Título:
The iDUDE framework for grayscale image denoising
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Autor:
Motta, G
Ordentlich, E
Ramírez Paulino, Ignacio
Seroussi, Gadiel
Weinberger, Marcelo
Ordentlich, E
Ramírez Paulino, Ignacio
Seroussi, Gadiel
Weinberger, Marcelo
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Tipo de documento:
Preprint
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Palabras clave:
Context-based denoising
Discrete universal de-noiser (DUDE) algorithm
Discrete universal denoising
Gaussian noise
Image denoising
Impulse noise
Discrete universal de-noiser (DUDE) algorithm
Discrete universal denoising
Gaussian noise
Image denoising
Impulse noise
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Año de publicación:
2011
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Resumen:
We present an extension of the discrete universal denoiser DUDE, specialized for the denoising of grayscale images. The original DUDE is a low-complexity algorithm aimed at recov-ering discrete sequences corrupted by discrete memoryless noise of known statistical characteristics. It is universal, in the sense of asymptotically achieving, without access to any information on the statistics of the clean sequence, the same performance as the best denoiser that does have access to such information. The DUDE, however, is not effective on grayscale images of practical size. The difficulty lies in the fact that one of the DUDE’s key com-ponents is the determination of conditional empirical probability distributions of image samples, given the sample values in their neighborhood. When the alphabet is relatively large (as is the case with grayscale images), even for a small-sized neighborhood, the required distributions would be estimated from a large collection of sparse statistics, resulting in poor estimates that would not enable effective denoising. The present work enhances the basic DUDE scheme by incorporating statistical modeling tools that have proven successful in addressing similar issues in lossless image compression. Instantiations of the enhanced framework, which is referred to as iDUDE, are described for examples of adadditive and nonadditive noise. The resulting denoisers significantly surpass the state of the art in the case of salt and pepper (S P) and M-ary symmetric noise, and perform well for Gaussian noise.
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G. Motta, E. Ordentlich, I. Ramirez, G. Seroussi and M. J. Weinberger, "The iDUDE Framework for Grayscale Image Denoising" [Preprint] Publicado en: IEEE Transactions on Image Processing, v. 20, no. 1, pp. 1-21. doi: 10.1109/TIP.2010.2053939.
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https://hdl.handle.net/20.500.12008/41106
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Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)
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| Ficheros | Descripción | Tamaño | Formato | ||
|---|---|---|---|---|---|
| MORSW11.pdf | — | 2.44 MB | Adobe PDF |