The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
A universal noise removal algorithm with an impulse detector
Abstract
We introduce a local image statistic for identifying noise pixels in images corrupted with impulse noise of random values. The statistical values quantify how different in intensity the particular pixels are from their most similar neighbors. We continue to demonstrate how this statistic may be incorporated into a filter designed to remove additive Gaussian noise. The result is a new filter capable of reducing both Gaussian and impulse noises from noisy images effectively, which performs remarkably well, both in terms of quantitative measures of signal restoration and qualitative judgements of image quality. Our approach is extended to automatically remove any mix of Gaussian and impulse noise.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“Of the image-processing algorithms, positive results have been shown for bilateral filtering and Gaussian blur algorithms [49,50].”
From Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection (2021) · cites this paper as [49]
Citations
Cites
Scale-space and edge detection using anisotropic diffusion
Perona, P., & Malik, J. (1990) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.56205 · Cited by 12,143
Bilateral Filtering for Gray and Color Images
Tomasi, C., & Manduchi, R. (2002) · Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271) · doi:10.1109/iccv.1998.710815 · Cited by 8,177 · cited 2×
“Throughout this paper, we will use standard matrix notation for images.”
MethodOn the origin of the bilateral filter and ways to improve it
Elad, M. (2002) · IEEE Transactions on Image Processing · doi:10.1109/tip.2002.801126 · Cited by 795
Detail-preserving median based filters in image processing
Sun, T., & Neuvo, Y. A. (1994) · Pattern Recognition Letters · doi:10.1016/0167-8655(94)90082-5 · Cited by 695
A new efficient approach for the removal of impulse noise from highly corrupted images
Abreu, E., Lightstone, M. L., Mitra, S. K., et al. (1996) · IEEE Transactions on Image Processing · doi:10.1109/83.503916 · Cited by 637
A generalization of median filtering using linear combinations of order statistics
Bovik, A., Huang, T., & Munson, D. (1983) · IEEE Transactions on Acoustics, Speech, and Signal Processing · doi:10.1109/tassp.1983.1164247 · Cited by 580
Fast Noise Variance Estimation
Immerkær, J. (1996) · Computer Vision and Image Understanding · doi:10.1006/cviu.1996.0060 · Cited by 555
Regularized iterative image restoration with ringing reduction
Lagendijk, R. L., Biemond, J., & Boekee, D. E. (1988) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/29.9032 · Cited by 291
Selective removal of impulse noise based on homogeneity level information
Pok, G., Liu, J., & Nair, A. S. (2003) · IEEE Transactions on Image Processing · doi:10.1109/tip.2002.804278 · Cited by 264
Median filters with adaptive length
Lin, H.-H., & Willson, A. N. (1988) · IEEE Transactions on Circuits and Systems · doi:10.1109/31.1805 · Cited by 209
Research works printed but not linked: 2
- Overton, A noise reducing preprocessing algorithm, 498
- UEA Signal and Image Processing Group
Cited by
Sorted by citations
Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection
Aust, J., Shankland, S., Pons, D., Mukundan, R., & Mitrovic, A. (2021) · Aerospace · doi:10.3390/aerospace8020030 · Cited by 66
“Of the image-processing algorithms, positive results have been shown for bilateral filtering and Gaussian blur algorithms [49,50].”
Method
Metadata
- Authors
- Garnett, R., Huegerich, T., Chui, C., & He, W.
- Affiliations
- Washington University in St. Louis (US)
- Rice University (US)
- University of Missouri–St. Louis (US)
- ORCID
- Venue
- IEEE Transactions on Image Processing
- Published
- 2005-11
- Volume
- 14
- Issue
- 11
- Pages
- 1747-1754
- ISSN
- 1057-7149 (ISSN-L)
- OpenAlex
- W2160817147
- Cited by
- 615
- References
- 13
- Citations by year
- 2026: 3 · 2025: 17 · 2024: 18 · 2023: 36 · 2022: 30 · 2021: 26 · 2020: 22 · 2019: 29 · 2018: 37 · 2017: 42 · 2016: 41 · 2015: 39 · 2014: 37 · 2013: 45 · 2012: 52
- FWCI
- 19.84
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Image Processing (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Enhancement Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
- Keywords
- impulse noise removal, Gaussian noise removal, mixed noise, local image statistics, image restoration, image quality
- MeSH terms
- Algorithms, Artificial Intelligence, Computer Simulation, Image Enhancement, Image Interpretation, Computer-Assisted, Numerical Analysis, Computer-Assisted, Signal Processing, Computer-Assisted, Stochastic Processes, Models, Statistical, Information Storage and Retrieval, Artifacts
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: A universal noise removal algorithm with an impulse detector. https://app.cochat.ai/papers/10.1109/tip.2005.857261
- The paper
- Garnett, R., Huegerich, T., Chui, C., & He, W. (2005). A universal noise removal algorithm with an impulse detector. IEEE Transactions on Image Processing. https://doi.org/10.1109/tip.2005.857261
