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Noise Level Estimation for Natural Images Based on Scale-Invariant Kurtosis and Piecewise Stationarity.

Authors
  • Dong, Li
  • Zhou, Jiantao
  • Tang, Yuan Y
Type
Published Article
Journal
IEEE Transactions on Image Processing
Publisher
Institute of Electrical and Electronics Engineers
Publication Date
Dec 14, 2016
Identifiers
PMID: 27992339
Source
Medline
License
Unknown

Abstract

Noise level estimation is crucial in many image processing applications such as blind image denoising. In this work, we propose a novel noise level estimation approach for natural images by jointly exploiting the piecewise stationarity and a regular property of the kurtosis in band-pass domains. We design a K-means based algorithm to adaptively partition an image into a series of non-overlapping regions, each of whose clean versions is assumed to be associated with a constant, but unknown kurtosis throughout scales. The noise level estimation is then cast into a problem to optimally fit this new kurtosis model. In addition, we develop a rectification scheme to further reduce the estimation bias through noise injection mechanism. Extensive experimental results show that our method can reliably estimate the noise level for a variety of noise types, and outperforms some state-of-the-art techniques, especially for non-Gaussian noises.

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