Sparsity based Poisson denoising

Raja Giryes; Michael Elad

doi:10.1109/EEEI.2012.6377139

Sparsity based Poisson denoising

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Sparsity based techniques have been widely used for image denoising. In this work we focus on Poisson noise and propose initial stages for a new strategy for its removal. We start with a method that removes the noise by converting it into an additive Gaussian noise using the Anscombe transform, applying a variant of the OMP-denoising algorithm. Then, following the recent work by Salmon et. al., we bypass the need for the Anscombe transform and rely directly on the noise statistics. The new strategy is shown to lead to near state-of-the-art results.

Original language	English
Title of host publication	2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012
DOIs	https://doi.org/10.1109/EEEI.2012.6377139
State	Published - 2012
Externally published	Yes
Event	2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012 - Eilat, Israel Duration: 14 Nov 2012 → 17 Nov 2012

Publication series

Name	2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012

Conference

Conference	2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012
Country/Territory	Israel
City	Eilat
Period	14/11/12 → 17/11/12

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10.1109/EEEI.2012.6377139

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title = "Sparsity based Poisson denoising",

abstract = "Sparsity based techniques have been widely used for image denoising. In this work we focus on Poisson noise and propose initial stages for a new strategy for its removal. We start with a method that removes the noise by converting it into an additive Gaussian noise using the Anscombe transform, applying a variant of the OMP-denoising algorithm. Then, following the recent work by Salmon et. al., we bypass the need for the Anscombe transform and rely directly on the noise statistics. The new strategy is shown to lead to near state-of-the-art results.",

author = "Raja Giryes and Michael Elad",

year = "2012",

doi = "10.1109/EEEI.2012.6377139",

language = "אנגלית",

isbn = "9781467346801",

series = "2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012",

booktitle = "2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012",

note = "null ; Conference date: 14-11-2012 Through 17-11-2012",

}

Giryes, R & Elad, M 2012, Sparsity based Poisson denoising. in 2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012., 6377139, 2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012, 2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012, Eilat, Israel, 14/11/12. https://doi.org/10.1109/EEEI.2012.6377139

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AU - Elad, Michael

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N2 - Sparsity based techniques have been widely used for image denoising. In this work we focus on Poisson noise and propose initial stages for a new strategy for its removal. We start with a method that removes the noise by converting it into an additive Gaussian noise using the Anscombe transform, applying a variant of the OMP-denoising algorithm. Then, following the recent work by Salmon et. al., we bypass the need for the Anscombe transform and rely directly on the noise statistics. The new strategy is shown to lead to near state-of-the-art results.

AB - Sparsity based techniques have been widely used for image denoising. In this work we focus on Poisson noise and propose initial stages for a new strategy for its removal. We start with a method that removes the noise by converting it into an additive Gaussian noise using the Anscombe transform, applying a variant of the OMP-denoising algorithm. Then, following the recent work by Salmon et. al., we bypass the need for the Anscombe transform and rely directly on the noise statistics. The new strategy is shown to lead to near state-of-the-art results.

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BT - 2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012

Y2 - 14 November 2012 through 17 November 2012

ER -

Sparsity based Poisson denoising

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