The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Practical Poissonian-Gaussian Noise Modeling and Fitting for Single-Image Raw-Data
Abstract
We present a simple and usable noise model for the raw-data of digital imaging sensors. This signal-dependent noise model, which gives the pointwise standard-deviation of the noise as a function of the expectation of the pixel raw-data output, is composed of a Poissonian part, modeling the photon sensing, and Gaussian part, for the remaining stationary disturbances in the output data. We further explicitly take into account the clipping of the data (over- and under-exposure), faithfully reproducing the nonlinear response of the sensor. We propose an algorithm for the fully automatic estimation of the model parameters given a single noisy image. Experiments with synthetic images and with real raw-data from various sensors prove the practical applicability of the method and the accuracy of the proposed model.
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Cites
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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
A High-Quality Denoising Dataset for Smartphone Cameras
Abdelhamed, A., Lin, S., & Brown, M. S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00182 · Cited by 867
Benchmarking Denoising Algorithms with Real Photographs
Plotz, T., & Roth, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.294 · Cited by 655
Content-Adaptive Steganography by Minimizing Statistical Detectability
Sedighi, V., Cogranne, R., & Fridrich, J. (2016) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2015.2486744 · Cited by 573
Deep joint demosaicking and denoising
Gharbi, M., Chaurasia, G., Paris, S., & Durand, F. (2016) · ACM Transactions on Graphics · doi:10.1145/2980179.2982399 · Cited by 473
Metadata
- Authors
- Foi, A., Trimeche, M., Katkovnik, V., & Egiazarian, K.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2008-10
- Volume
- 17
- Issue
- 10
- Pages
- 1737-1754
- ISSN
- 1057-7149 (ISSN-L)
- OpenAlex
- W2136035751
- Cited by
- 928
- References
- 14
- FWCI
- 20.05
- Type
- journal article
- Language
- en
- Open access
- closed
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- Foi, A., Trimeche, M., Katkovnik, V., & Egiazarian, K. (2008). Practical Poissonian-Gaussian Noise Modeling and Fitting for Single-Image Raw-Data. IEEE Transactions on Image Processing. https://doi.org/10.1109/tip.2008.2001399
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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