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Erschienen in:
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2016 | OriginalPaper | Buchkapitel

An Improved Self-adaptive Regularization Method for Mixed Multiplicative and Additive Noise Reduction

verfasst von : Ziling Wu, Hongxia Gao, Ge Ma, Lixuan Wu

Erschienen in: Pattern Recognition

Verlag: Springer Singapore

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Abstract

The noise in micro focus X-ray images is complicated with low signal-to-noise-ratio (SNR) and can be described as mixed multiplicative and additive noise. Nevertheless, the present self-adaptive regularization methods for smoothing such mixed noise remain scarce. Thus, this paper proposes an improved self-adaptive regularization method to reduce the mixed multiplicative and additive noise in micro focus X-ray images. A novel scheme to adaptively select the regularization operator and regularization parameter based on local variance is presented, in which a \( \varvec{p} \)-Laplace function is used as the regularization operator with self-adaptive \( \varvec{p} \) and the regularization parameter is designed according to a barrier function. Experiment results demonstrate that the proposed method can achieve a better balance between noise-reducing and edge-preserving, which effectively improve the denoising quality.

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Vorheriges Kapitel Clique-Based Locally Consistent Latent Space Clustering for Community Detection
Nächstes Kapitel The GEPSVM Classifier Based on L1-Norm Distance Metric
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Metadaten
Titel
An Improved Self-adaptive Regularization Method for Mixed Multiplicative and Additive Noise Reduction
verfasst von
Ziling Wu
Hongxia Gao
Ge Ma
Lixuan Wu
Copyright-Jahr
2016
Verlag
Springer Singapore
Buch
Pattern Recognition

Print ISBN: 978-981-10-3001-7

Electronic ISBN: 978-981-10-3002-4

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-981-10-3002-4_56

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