Multiresolution adaptive filtering of signal-dependent noise based on a generalized Laplacian pyramid

Signal-dependent noise may be described by a unique parametric model yielding additive, multiplicative, and film-grain noise. For such a model, adaptive filtering can be written as local linear minimum mean square error (LLMMSE) filtering. Multiresolution processing is exploited to achieve adaptivity also across scale, as SNR increases with the scale of the decomposition, in natural images. A generalized Laplacian pyramid is designed to match the signal-dependent nature of noise, thus allowing LLMMSE filtering to be carried out on its layers. Results from images affected by several types of synthetic noise are superior to those achieved without multiresolution context, by 1 to 2 dB on an average.