The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Filtering noise from images with wavelet transforms
Abstract
A new method of filtering MR images is presented that uses wavelet transforms instead of Fourier transforms. The new filtering method does not reduce the sharpness of edges. However, the new method does eliminate any small structures that are similar in size to the noise eliminated. There are many possible extensions of the filter. © 1991 Academic Press, Inc.
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
The wavelet transform, time-frequency localization and signal analysis
Daubechies, I. (1990) · IEEE Transactions on Information Theory · doi:10.1109/18.57199 · Cited by 6,483
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
Cited by
Wavelet transform domain filters: a spatially selective noise filtration technique
Xu, Y., Weaver, J. B., Healy, D. M., & Lu, J. (1994) · IEEE Transactions on Image Processing · doi:10.1109/83.336245 · Cited by 747
Self-citationDetection of Methane Plumes Using Airborne Midwave Infrared (3–5 µm) Hyperspectral Data
Scafutto, R. D. P. M., & De Souza Filho, C. R. (2018) · Remote Sensing · doi:10.3390/rs10081237 · Cited by 27
“This product is obtained by (i) filtering the noise in the image; (ii) highlighting spectral features; (iii) decreasing the spectral continuum variability introduced from variations in illumination and topography; and (iv) minimization of residuals from atmospheric correction and temperature estimation [27,28].”
Background
Metadata
- Authors
- Weaver, J. B., Xu, Y., Healy, D. M., & Cromwell, L. D.
- Venue
- Magnetic Resonance in Medicine
- Publisher
- Wiley
- Published
- 1991-10
- Volume
- 21
- Issue
- 2
- Pages
- 288-295
- ISSN
- 0740-3194 (ISSN-L)
- 1522-2594
- OpenAlex
- W2032871739
- Cited by
- 276
- References
- 2
- FWCI
- 1.73
- 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: Filtering noise from images with wavelet transforms. https://app.cochat.ai/papers/10.1002/mrm.1910210213
- The paper
- Weaver, J. B., Xu, Y., Healy, D. M., & Cromwell, L. D. (1991). Filtering noise from images with wavelet transforms. Magnetic Resonance in Medicine. https://doi.org/10.1002/mrm.1910210213
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.
