Total Variation Regularization Algorithms for Images Corrupted with Different Noise Models: A Review
Total Variation (TV) regularization has evolved from an image denoising method for images corrupted with Gaussian noise into a more general technique for inverse problems such as deblurring, blind deconvolution, and inpainting, which also encompasses the Impulse, Poisson, Speckle, and mixed noise mo...
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2013/217021 |
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| author | Paul Rodríguez |
| author_facet | Paul Rodríguez |
| author_sort | Paul Rodríguez |
| collection | DOAJ |
| description | Total Variation (TV) regularization has evolved from an image denoising method for images corrupted with Gaussian noise into a more general technique for inverse problems such as deblurring, blind deconvolution, and inpainting, which also encompasses the Impulse, Poisson, Speckle, and mixed noise models. This paper focuses on giving a summary of the most relevant TV numerical algorithms for solving the restoration problem for grayscale/color images corrupted with several noise models, that is, Gaussian, Salt & Pepper, Poisson, and Speckle (Gamma) noise models as well as for the mixed noise scenarios, such the mixed Gaussian and impulse model. We also include the description of the maximum a posteriori (MAP) estimator for each model as well as a summary of general optimization procedures that are typically used to solve the TV problem. |
| format | Article |
| id | doaj-art-bffb7e9156274e7aaf7392c516a152c9 |
| institution | Kabale University |
| issn | 2090-0147 2090-0155 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-bffb7e9156274e7aaf7392c516a152c92025-02-03T01:01:40ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552013-01-01201310.1155/2013/217021217021Total Variation Regularization Algorithms for Images Corrupted with Different Noise Models: A ReviewPaul Rodríguez0Department of Electrical Engineering, Pontifical Catholic University of Peru, San Miguel, Lima 32, PeruTotal Variation (TV) regularization has evolved from an image denoising method for images corrupted with Gaussian noise into a more general technique for inverse problems such as deblurring, blind deconvolution, and inpainting, which also encompasses the Impulse, Poisson, Speckle, and mixed noise models. This paper focuses on giving a summary of the most relevant TV numerical algorithms for solving the restoration problem for grayscale/color images corrupted with several noise models, that is, Gaussian, Salt & Pepper, Poisson, and Speckle (Gamma) noise models as well as for the mixed noise scenarios, such the mixed Gaussian and impulse model. We also include the description of the maximum a posteriori (MAP) estimator for each model as well as a summary of general optimization procedures that are typically used to solve the TV problem.http://dx.doi.org/10.1155/2013/217021 |
| spellingShingle | Paul Rodríguez Total Variation Regularization Algorithms for Images Corrupted with Different Noise Models: A Review Journal of Electrical and Computer Engineering |
| title | Total Variation Regularization Algorithms for Images Corrupted with Different Noise Models: A Review |
| title_full | Total Variation Regularization Algorithms for Images Corrupted with Different Noise Models: A Review |
| title_fullStr | Total Variation Regularization Algorithms for Images Corrupted with Different Noise Models: A Review |
| title_full_unstemmed | Total Variation Regularization Algorithms for Images Corrupted with Different Noise Models: A Review |
| title_short | Total Variation Regularization Algorithms for Images Corrupted with Different Noise Models: A Review |
| title_sort | total variation regularization algorithms for images corrupted with different noise models a review |
| url | http://dx.doi.org/10.1155/2013/217021 |
| work_keys_str_mv | AT paulrodriguez totalvariationregularizationalgorithmsforimagescorruptedwithdifferentnoisemodelsareview |