Wavelet domain denoising of multispectral remote sensing imagery adapted to the local spatial and spectral context
We propose a novel Bayesian wavelet domain denoising method for multispectral images, which adapts itself to the local spatial and spectral image context. The proposed estimator is derived under the minimum mean squared error criterion and it takes into account uncertainty about the presence of a signal of interest in a noisy observation. Comparisons with other recent wavelet domain denoisers for multiband data demonstrate the advantages of the new method both in terms of mean squared error and visually.