The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Noise Estimation from a Single Image
Abstract
In order to work well, many computer vision algorithms require that their parameters be adjusted according to the image noise level, making it an important quantity to estimate. We show how to estimate an upper bound on the noise level from a single image based on a piecewise smooth image prior model and measured CCD camera response functions. We also learn the space of noise level functions how noise level changes with respect to brightness and use Bayesian MAP inference to infer the noise level function from a single image. We illustrate the utility of this noise estimation for two algorithms: edge detection and featurepreserving smoothing through bilateral filtering. For a variety of different noise levels, we obtain good results for both these algorithms with no user-specified inputs.
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Citations
Cites
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
Object recognition from local scale-invariant features
Lowe, D. G. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.790410 · Cited by 16,472
Scale-space and edge detection using anisotropic diffusion
Perona, P., & Malik, J. (1990) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.56205 · Cited by 12,119
De-noising by soft-thresholding
Donoho, D. L. (1995) · IEEE Transactions on Information Theory · doi:10.1109/18.382009 · Cited by 9,619
Martin, D., Fowlkes, C., Tal, D., & Malik, J. (2002) · Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 · doi:10.1109/iccv.2001.937655 · Cited by 8,216
Bilateral Filtering for Gray and Color Images
Tomasi, C., & Manduchi, R. (2002) · Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271) · doi:10.1109/iccv.1998.710815 · Cited by 8,177 · cited 2×
“Adaptive Bilateral Filtering Bilateral filtering [17] is a simple and popular algorithm for feature-preserving image smoothing.”
BackgroundLucas-Kanade 20 Years On: A Unifying Framework
Baker, S., & Matthews, I. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000011205.11775.fd · Cited by 3,155
Image denoising using scale mixtures of gaussians in the wavelet domain
Portilla, J., Strela, V., Wainwright, M. J., & Simoncelli, E. P. (2003) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.818640 · Cited by 2,255
Zhang, R., Tsai, P.-S., Cryer, J. E., & Shah, M. (1999) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.784284 · Cited by 1,731
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Cited by
He, K., Sun, J., & Tang, X. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.213 · Cited by 5,546
Fattal, R. (2008) · ACM Transactions on Graphics · doi:10.1145/1360612.1360671 · Cited by 2,114
Sparse Representation for Color Image Restoration
Mairal, J., Elad, M., & Sapiro, G. (2008) · IEEE Transactions on Image Processing · doi:10.1109/tip.2007.911828 · Cited by 1,716
Unsupervised feature learning framework for no-reference image quality assessment
Ye, P., Kumar, J., Kang, L., & Doermann, D. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6247789 · Cited by 785
Nonlocal Image and Movie Denoising
Buades, A., Coll, B., & Morel, J.-M. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-007-0052-1 · Cited by 769
Bilateral Filtering: Theory and Applications
Paris, S., Kornprobst, P., Tumblin, J., & Durand, F. (2009) · Foundations and Trends in Computer Graphics and Vision · doi:10.1561/0600000020 · Cited by 586
Automatic Estimation and Removal of Noise from a Single Image
Liu, C., Szeliski, R., Kang, S. B., Zitnick, C. L., & Freeman, W. T. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1176 · Cited by 486
Self-citationA Fast Approximation of the Bilateral Filter Using a Signal Processing Approach
Paris, S., & Durand, F. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744085_44 · Cited by 481
Face Hallucination: Theory and Practice
Liu, C., Shum, H.-Y., & Freeman, W. T. (2007) · International Journal of Computer Vision · doi:10.1007/s11263-006-0029-5 · Cited by 398
Self-citationMishchenko, Y. (2009) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2008.09.006 · Cited by 73
Metadata
- Authors
- Liu, C., Freeman, W. T., Szeliski, R., & Kang, S. B.
- Venue
- 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (CVPR'06)
- Publisher
- IEEE
- Published
- 2006-07-10
- Volume
- 1
- Pages
- 901-908
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2160715448
- Cited by
- 316
- References
- 18
- FWCI
- 14.14
- Type
- conference paper
- 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).
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