of the data distribution,” NeurIPS , vol. 32, 2019
2019
Earlier work this paper cites.
T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” in CVPR , 2019
2019
Earlier work this paper cites.
J. Ho, E. Lohn, and P. Abbeel, “Compression with flows via local bits-back coding,” NeurIPS , vol. 32, 2019
2019
Earlier work this paper cites.
W. Zhang, Y. Liu, C. Dong, and Y. Qiao, “Ranksrgan: Generative adversarial networks with ranker for image super-resolution,” in CVPR , 2019
2019
Earlier work this paper cites.
J. Schwab, S. Antholzer, and M. Haltmeier, “Deep null space learning for inverse problems: convergence analysis and rates,” Inverse Problems , vol. 35, no. 2, 2019
2019
Earlier work this paper cites.
S. Yun, D. Han, S. J. Oh, S. Chun, J. Choe, and Y. Yoo, “Cutmix: Regularization strategy to train strong classifiers with localizable features,” in CVPR , 2019
2019
Earlier work this paper cites.
W. Sun and Z. Chen, “Learned image downscaling for upscaling using content adaptive resampler,” IEEE TIP , vol. 29, 2020
2020
Earlier work this paper cites.
J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” NeurIPS , vol. 33, 2020
2020
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial networks,” Communications of the ACM , vol. 63, no. 11, 2020
2020
Cited alongside, same era.
2020
Cited alongside, same era.
S. Anwar and N. Barnes, “Densely residual laplacian super-resolution,” IEEE TPAMI , 2020
2020
Cited alongside, same era.
A. Lugmayr, M. Danelljan, L. Van Gool, and R. Timofte, “Srflow: Learning the super-resolution space with normalizing flow,” in ECCV . Springer, 2020
2020
Cited alongside, same era.
Y. Song, S. Garg, J. Shi, and S. Ermon, “Sliced score matching: A scalable approach to density and score estimation,” in Uncertainty in Artificial Intelligence . PMLR, 2020
2022
Later among the works it cites.
J. Choi, J. Lee, C. Shin, S. Kim, H. Kim, and S. Yoon, “Perception prioritized training of diffusion models,” in CVPR , 2022
2022
Later among the works it cites.
B. Kawar, M. Elad, S. Ermon, and J. Song, “Denoising diffusion restoration models,” NeurIPS , vol. 35, 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2020
Cited alongside, same era.
J. Song, C. Meng, and S. Ermon, “Denoising diffusion implicit models,” arXiv:2010.02502 , 2020
2020
Cited alongside, same era.
A. Lugmayr, M. Danelljan, L. V. Gool, and R. Timofte, “Srflow: Learning the super-resolution space with normalizing flow,” in ECCV . Springer, 2020
2020
Cited alongside, same era.
S. Menon, A. Damian, S. Hu, N. Ravi, and C. Rudin, “Pulse: Self-supervised photo upsampling via latent space exploration of generative models,” in CVPR , 2020
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2020
Cited alongside, same era.
D. Valsesia and E. Magli, “Permutation invariance and uncertainty in multitemporal image super-resolution,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, 2021
2021
Cited alongside, same era.
S. M. A. Bashir, Y. Wang, M. Khan, and Y. Niu, “A comprehensive review of deep learning-based single image super-resolution,” PeerJ Computer Science , vol. 7, 2021
2021
Cited alongside, same era.
2022
Later among the works it cites.
A. Lugmayr, M. Danelljan, A. Romero, F. Yu, R. Timofte, and L. Van Gool, “Repaint: Inpainting using denoising diffusion probabilistic models,” in CVPR , 2022
2022
Later among the works it cites.
H. Chung, B. Sim, and J. C. Ye, “Come-closer-diffuse-faster: Accelerating conditional diffusion models for inverse problems through stochastic contraction,” in CVPR , 2022
2022
Later among the works it cites.
H. Chung, B. Sim, D. Ryu, and J. C. Ye, “Improving diffusion models for inverse problems using manifold constraints,” NeurIPS , vol. 35, 2022
2022
Later among the works it cites.
J. Song, A. Vahdat, M. Mardani, and J. Kautz, “Pseudoinverse-guided diffusion models for inverse problems,” in ICLR , 2022
2022
Later among the works it cites.
H. Chung, E. S. Lee, and J. C. Ye, “Mr image denoising and super-resolution using regularized reverse diffusion,” IEEE Transactions on Medical Imaging , vol. 42, no. 4, 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
J. Liu, Z. Yuan, Z. Pan, Y. Fu, L. Liu, and B. Lu, “Diffusion model with detail complement for super-resolution of remote sensing,” Remote Sensing , vol. 14, no. 19, 2022
2022
Later among the works it cites.
D. Ganguli, D. Hernandez, L. Lovitt, A. Askell, Y. Bai, A. Chen, T. Conerly, N. Dassarma, D. Drain, N. Elhage et al. , “Predictability and surprise in large generative models,” in Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency , 2022
2022
Later among the works it cites.
B. Jing, G. Corso, R. Berlinghieri, and T. Jaakkola, “Subspace diffusion generative models,” in ECCV . Springer, 2022
2022
Later among the works it cites.
C. Saharia, J. Ho, W. Chan, T. Salimans, D. J. Fleet, and M. Norouzi, “Image super-resolution via iterative refinement,” IEEE TPAMI , vol. 45, no. 4, 2023
2023
Later among the works it cites.
B. B. Moser, F. Raue, S. Frolov, S. Palacio, J. Hees, and A. Dengel, “Hitchhiker’s guide to super-resolution: Introduction and recent advances,” IEEE TPAMI , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
X. Chen, X. Wang, J. Zhou, Y. Qiao, and C. Dong, “Activating more pixels in image super-resolution transformer,” in CVPR , 2023
2023
Later among the works it cites.
J. Wang, K. C. Chan, and C. C. Loy, “Exploring clip for assessing the look and feel of images,” in AAAI , vol. 37, no. 2, 2023
2023
Later among the works it cites.
L. Yang, Z. Zhang, Y. Song, S. Hong, R. Xu, Y. Zhao, W. Zhang, B. Cui, and M.-H. Yang, “Diffusion models: A comprehensive survey of methods and applications,” ACM Computing Surveys , vol. 56, no. 4, 2023
2023
Later among the works it cites.
L. Zhang, A. Rao, and M. Agrawala, “Adding conditional control to text-to-image diffusion models,” in CVPR , 2023
2023
Later among the works it cites.
C. Meng, R. Rombach, R. Gao, D. Kingma, S. Ermon, J. Ho, and T. Salimans, “On distillation of guided diffusion models,” in CVPR , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
B. B. Moser, S. Frolov, F. Raue, S. Palacio, and A. Dengel, “Dwa: Differential wavelet amplifier for image super-resolution,” in Artificial Neural Networks and Machine Learning – ICANN 2023 , L. Iliadis, A. Papaleonidas, P. Angelov, and C. Jayne, Eds. Cham: Springer Nature Switzerland, 2023
2023
Later among the works it cites.
B. Moser, S. Frolov, F. Raue, S. Palacio, and A. Dengel, “Waving goodbye to low-res: A diffusion-wavelet approach for image super-resolution,” 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Z. Yue, J. Wang, and C. C. Loy, “Resshift: Efficient diffusion model for image super-resolution by residual shifting,” 2023
2023
Later among the works it cites.
A. Niu, K. Zhang, T. X. Pham, J. Sun, Y. Zhu, I. S. Kweon, and Y. Zhang, “Cdpmsr: Conditional diffusion probabilistic models for single image super-resolution,” 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
B. Fei, Z. Lyu, L. Pan, J. Zhang, W. Yang, T. Luo, B. Zhang, and B. Dai, “Generative diffusion prior for unified image restoration and enhancement,” in CVPR , 2023
2023
Later among the works it cites.
Y. Wang, Y. Hu, J. Yu, and J. Zhang, “Gan prior based null-space learning for consistent super-resolution,” in AAAI , vol. 37, no. 3, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Z. Wang, Z. Zhang, X. Zhang, H. Zheng, M. Zhou, Y. Zhang, and Y. Wang, “Dr2: Diffusion-based robust degradation remover for blind face restoration,” in CVPR , 2023
2023
Later among the works it cites.
X. Wang, S. López-Tapia, and A. K. Katsaggelos, “Atmospheric turbulence correction via variational deep diffusion,” in 2023 IEEE 6th International Conference on MIPR . IEEE, 2023
2023
Later among the works it cites.
N. G. Nair, K. Mei, and V. M. Patel, “At-ddpm: Restoring faces degraded by atmospheric turbulence using denoising diffusion probabilistic models,” in WACV , 2023
2023
Later among the works it cites.
Y. Xiao, Q. Yuan, K. Jiang, J. He, X. Jin, and L. Zhang, “Ediffsr: An efficient diffusion probabilistic model for remote sensing image super-resolution,” IEEE Transactions on Geoscience and Remote Sensing , 2023
2023
Later among the works it cites.
A. M. Ali, B. Benjdira, A. Koubaa, W. Boulila, and W. El-Shafai, “Tesr: Two-stage approach for enhancement and super-resolution of remote sensing images,” Remote Sensing , vol. 15, no. 9, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
Z. Cheng, “Sampler scheduler for diffusion models,” arXiv:2311.06845 , 2023
2023
Later among the works it cites.
T. Chen, “On the importance of noise scheduling for diffusion models,” arXiv:2301.10972 , 2023
2023
Later among the works it cites.
M. Kwon, J. Jeong, and Y. Uh, “Diffusion models already have a semantic latent space,” in ICLR , 2023
2023
Later among the works it cites.
Q. Wu, Y. Liu, H. Zhao, A. Kale, T. Bui, T. Yu, Z. Lin, Y. Zhang, and S. Chang, “Uncovering the disentanglement capability in text-to-image diffusion models,” in CVPR , 2023
2023
Later among the works it cites.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
W. Zhao, L. Bai, Y. Rao, J. Zhou, and J. Lu, “Unipc: A unified predictor-corrector framework for fast sampling of diffusion models,” NeurIPS , vol. 36, 2024
2024
Closest in time.
2024
Closest in time.
C. Bi, X. Luo, S. Shen, M. Zhang, H. Yue, and J. Yang, “Deedsr: Towards real-world image super-resolution via degradation-aware stable diffusion,” arXiv , 2024
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
J. Lin, Y. Wang, Z. Tao, B. Wang, Q. Zhao, H. Wang, X. Tong, X. Mai, Y. Lin, W. Song et al. , “Adaptive multi-modal fusion of spatially variant kernel refinement with diffusion model for blind image super-resolution,” arXiv , 2024
2024
Closest in time.
2024
Closest in time.
S. Khanna, P. Liu, L. Zhou, C. Meng, R. Rombach, M. Burke, D. Lobell, and S. Ermon, “Diffusionsat: A generative foundation model for satellite imagery,” ICLR , 2024
2024
Closest in time.