The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization
Abstract
This paper proposes a two-phase scheme for removing salt-and-pepper impulse noise. In the first phase, an adaptive median filter is used to identify pixels which are likely to be contaminated by noise (noise candidates). In the second phase, the image is restored using a specialized regularization method that applies only to those selected noise candidates. In terms of edge preservation and noise suppression, our restored images show a significant improvement compared to those restored by using just nonlinear filters or regularization methods only. Our scheme can remove salt-and-pepper-noise with a noise level as high as 90%.
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
Davies, A., & Fennessy, P. (2001) · Digital Imaging for Photographers · doi:10.1016/b978-0-240-51590-8.50007-x · Cited by 4,371
Digital image processing (2nd ed.)
(1992) · CVGIP: Image Understanding · doi:10.1016/1049-9660(92)90019-y · Cited by 1,780
Handbook of Image and Video Processing
(2005) · Elsevier eBooks · doi:10.1016/b978-0-12-119792-6.x5062-1 · Cited by 1,578
A fast two-dimensional median filtering algorithm
Huang, T., Yang, G., & Tang, G. (1979) · IEEE Transactions on Acoustics, Speech, and Signal Processing · doi:10.1109/tassp.1979.1163188 · Cited by 1,390
Deterministic edge-preserving regularization in computed imaging
Charbonnier, P., Blanc-Feraud, L., Aubert, G., & Barlaud, M. (1997) · IEEE Transactions on Image Processing · doi:10.1109/83.551699 · Cited by 1,301
Random projection in dimensionality reduction: applications to image and text data
Bingham, E., & Mannila, H. (2001) · Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/502512.502546 · Cited by 1,274
Bayesian reconstructions from emission tomography data using a modified EM algorithm
Green, P. J. (1990) · IEEE Transactions on Medical Imaging · doi:10.1109/42.52985 · Cited by 1,241
A Nonlinear Primal-Dual Method for Total Variation-Based Image Restoration
Chan, T. F., Golub, G. H., & Mulet, P. (1999) · SIAM Journal on Scientific Computing · doi:10.1137/s1064827596299767 · Cited by 840
Handbook of Image and Video Processing
Wells, P. N. T. (2001) · Physiological Measurement · doi:10.1088/0967-3334/22/1/702 · Cited by 827
Black, M. J., & Rangarajan, A. (1996) · International Journal of Computer Vision · doi:10.1007/bf00131148 · Cited by 757
Cited by
Generating pit-free canopy height models from airborne lidar
Khosravipour, A., Skidmore, A. K., Isenburg, M., Wang, T., & Hussin, Y. A. (2014) · Photogrammetric Engineering & Remote Sensing · doi:10.14358/pers.80.9.863 · Cited by 347
An Unsupervised-Learning-Based Approach for Automated Defect Inspection on Textured Surfaces
Mei, S., Yang, H., & Yin, Z. (2018) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2018.2795178 · Cited by 308
Efficient Federated Learning With Spike Neural Networks for Traffic Sign Recognition
Xie, K., Zhang, Z., Li, B., Kang, J., Niyato, D., Xie, S., & Wu, Y. (2022) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2022.3178808 · Cited by 128
Metadata
- Authors
- Chan, R. H., Chung-Wa, & Nikolova, M.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2005-10
- Volume
- 14
- Issue
- 10
- Pages
- 1479-1485
- ISSN
- 1057-7149 (ISSN-L)
- OpenAlex
- W2122374500
- Cited by
- 1,152
- References
- 18
- FWCI
- 30.71
- 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: Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization. https://app.cochat.ai/papers/10.1109/tip.2005.852196
- The paper
- Chan, R. H., Chung-Wa, & Nikolova, M. (2005). Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization. IEEE Transactions on Image Processing. https://doi.org/10.1109/tip.2005.852196
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.
