Image Restoration with Mixed or Unknown Noises

This paper proposes a simple model for image restoration with mixed or unknown noises. It can handle image restoration without assuming any prior knowledge of the noise distribution. It is particularly useful for solving real-life image restoration problems, since under various constraints, images a...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Multiscale modeling & simulation Ročník 12; číslo 2; s. 458 - 487
Hlavní autoři: Gong, Zheng, Shen, Zuowei, Toh, Kim-Chuan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Philadelphia Society for Industrial and Applied Mathematics 01.01.2014
Témata:
Algorithms
Applied mathematics
Computer simulation
Contamination
Fittings
Gaussian
Image restoration
Inverse problems
Noise
Renovating
Sparsity
ISSN:1540-3459, 1540-3467
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:This paper proposes a simple model for image restoration with mixed or unknown noises. It can handle image restoration without assuming any prior knowledge of the noise distribution. It is particularly useful for solving real-life image restoration problems, since under various constraints, images are always degraded with mixed noise and it is impossible to determine what type of noise is involved. The proposed model can remove mixed-type noises as well as unknown noises and at the same time also works comparably well against the model whose data fitting term is designed for a specific given noise type. While most of the existing methods for image restoration are designed specifically for a given type of noise, ours appears to be the first universal model for handling image restoration with various mixed noises and unknown noises. Extensive simulations on synthetic data show that our method is effective and robust in restoring images contaminated by additive Gaussian noise, Poisson noise, random-valued impulse noise, multiplicative Gamma noise, and mixtures of these noises. Numerical results on real data show that it can remove noises without any prior knowledge of the noise distribution. [PUBLICATION ABSTRACT]
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ObjectType-Article-2
ObjectType-Feature-1
content type line 23
ISSN:1540-3459
1540-3467
DOI:10.1137/130904533