Fetching the paper…
Reading the bibliography…
Blind super-resolution methods based on stable diffusion showcase formidable generative capabilities in reconstructing clear high-resolution images with intricate details from low-resolution inputs.
Wang Z, Bovik A, Sheikh H, et al (2004) Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing 13(4):600–612. 10.1109/TIP.2003.819861
2003
Earlier work this paper cites.
2010
Earlier work this paper cites.
Goodfellow IJ, Pouget-Abadie J, Mirza M, et al (2014) Generative adversarial networks. 1406.2661
2014
Earlier work this paper cites.
Dong C, Loy CC, He K, et al (2016) Image super-resolution using deep convolutional networks. IEEE Transactions on Pattern Analysis and Machine Intelligence 38(2):295–307. 10.1109/TPAMI.2015.2439281
2015
Earlier work this paper cites.
Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18, Springer, pp 234–241
2015
Earlier work this paper cites.
Kim J, Lee JK, Lee KM (2016) Accurate image super-resolution using very deep convolutional networks. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 1646–1654, 10.1109/CVPR.2016.182
2016
Earlier work this paper cites.
Agustsson E, Timofte R (2017) Ntire 2017 challenge on single image super-resolution: Dataset and study. In: CVPRW
2017
Earlier work this paper cites.
Ledig C, Theis L, Huszár F, et al (2017) Photo-realistic single image super-resolution using a generative adversarial network. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 105–114, 10.1109/CVPR.2017.19
2017
Earlier work this paper cites.
Timofte R, Agustsson E, Van Gool L, et al (2017) Ntire 2017 challenge on single image super-resolution: Methods and results. In: CVPRW
2017
Earlier work this paper cites.
Blau Y, Michaeli T (2018) The perception-distortion tradeoff. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 6228–6237, 10.1109/CVPR.2018.00652
2018
Earlier work this paper cites.
Zhang R, Isola P, Efros AA, et al (2018) The unreasonable effectiveness of deep features as a perceptual metric. In: CVPR
2018
Earlier work this paper cites.
Cai J, Zeng H, Yong H, et al (2019) Toward real-world single image super-resolution: A new benchmark and a new model. In: Proceedings of the IEEE International Conference on Computer Vision
2019
Earlier work this paper cites.
Karras T, Laine S, Aila T (2019) A style-based generator architecture for generative adversarial networks. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 4396–4405, 10.1109/CVPR.2019.00453
2019
Earlier work this paper cites.
Ho J, Jain A, Abbeel P (2020) Denoising diffusion probabilistic models. Advances in neural information processing systems 33:6840–6851
2020
Earlier work this paper cites.
Jinjin G, Haoming C, Haoyu C, et al (2020) Pipal: a large-scale image quality assessment dataset for perceptual image restoration. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XI 16, Springer, pp 633–651
2020
Earlier work this paper cites.
Wei P, Xie Z, Lu H, et al (2020) Component divide-and-conquer for real-world image super-resolution. In: Proceedings of the European Conference on Computer Vision
2020
Earlier work this paper cites.
Chen H, Wang Y, Guo T, et al (2021) Pre-trained image processing transformer. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 12299–12310
2021
Earlier work this paper cites.
Ke J, Wang Q, Wang Y, et al (2021) Musiq: Multi-scale image quality transformer. In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pp 5128–5137, 10.1109/ICCV48922.2021.00510
2021
Cited alongside, same era.
Liang J, Cao J, Sun G, et al (2021) Swinir: Image restoration using swin transformer. arXiv preprint arXiv:210810257
2021
Cited alongside, same era.
Radford A, Kim JW, Hallacy C, et al (2021) Learning transferable visual models from natural language supervision. In: International conference on machine learning, PMLR, pp 8748–8763
2021
Cited alongside, same era.
Wang X, Xie L, Dong C, et al (2021) Real-esrgan: Training real-world blind super-resolution with pure synthetic data. In: International Conference on Computer Vision Workshops (ICCVW)
2021
Cited alongside, same era.
Lin X, He J, Chen Z, et al (2023) Diffbir: Towards blind image restoration with generative diffusion prior. arxiv
2023
Later among the works it cites.
Lu C, Zhou Y, Bao F, et al (2023) Dpm-solver++: Fast solver for guided sampling of diffusion probabilistic models. 2211.01095
2023
Later among the works it cites.
Oquab M, Darcet T, Moutakanni T, et al (2023) Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:230407193
2023
Later among the works it cites.
Park J, Son S, Lee KM (2023) Content-aware local gan for photo-realistic super-resolution. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 10585–10594
2023
Later among the works it cites.
Sahak H, Watson D, Saharia C, et al (2023) Denoising diffusion probabilistic models for robust image super-resolution in the wild. 2302.07864
2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2021
Cited alongside, same era.
Chen C, Shi X, Qin Y, et al (2022) Real-world blind super-resolution via feature matching with implicit high-resolution priors
2022
Cited alongside, same era.
Gu J, Cai H, Dong C, et al (2022) Ntire 2022 challenge on perceptual image quality assessment. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 951–967
2022
Cited alongside, same era.
Ho J, Salimans T (2022) Classifier-free diffusion guidance. arXiv preprint arXiv:220712598
2022
Cited alongside, same era.
Li H, Yang Y, Chang M, et al (2022) Srdiff: Single image super-resolution with diffusion probabilistic models. Neurocomputing 479:47–59
2022
Cited alongside, same era.
Liang J, Zeng H, Zhang L (2022) Details or artifacts: A locally discriminative learning approach to realistic image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2022
Cited alongside, same era.
Liu L, Ren Y, Lin Z, et al (2022) Pseudo numerical methods for diffusion models on manifolds. In: International Conference on Learning Representations, URL https://openreview.net/forum?id=PlKWVd2yBkY
2022
Cited alongside, same era.
Lu C, Zhou Y, Bao F, et al (2022) Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps. arXiv preprint arXiv:220600927
2022
Cited alongside, same era.
Later among the works it cites.
Sun L, Wu R, Zhang Z, et al (2023) Improving the stability of diffusion models for content consistent super-resolution. arXiv preprint arXiv:240100877
2023
Later among the works it cites.
Wu R, Yang T, Sun L, et al (2023) Seesr: Towards semantics-aware real-world image super-resolution. arXiv preprint arXiv:231116518
2023
Later among the works it cites.
Xie L, Wang X, Chen X, et al (2023) Desra: detect and delete the artifacts of gan-based real-world super-resolution models. arXiv preprint arXiv:230702457
2023
Later among the works it cites.
2023
Later among the works it cites.
Yue Z, Wang J, Loy CC (2023) Resshift: Efficient diffusion model for image super-resolution by residual shifting. 2307.12348
2023
Later among the works it cites.
2024
Closest in time.
Lin S, Wang A, Yang X (2024) Sdxl-lightning: Progressive adversarial diffusion distillation. arXiv preprint arXiv:240213929
2024
Closest in time.
Nan K, Xie R, Zhou P, et al (2024) Openvid-1m: A large-scale high-quality dataset for text-to-video generation. arXiv preprint arXiv:240702371
2024
Closest in time.
Song Y, Sun Z, Yin X (2024) Sdxs: Real-time one-step latent diffusion models with image conditions. arXiv preprint arXiv:240316627
2024
Closest in time.
Wang Y, Yang W, Chen X, et al (2024) Sinsr: diffusion-based image super-resolution in a single step. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 25796–25805
2024
Closest in time.
Yu F, Gu J, Li Z, et al (2024) Scaling up to excellence: Practicing model scaling for photo-realistic image restoration in the wild. arXiv preprint arXiv:240113627
2024
Closest in time.