The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Advances in mathematical models for image processing
Abstract
Several state-of-the-art mathematical models useful in image processing are considered. These models include the traditional fast unitary transforms, autoregessive and state variable models as well as two-dimensional linear prediction models. These models introduced earlier [51], [52] as low-order finite difference approximations of partial differential equations are generalized and extended to higher order in the framework of linear prediction theory. Applications in several image Processing problems, including image restoration, smoothing, enhancement, data compression, spectral estimation, and filter design, are discussed and examples given.
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
Ahmed, N., Natarajan, T., & Rao, K. R. (1974) · IEEE Transactions on Computers · doi:10.1109/t-c.1974.223784 · Cited by 5,156
Singular value decomposition and least squares solutions
Golub, G. H., & Reinsch, C. (1970) · Numerische Mathematik · doi:10.1007/bf02163027 · Cited by 2,929
Cited by
Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.
Geman, S., & Geman, D. (1984) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1984.4767596 · Cited by 17,949
Image data compression: A review
Jain, A. K. (1981) · Proceedings of the IEEE · doi:10.1109/proc.1981.11971 · Cited by 709
Self-citationOn the Euclidean distance of images
Wang, L., Zhang, Y., & Feng, J. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.165 · Cited by 658
Color and spatial structure in natural scenes
Burton, G. J., & Moorhead, I. R. (1987) · Applied Optics · doi:10.1364/ao.26.000157 · Cited by 499
A local update strategy for iterative reconstruction from projections
Sauer, K., & Bouman, C. (1993) · IEEE Transactions on Signal Processing · doi:10.1109/78.193196 · Cited by 494
Metadata
- Authors
- Jain, A. K.
- Venue
- Proceedings of the IEEE
- Published
- 1981
- Volume
- 69
- Issue
- 5
- Pages
- 502-528
- ISSN
- 0018-9219 (ISSN-L)
- OpenAlex
- W2086627000
- Cited by
- 402
- References
- 2
- FWCI
- 13.34
- 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.
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: Advances in mathematical models for image processing. https://app.cochat.ai/papers/10.1109/proc.1981.12021
- The paper
- Jain, A. K. (1981). Advances in mathematical models for image processing. Proceedings of the IEEE. https://doi.org/10.1109/proc.1981.12021
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.
