The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Contextual and Variational Contrast Enhancement
Abstract
This paper proposes an algorithm that enhances the contrast of an input image using interpixel contextual information. The algorithm uses a 2-D histogram of the input image constructed using a mutual relationship between each pixel and its neighboring pixels. A smooth 2-D target histogram is obtained by minimizing the sum of Frobenius norms of the differences from the input histogram and the uniformly distributed histogram. The enhancement is achieved by mapping the diagonal elements of the input histogram to the diagonal elements of the target histogram. Experimental results show that the algorithm produces better or comparable enhanced images than four state-of-the-art algorithms.
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Cites
A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb01338.x · Cited by 88,486
Contour Detection and Hierarchical Image Segmentation
Arbeláez, P., Maire, M., Fowlkes, C., & Malik, J. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.161 · Cited by 5,454
Handbook of Parametric and Nonparametric Statistical Procedures
(2004) · Technometrics · doi:10.1198/tech.2004.s209 · Cited by 4,661
Two-dimensional pca: a new approach to appearance-based face representation and recognition
Yang, J., Zhang, D., Frangi, A. F., & Yang, J.-Y. (2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2004.1261097 · Cited by 3,563
A multiscale retinex for bridging the gap between color images and the human observation of scenes
Jobson, D. J., Rahman, Z., & Woodell, G. A. (1997) · IEEE Transactions on Image Processing · doi:10.1109/83.597272 · Cited by 3,036
Digital Image Processing: PIKS Scientific Inside
Pratt, W. K. (2007) · Wiley eBooks · doi:10.1002/0470097434 · Cited by 2,616
Contrast enhancement using brightness preserving bi-histogram equalization
Kim, Y.-T. (1997) · IEEE Transactions on Consumer Electronics · doi:10.1109/30.580378 · Cited by 1,586
A Dynamic Histogram Equalization for Image Contrast Enhancement
Abdullah-Al-Wadud, M., Kabir, M., Akber Dewan, M., & Chae, O. (2007) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2007.381734 · Cited by 1,346
Image enhancement based on equal area dualistic sub-image histogram equalization method
Wang, Y., Chen, Q., & Zhang, B. (1999) · IEEE Transactions on Consumer Electronics · doi:10.1109/30.754419 · Cited by 1,165
Minimum mean brightness error bi-histogram equalization in contrast enhancement
Chen, S.-D., & Ramli, A. R. (2003) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2003.1261234 · Cited by 994
Cited by
LIME: Low-Light Image Enhancement via Illumination Map Estimation
Guo, X., Li, Y., & Ling, H. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2639450 · Cited by 2,998
Kindling the Darkness: A Practical Low-light Image Enhancer
Zhang, Y., Zhang, J., & Guo, X. (2019) · Proceedings of the 27th ACM International Conference on Multimedia · doi:10.1145/3343031.3350926 · Cited by 1,507
Learning a Deep Single Image Contrast Enhancer from Multi-Exposure Images
Cai, J., Gu, S., & Zhang, L. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2794218 · Cited by 1,234
Efficient Contrast Enhancement Using Adaptive Gamma Correction With Weighting Distribution
Huang, S.-C., Cheng, F.-C., & Chiu, Y.-S. (2013) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2226047 · Cited by 1,233
Low-Light Image Enhancement via a Deep Hybrid Network
Ren, W., Liu, S., Ma, L., Xu, Q., Xu, X., Cao, X., Du, J., & Yang, M.-H. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2910412 · Cited by 508
Metadata
- Authors
- Celik, T., & Tjahjadi, T.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2011-12
- Volume
- 20
- Issue
- 12
- Pages
- 3431-3441
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W2011260964
- Cited by
- 531
- References
- 17
- FWCI
- 11.73
- Type
- journal article
- Language
- en
- Open access
- closed
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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.