The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.
Automatic Estimation and Removal of Noise from a Single Image
Abstract
Image denoising algorithms often assume an additive white Gaussian noise (AWGN) process that is independent of the actual RGB values. Such approaches are not fully automatic and cannot effectively remove color noise produced by todays CCD digital camera. In this paper, we propose a unified framework for two tasks: automatic estimation and removal of color noise from a single image using piecewise smooth image models. We introduce the noise level function (NLF), which is a continuous function describing the noise level as a function of image brightness. We then estimate an upper bound of the real noise level function by fitting a lower envelope to the standard deviations of per-segment image variances. For denoising, the chrominance of color noise is significantly removed by projecting pixel values onto a line fit to the RGB values in each segment. Then, a Gaussian conditional random field (GCRF) is constructed to obtain the underlying clean image from the noisy input. Extensive experiments are conducted to test the proposed algorithm, which is shown to outperform state-of-the-art denoising algorithms.
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Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
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A Computational Approach to Edge Detection
Canny, J. (1986) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1986.4767851 · Cited by 30,004
A theory for multiresolution signal decomposition: the wavelet representation
Mallat, S. G. (1989) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.192463 · Cited by 21,372
Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.
Geman, S., & Geman, D. (1984) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1984.4767596 · Cited by 17,949
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
Lafferty, J., McCallum, A., & Pereira, F. C. N. (2001) · ScholarlyCommons (University of Pennsylvania) · Cited by 12,935
Scale-space and edge detection using anisotropic diffusion
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Mean shift: a robust approach toward feature space analysis
Comaniciu, D., & Meer, P. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.1000236 · Cited by 11,305
De-noising by soft-thresholding
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Bilateral Filtering for Gray and Color Images
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Cited by
Toward Convolutional Blind Denoising of Real Photographs
Guo, S., Yan, Z., Zhang, K., Zuo, W., & Zhang, L. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00181 · Cited by 1,221
Exposing image splicing with inconsistent local noise variances
Pan, X., Zhang, X., & Lyu, S. (2012) · 2012 IEEE International Conference on Computational Photography (ICCP) · doi:10.1109/iccphot.2012.6215223 · Cited by 120
Metadata
- Authors
- Liu, C., Szeliski, R., Kang, S. B., Zitnick, C. L., & Freeman, W. T.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2008-02
- Volume
- 30
- Issue
- 2
- Pages
- 299-314
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2159736423
- Cited by
- 486
- References
- 55
- FWCI
- 20.95
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
