The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Learning Low-Level Vision
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Citations
Cites
Neural Networks for Pattern Recognition
Bishop, C. M. (1995) · doi:10.1093/oso/9780198538493.001.0001 · Cited by 12,287
The Laplacian pyramid as a compact image code
Burt, P., & Adelson, E. (1983) · IEEE Transactions on Communications · doi:10.1109/tcom.1983.1095851 · Cited by 6,077
Emergence of simple-cell receptive field properties by learning a sparse code for natural images
Olshausen, B. A., & Field, D. J. (1996) · Nature · doi:10.1038/381607a0 · Cited by 5,694
Summation and Division by Neurons in Primate Visual Cortex
Carandini, M., & Heeger, D. J. (1994) · Science · doi:10.1126/science.8191289 · Cited by 649 · cited 2×
“This saves having to replicate the training set over all possible values of image contrast, and is a very simpli ed model of the contrast normalization which may take place in the mammalian visual system (Carandini and Heeger, 1994).”
Cited by
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
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
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
Kim, J., Lee, J. K., & Lee, K. M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.182 · Cited by 7,870
“We address the problem of generating a high-resolution (HR) image given a low-resolution (LR) image, commonly referred as single image super-resolution (SISR) [12], [8], [9].”
BackgroundRegion Filling and Object Removal by Exemplar-Based Image Inpainting
Criminisi, A., Perez, P., & Toyama, K. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2004.833105 · Cited by 3,258
The Bayesian brain: The role of uncertainty in neural coding and computation
Knill, D. C., & Pouget, A. (2004) · Trends in Neurosciences · doi:10.1016/j.tins.2004.10.007 · Cited by 2,588
Metadata
- Authors
- Freeman, W. T., Pasztor, E. C., & Carmichael, O. T.
- Venue
- International Journal of Computer Vision
- Published
- 2000-10
- Volume
- 40
- Issue
- 1
- Pages
- 25-47
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W2149760002
- Cited by
- 1,459
- References
- 4
- FWCI
- 21.92
- Type
- journal article
- 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.
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Learning Low-Level Vision. https://app.cochat.ai/papers/10.1023/a:1026501619075
- The paper
- Freeman, W. T., Pasztor, E. C., & Carmichael, O. T. (2000). Learning Low-Level Vision. International Journal of Computer Vision. https://doi.org/10.1023/a%3A1026501619075
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.
