Adaptive-Size Block Transforms for Signal-Dependent Noise Removal

We present a new transform-based method for adaptive de-noising. It is assumed that the observations are given by a broad class of models with a signal-dependent variance. Denoising is performed by coefficient shrinkage in local block-transform domain. The intersection of confidence intervals (ICI) rule is used in order to determine the spatially-adaptive size of the block transforms. It enables both a simpler modeling of the noise in the transform domain and a sparser decomposition of the signal. Consequently, coefficient shrinkage is very effective and the reconstructed estimate's quality is high. Experiments with simulated as well as with real data demonstrate the advanced performance of the proposed algorithm

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