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Erschienen in:
2017 | OriginalPaper | Buchkapitel
Image Noise Estimation Based on Principal Component Analysis and Variance-Stabilizing Transformation
verfasst von : Ling Ding, Huying Zhang, Bijun Li, Jinsheng Xiao, Jian Zhou
Erschienen in: Image and Graphics
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Abstract
Image denoising requires taking into account the dependence of the noise distribution on the original image, and the performance of most video denoising algorithms depend on the noise parameters of noisy video, which is particularly important for the estimation of noise parameters. We propose a novel noise estimation method which combines principal component analysis (PCA) and variance-stabilizing transformation (VST), and extend the noise estimation to mixed noise estimation. We also introduce the excess kurtosis to ensure the accuracy of noise estimation and estimate the parameters of VST by minimizing the excess kurtosis of noise distribution. Subjective and objective results show that proposed noise estimation combining with classic video denoising algorithms obtains better effects and make video denoising more widely in application.