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

Li Dong, Jiantao Zhou, Yuan Y Tang
IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society 2016 December 14
28113317
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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