The DOI matches its Crossref record.
Image enhancement via adaptive unsharp masking
Abstract
This paper presents a new method for unsharp masking for contrast enhancement of images. The approach employs an adaptive filter that controls the contribution of the sharpening path in such a way that contrast enhancement occurs in high detail areas and little or no image sharpening occurs in smooth areas.
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Citations
Cited by
Transform coefficient histogram-based image enhancement algorithms using contrast entropy
Agaian, S. S., Silver, B., & Panetta, K. A. (2007) · IEEE Transactions on Image Processing · doi:10.1109/tip.2006.888338 · Cited by 575
Human visual system-based image enhancement and logarithmic contrast measure
Panetta, K. A., Wharton, E. J., & Agaian, S. S. (2008) · IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) · doi:10.1109/tsmcb.2007.909440 · Cited by 267
Yin, D., Zhao, Y., Wang, Y., Zhao, W., & Hu, X. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-08984-6 · Cited by 15
Generating Higher-Quality Anti-Forensics DeepFakes with Adversarial Sharpening Mask
Fan, B., Ding, F., Zhu, G., Huang, J., Kwong, S., Atrey, P., & Lyu, S. (2025) · ACM Transactions on Multimedia Computing, Communications, and Applications · doi:10.1145/3729233 · Cited by 8
Metadata
- Authors
- Polesel, A., Ramponi, G., & Mathews, V. J.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2000-03
- Volume
- 9
- Issue
- 3
- Pages
- 505-510
- ISSN
- 1057-7149 (ISSN-L)
- OpenAlex
- W2166538457
- Cited by
- 782
- References
- 0
- FWCI
- 3.07
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Image enhancement via adaptive unsharp masking. https://app.cochat.ai/papers/10.1109/83.826787
- The paper
- Polesel, A., Ramponi, G., & Mathews, V. J. (2000). Image enhancement via adaptive unsharp masking. IEEE Transactions on Image Processing. https://doi.org/10.1109/83.826787
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.
