CHAPTER 11 Denoising
Removing noise from signals is possible only if some prior information is available. This information is encapsulated in an operator designed to reduce the noise while preserving the signal. Ideally, the joint probability distribution of the signal and the noise is known. Bayesian calculations then derive optimal operators that minimize the average estimation error. However, such probabilistic models are often not available for complex signals such as natural images.
Simpler signal models can be incorporated in the design of a basis or a frame, which takes advantage of known signal properties to build a sparse representation. Efficient nonlinear estimators are then computed by thresholding the resulting coefficients. For ...
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