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• W2019251053 abstract "We develop a low-complexity overcomplete wavelet domain method for denoising digital images corrupted with non-stationary white additive Gaussian noise. The noise level for each pixel is estimated from a local window around that pixel. We use a shrinkage function that adapts itself to the noise level and to the spatially changing statistics of the image. Experiments show that this noise model has good results for different non-stationary noise sources. Finally, we extend our method for denoising images corrupted with signal-dependent noise." @default.
• W2019251053 created "2016-06-24" @default.
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• W2019251053 date "2006-10-01" @default.
• W2019251053 modified "2023-10-02" @default.
• W2019251053 title "Wavelet Domain Image Denoising for Non-Stationary Noise and Signal-Dependent Noise" @default.
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• W2019251053 doi "https://doi.org/10.1109/icip.2006.312694" @default.
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