MRI Super-Resolution With Partial Diffusion Models
Open accessAbstract
Diffusion models have achieved impressive performance on various image generation tasks, including image super-resolution. Despite their impressive performance, diffusion models suffer from high computational costs due to the large number of denoising steps. In this paper, we proposed a novel accelerated diffusion model, termed Partial Diffusion Models (PDMs), for magnetic resonance imaging (MRI) super-resolution. We observed that the latents of diffusing a pair of low- and high-resolution images gradually converge and become indistinguishable after a certain noise level. This inspires us to use certain low-resolution latent to approximate corresponding high-resolution latent. With the approximation, we can skip part of the diffusion and denoising steps, reducing the computation in training and inference. To mitigate the approximation error, we further introduced 'latent alignment' that gradually interpolates and approaches the high-resolution latents from the low-resolution latents. Partial diffusion models, in conjunction with latent alignment, essentially establish a new trajectory where the latents, unlike those in original diffusion models, gradually transition from low-resolution to high-resolution images. Experiments on three MRI datasets demonstrate that partial diffusion models achieve competetive super-resolution quality with significantly fewer denoising steps than original diffusion models. In addition, they can be incorporated with recent accelerated diffusion models to further enhance the efficiency.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
A pronounced domain gap exists between images and videos.
“Second, a pronounced domain gap exists between images and videos21–23.”
From Temporally consistent low-light face video enhancement via video-to-video conditional diffusion (2026) · cites this paper as [23]
The Introduction describes a statistical disparity between low- and high-resolution image latents, not a domain gap between images and videos. A search for an image–video domain gap and related terms found no passage in our copy addressing that claim; page numbers were unavailable. (LLM, conf=0.76)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al. (2019) · arXiv · doi:10.48550/arxiv.1912.01703 · Cited by 31,473 · see citing works
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067 · see citing works
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.19 · Cited by 12,696 · see citing works
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
Johnson, J., Alahi, A., & Fei-Fei, L. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46475-6_43 · Cited by 11,283 · see citing works
Image Super-Resolution Using Deep Convolutional Networks
Dong, C., Loy, C. C., He, K., & Tang, X. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2439281 · Cited by 10,105 · see citing works
Denoising Diffusion Probabilistic Models
Ho, J., Jain, A., & Abbeel, P. (2020) · arXiv · doi:10.48550/arxiv.2006.11239 · Cited by 8,712 · see citing works
Enhanced Deep Residual Networks for Single Image Super-Resolution
Lim, B. O., Son, S., Kim, H., Nah, S., & Lee, K. M. (2017) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.151 · Cited by 8,117 · see citing works
Shi, W., Caballero, J., Huszar, F., Totz, J., Aitken, A. P., Bishop, R., Rueckert, D., & Wang, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.207 · Cited by 7,570 · see citing works
(2017) · International Conference on Machine Learning · Cited by 6,382 · see citing works
SwinIR: Image Restoration Using Swin Transformer
Liang, J., Cao, J., Sun, G., Zhang, K., Gool, L. V., & Timofte, R. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00210 · Cited by 5,150 · see citing works
Large Scale GAN Training for High Fidelity Natural Image Synthesis
Brock, A., Donahue, J., & Simonyan, K. (2018) · arXiv · doi:10.48550/arxiv.1809.11096 · Cited by 3,717 · see citing works
Deep Unsupervised Learning using Nonequilibrium Thermodynamics
Sohl-Dickstein, J., Weiss, E. A., Maheswaranathan, N., & Ganguli, S. (2015) · arXiv · doi:10.48550/arxiv.1503.03585 · Cited by 2,637 · see citing works
Denoising Diffusion Implicit Models
Song, J., Meng, C., & Ermon, S. (2020) · arXiv · doi:10.48550/arxiv.2010.02502 · Cited by 2,507 · see citing works
Score-Based Generative Modeling through Stochastic Differential Equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., & Poole, B. (2020) · arXiv · doi:10.48550/arxiv.2011.13456 · Cited by 1,953 · see citing works
Image Super-Resolution Via Iterative Refinement
Saharia, C., Ho, J., Chan, W., Salimans, T., Fleet, D. J., & Norouzi, M. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3204461 · Cited by 1,829 · see citing works
Generative Modeling by Estimating Gradients of the Data Distribution
Song, Y., & Ermon, S. (2019) · arXiv · doi:10.48550/arxiv.1907.05600 · Cited by 1,453 · see citing works
Perceptual Losses for Real-Time Style Transfer and Super-ResolutionPreprint
Johnson, J. M., Alahi, A., & Fei-Fei, L. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.08155 · Cited by 1,180 · see citing works
EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis
Sajjadi, M. S. M., Schölkopf, B., & Hirsch, M. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.481 · Cited by 1,088 · see citing works
Single-Image Super-Resolution Using Sparse Regression and Natural Image Prior
Kim, K. I., & Kwon, Y. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.25 · Cited by 993 · see citing works
Image super-resolution using gradient profile prior
Sun, J., Xu, Z., & Shum, H. (2008) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2008.4587659 · Cited by 948 · see citing works
Feedback Network for Image Super-Resolution
Li, Z., Yang, J., Liu, Z., Yang, X., Jeon, G., & Wu, W. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00399 · Cited by 934 · see citing works
Deep Networks for Image Super-Resolution with Sparse Prior
Wang, Z., Liu, D., Yang, J., Han, W., & Huang, T. S. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.50 · Cited by 807 · see citing works
SRDiff: Single image super-resolution with diffusion probabilistic models
Li, H., Yang, Y., Chang, M., Chen, S., Feng, H., Xu, Z., Li, Q., & Chen, Y. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.01.029 · Cited by 777 · see citing works
FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors
Chen, Y., Tai, Y. Y., Liu, X., Shen, C., & Yang, J. J. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00264 · Cited by 600 · see citing works
Improved Techniques for Training Score-Based Generative Models
Song, Y., & Ermon, S. (2020) · arXiv · doi:10.48550/arxiv.2006.09011 · Cited by 482 · see citing works
Score-based diffusion models for accelerated MRI
Chung, H., & Ye, J. C. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102479 · Cited by 436 · see citing works
Knöll, F., Žbontar, J., Sriram, A., Muckley, M. J., Bruno, M. T., Defazio, A., Parente, M., Geras, K. J., Katsnelson, J., Chandarana, H., et al. (2020) · Radiology Artificial Intelligence · doi:10.1148/ryai.2020190007 · Cited by 420 · see citing works
WaveGrad: Estimating Gradients for Waveform GenerationPreprint
Chen, N., Zhang, Y., Zen, H., Weiss, R. J., Norouzi, M., & Chan, W. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.00713 · Cited by 410 · see citing works
Shan, Q., Li, Z., Jia, J., & Tang, C. (2008) · ACM Transactions on Graphics · doi:10.1145/1409060.1409106 · Cited by 364 · see citing works
Image upsampling via imposed edge statistics
Fattal, R. (2007) · ACM Transactions on Graphics · doi:10.1145/1276377.1276496 · Cited by 323 · see citing works
Progressive Distillation for Fast Sampling of Diffusion Models
Salimans, T., & Ho, J. (2022) · arXiv · doi:10.48550/arxiv.2202.00512 · Cited by 287 · see citing works
Chung, H., Sim, B., & Ye, J. C. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01209 · Cited by 285 · see citing works
SMORE: A Self-Supervised Anti-Aliasing and Super-Resolution Algorithm for MRI Using Deep Learning
Zhao, C., Dewey, B. E., Pham, D. L., Calabresi, P. A., Reich, D. S., & Prince, J. L. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3037187 · Cited by 196 · see citing works
Song, Y., Dhariwal, P., Chen, M., & Sutskever, I. (2023) · arXiv · doi:10.48550/arxiv.2303.01469 · Cited by 155 · see citing works
Israël, B., van der Leest, M., Sedelaar, J. P. M., Padhani, A. R., Zámecnik, P., & Barentsz, J. O. (2019) · European Urology · doi:10.1016/j.eururo.2019.10.024 · Cited by 118 · see citing works
Xie, Y., & Li, Q. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16446-0_62 · Cited by 106 · see citing works
Deep learning-based single image super-resolution for low-field MR brain images
den Bouter, M. L. D. L., Ippolito, G., O’Reilly, T., Remis, R., van Gijzen, M. B., & WEBB, A. G. (2022) · Scientific Reports · doi:10.1038/s41598-022-10298-6 · Cited by 99 · see citing works
CAT-Net: A Cross-Slice Attention Transformer Model for Prostate Zonal Segmentation in MRI
Hung, A., Zheng, H., Miao, Q., Raman, S., Terzopoulos, D., & Sung, K. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3211764 · Cited by 91 · see citing works
Self-citationMed-cDiff: Conditional Medical Image Generation with Diffusion Models
Hung, A. L. Y., Zhao, K., Zheng, H., Yan, R., Raman, S. S., Terzopoulos, D., & Sung, K. (2023) · Bioengineering · doi:10.3390/bioengineering10111258 · Cited by 51 · see citing works
Self-citationAnisotropic Super Resolution In Prostate Mri Using Super Resolution Generative Adversarial Networks
Sood, R., & Rusu, M. (2019) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2019.8759237 · Cited by 28 · see citing works
Truncated Diffusion Probabilistic Models and Diffusion-based Adversarial Auto-EncodersPreprint
Zheng, H., He, P., Chen, W., & Zhou, M. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2202.09671 · Cited by 27 · see citing works
Self-citationSkipDiff: Adaptive Skip Diffusion Model for High-Fidelity Perceptual Image Super-resolution
Luo, X., Xie, Y., Qu, Y., & Fu, Y. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i5.28195 · Cited by 16 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- [3]Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, and Bengio Y, “ Generative adversarial nets,” in Neural Information Processing Systems, Ghahramani Z, Welling M, Cortes C, Lawrence N, and Weinberger K, Eds., vol. 27. Curran Associates, Inc., 2014.
- [28]Lu C, Zhou Y, Bao F, Chen J, Li C, and Zhu J, “ Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps,” in Neural Information Processing Systems, 2022.
- [37]Litjens G, Debats O, Barentsz J, Karssemeijer N, and Huisman H, “ Prostatex challenge data,” Cancer Imaging Arch, vol. 10, p. K9TCIA, 2017.
Cited by
Sorted by citations
Diffusion Models in Low-Level Vision: A Survey
He, C., Shen, Y., Fang, C., Xiao, F., Tang, L., Zhang, Y., Zuo, W., Guo, Z., & Li, X. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3545047 · Cited by 109 · see citing works
Comprehensive exploration of diffusion models in image generation: a survey
Chen, H., Xiang, Q., Hu, J., Ye, M., Yu, C., Cheng, H., & Zhang, L. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11110-3 · Cited by 104 · see citing works
Lightweight diffusion models: a survey
Song, W., Ma, W., Zhang, M., Zhang, Y., & Zhao, X. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10800-8 · Cited by 27 · see citing works
Safari, M., Wang, S., Eidex, Z., Li, Q., Qiu, R. L. J., Middlebrooks, E. H., Yu, D. S., & Yang, X. (2025) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ade049 · Cited by 18 · see citing works
NExpR: Neural Explicit Representation for fast arbitrary-scale medical image super-resolution
Pang, K., Zhao, K., Hung, A. L. Y., Zheng, H., Yan, R., & Sung, K. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109354 · Cited by 7 · see citing works
Self-citationYang, Z., Xiao, H., Wang, X., Zhou, F., Deng, T., & Liu, S. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.108815 · Cited by 6 · see citing works
CHARMS: A CNN-Transformer Hybrid with Attention Regularization for MRI Super-Resolution
Li, X., Sun, H., & Li, T. (2026) · Sensors · doi:10.3390/s26020738 · Cited by 3 · see citing works
InfinityEBSD : Metrics-guided infinite-size EBSD map generation with diffusion models
Labady, S., Mesri, Y., Muñoz, D. P., Flipon, B., & Bernacki, M. (2026) · Acta Materialia · doi:10.1016/j.actamat.2026.122240 · Cited by 3 · see citing works
A Multi-view Open-access Dataset of Paired Knee MRI for Motion Artifact Removal
Xi, Y., Wang, F., Shi, F., Zhou, Q. H., Li, R., Li, Y., & Wang, Y. (2025) · Scientific Data · doi:10.1038/s41597-025-05439-1 · Cited by 3 · see citing works
Hamidian, M., Miri, R., & Fazeli, H. (2025) · Advances in Water Resources · doi:10.1016/j.advwatres.2025.105184 · Cited by 2 · see citing works
Condoiu, C., Băloi, A., Săndesc, D., Cumpănaş, A. A., Latcu, S. C., Dema, V., Căprariu, R., Barb, A. C., Ciucurita, A., Marinescu, A. R., et al. (2026) · Cancers · doi:10.3390/cancers18050730 · Cited by 2 · see citing works
Wang, J., Shi, Z., Gu, X., Yang, Y. C., & Sun, J. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103814 · Cited by 2 · see citing works
Dual-domain multi-modality brain MRI arbitrary-scale super-resolution network
Yang, Z., Wang, X., Zhou, F., & Xiao, H. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.129575 · Cited by 1 · see citing works
Anime4K: A Hybrid CNN–Transformer Network for Anime Super-Resolution
Liu, Q., Cai, F., Zhang, Z., Zhu, H., & Ding, Y. (2025) · IEEE Transactions on Broadcasting · doi:10.1109/tbc.2025.3622413 · Cited by 1 · see citing works
MARSeg: A Multimodal Adaptive-Resolution Segmentation framework for brain MRI
Li, Y., Liu, J., Zhang, H., Shi, B., & Lian, J. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110417 · Cited by 1 · see citing works
Temporally consistent low-light face video enhancement via video-to-video conditional diffusion
Ding, X., He, K., Sun, H., & Yang, J. (2026) · Scientific Reports · doi:10.1038/s41598-026-44219-8
Myocardial Scar Enhancement in LGE Cardiac MRI Using Localized Diffusion
Hasny, M., Demirel, Ö. B., Amyar, A., Faghihroohi, S., & Nezafat, R. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72378-0_29
Niu, M. (2025) · Theoretical and Natural Science · doi:10.54254/2753-8818/2025.25683
Rauso, G., Sangiovanni, M., Barra, S., Riccio, D., Staffa, M., D’Errico, L., & Longobardi, F. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-05060-1_7
Brain-CDM: A Cascaded Diffusion Model for Super-Resolution of Thick-Slice T2-FLAIR Brain MRI
Yu, R., Lu, J., & Zhuo, J. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356408
OS-DDPM: One-Step Denoising Diffusion Probabilistic Model for Anisotropic MRI Super-Resolution
Yan, Y., Wang, X., Wu, Z., Liu, J., Ju, X., Zhu, Y., & Shui, W. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356241
BoxSupSeg: Box-supervised polyp segmentation via multi-loss synergistic optimization
Long, J., Liu, J., & Lin, J. (2026) · Multimedia Systems · doi:10.1007/s00530-026-02246-5
Arif, S. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1770289
Pang, K., Miao, Q., Hung, A. L. Y., Oh, C., Zhao, K., He, Q., Nickel, D. M., Han, F., & Sung, K. (2026) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi61048.2026.11515945
Self-citationMulti-modal feature transfer network for anisotropic 3D MRI image super-resolution
Li, H., Yao, R., Du, J., Zhu, H., Peng, W., & Jia, Y. (2026) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-026-03204-3
Combining transformer and Mamba diffusion structures for super-resolution of brain MRI
Zhang, Q., Hang, Y., Chen, H., Min, B., & Qiu, J. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.111034
RAVEN: Robust, generalizable, multi-resolution structural MRI upsampling using autoencoders
Adame‐Gonzalez, W., Moqadam, R., Zeighami, Y., & Dadar, M. (2026) · Imaging Neuroscience · doi:10.1162/imag.a.1326
Lv, X., Lü, T., & Liu, Q. (2026) · International Conference on Image Processing and Artificial Intelligence (ICIPAI) · doi:10.1109/icipai70034.2026.11605782
A Comprehensive Survey on Deep Learning-Based Super-Resolution Techniques for 3D MRI Reconstruction
Manivasagan, N., & Sivabalaselvamani, D. (2026) · Algorithms for intelligent systems · doi:10.1007/978-3-032-32423-8_28
Semantic-Conditioned Latent Diffusion for Low-Field Brain MRI Enhancement
Zhang, D., Gao, J., Duan, C., Lou, X., & Wang, Z. J. (2026) · Proceedings - International Conference on Image Processing · doi:10.1109/icip61757.2026.11630281
Zhang, J., Yang, G., Gao, R., & Yang, Y. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.111343
Cui, Y., Liu, H., Zuo, W., Fang, C., Shang, F., Wan, L., Guo, L., & Wei, A. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.114696
Huang, S.-Y., Huang, X.-Y., Yang, Y., Wang, X.-Z., Ren, H., & Niu, Y.-F. (2026) · Journal of Computer Science and Technology · doi:10.1007/s11390-026-5762-3
Micro-ultrasound super-resolution with geometry-driven consistency models
Pang, K., Zhao, K., Miao, Q., Hung, A. L. Y., Oh, C., Ramirez, R. O., He, Q., Klein, J., Shao, W., Brisbane, W. G., et al. (2026) · Medical Image Analysis · doi:10.1016/j.media.2026.104333
Self-citationPW-GAN: Pseudo-Warping Field Guided GAN for Unsupervised Denoising of Fetal Brain MRI Images
Chen, L., Fu, G., Yang, P., Lin, J., Ning, K., Chen, Z., Liu, Y., Tan, T., Liu, H., Liu, B., et al. (2026) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-026-02416-x
- Authors
- Zhao, K., Pang, K., Hung, A. L. Y., Zheng, H., Yan, R., & Sung, K.
- Affiliations
- University of California, Los Angeles (US)
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2025-03
- Volume
- 44
- Issue
- 3
- Pages
- 1194-1205
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- PMC
- PMC11913589
- OpenAlex
- W4403511234
- Cited by
- 36
- References
- 42
- Citations by year
- 2026: 20 · 2025: 13 · 2024: 3
- FWCI
- 5.00
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Institutes of Health (R01-CA248506, R01-CA272702)
- Integrated Diagnostics Program of the Department of Radiological Sciences and Department of Pathology, David Geffen School of Medicine, University of California at Los Angeles
- 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 and Signal Denoising Methods
- Keywords
- MRI super-resolution, diffusion models, latent alignment, accelerated diffusion, denoising steps, image super-resolution
- MeSH terms
- Algorithms, Brain, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
