Abstract
Noise level estimation is a long-standing problem in image processing. The challenge arises from the estimation being easily affected by texture information. We propose an innovative noise level estimation method via the kurtosis test, which is a normalized fourth-order moment. The proposed method consists of two stages: the first one is to determine the image patches with normality using the kurtosis test; the noise level is then estimated from these selected normal patches in the second stage, in which the average of the standard deviations is used as the final estimation. Experimental results show that the proposed method outperforms the state-of-the-art estimation techniques in terms of noise level estimation and guided denoising.
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