The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
Real-World Blur Dataset for Learning and Benchmarking Deblurring Algorithms
Overview
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
Verification
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.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892
GAN（Generative Adversarial Nets）
(2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 19,270
A flexible new technique for camera calibration
Zhang, Z. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.888718 · Cited by 15,004
Image denoising by sparse 3-D transform-domain collaborative filtering
Dabov, K., Foi, A., Katkovnik, V., & Egiazarian, K. (2007) · IEEE Transactions on Image Processing · doi:10.1109/tip.2007.901238 · Cited by 9,283
Martin, D., Fowlkes, C., Tal, D., & Malik, J. (2002) · Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 · doi:10.1109/iccv.2001.937655 · Cited by 8,216
Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring
Nah, S., Kim, T. H., & Lee, K. M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.35 · Cited by 2,275
A four-step camera calibration procedure with implicit image correction
Heikkila, J., & Silven, O. (2002) · Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.1997.609468 · Cited by 2,051
An FFT-based technique for translation, rotation, and scale-invariant image registration
Reddy, B. S., & Chatterji, B. N. (1996) · IEEE Transactions on Image Processing · doi:10.1109/83.506761 · Cited by 1,836
Removing camera shake from a single photograph
Fergus, R., Singh, B., Hertzmann, A., Roweis, S. T., & Freeman, W. T. (2006) · ACM Transactions on Graphics · doi:10.1145/1141911.1141956 · Cited by 1,815
DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks
Kupyn, O., Budzan, V., Mykhailych, M., Mishkin, D., & Matas, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00854 · Cited by 1,706
Cited by
Uformer: A General U-Shaped Transformer for Image Restoration
Wang, Z., Cun, X., Bao, J., Zhou, W., Liu, J., & Li, H. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01716 · Cited by 2,274
Multi-Stage Progressive Image Restoration
Zamir, S. W., Arora, A., Khan, S., Hayat, M., Khan, F. S., Yang, M.-H., & Shao, L. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01458 · Cited by 2,095
Exploring CLIP for Assessing the Look and Feel of Images
Wang, J., Chan, K. C. K., & Loy, C. C. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i2.25353 · Cited by 733
MAXIM: Multi-Axis MLP for Image Processing
Tu, Z., Talebi, H., Zhang, H., Yang, F., Milanfar, P., Bovik, A., & Li, Y. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00568 · Cited by 616
Efficient Frequency Domain-based Transformers for High-Quality Image Deblurring
Kong, L., Dong, J., Ge, J., Li, M., & Pan, J. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00570 · Cited by 398
Metadata
- Authors
- Rim, J., Lee, H., Won, J., & Cho, S.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2020
- Pages
- 184-201
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3085824656
- Cited by
- 373
- References
- 48
- FWCI
- 12.25
- Type
- conference paper
- Language
- en
- Open access
- closed
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Real-World Blur Dataset for Learning and Benchmarking Deblurring Algorithms. https://app.cochat.ai/papers/10.1007/978-3-030-58595-2_12
- The paper
- Rim, J., Lee, H., Won, J., & Cho, S. (2020). Real-World Blur Dataset for Learning and Benchmarking Deblurring Algorithms. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-030-58595-2_12
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
