The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Fields of Experts: A Framework for Learning Image Priors
Abstract
We develop a framework for learning generic, expressive image priors that capture the statistics of natural scenes and can be used for a variety of machine vision tasks. The approach extends traditional Markov random field (MRF) models by learning potential functions over extended pixel neighborhoods. Field potentials are modeled using a Products-of-Experts framework that exploits nonlinear functions of many linear filter responses. In contrast to previous MRF approaches all parameters, including the linear filters themselves, are learned from training data. We demonstrate the capabilities of this Field of Experts model with two example applications, image denoising and image inpainting, which are implemented using a simple, approximate inference scheme. While the model is trained on a generic image database and is not tuned toward a specific application, we obtain results that compete with and even outperform specialized techniques.
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Cites
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892
An Information-Maximization Approach to Blind Separation and Blind Deconvolution
Bell, A. J., & Sejnowski, T. J. (1995) · Neural Computation · doi:10.1162/neco.1995.7.6.1129 · Cited by 8,986
Training Products of Experts by Minimizing Contrastive Divergence
Hinton, G. E. (2002) · Neural Computation · doi:10.1162/089976602760128018 · Cited by 4,879 · cited 2×
“Instead of running the Markov chain until convergence we use the idea of contrastive divergence [12] to initialize the sampler at the data points and only run it for a small, fixed number of steps.”
BackgroundMethodSparse coding with an overcomplete basis set: A strategy employed by V1?
Olshausen, B. A., & Field, D. J. (1997) · Vision Research · doi:10.1016/s0042-6989(97)00169-7 · Cited by 3,642
Cited by
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
Extracting and composing robust features with denoising autoencoders
Vincent, P., Larochelle, H., Bengio, Y., & Manzagol, P.-A. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390294 · Cited by 7,436
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
Elad, M., & Aharon, M. (2006) · IEEE Transactions on Image Processing · doi:10.1109/tip.2006.881969 · Cited by 5,321
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
Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
Denton, E., Chintala, S., Szlam, A. D., & Fergus, R. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.05751 · Cited by 1,541
Metadata
- Authors
- Roth, S., & Black, M. J.
- Venue
- 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)
- Publisher
- IEEE
- Published
- 2005-07-27
- Volume
- 2
- Pages
- 860-867
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2131686571
- Cited by
- 1,039
- References
- 4
- FWCI
- 39.34
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Roth, S., & Black, M. J. (2005). Fields of Experts: A Framework for Learning Image Priors. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05). https://doi.org/10.1109/cvpr.2005.160
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