In this paper, we study denoising of multicomponent images. We present a framework of spatial wavelet-based
denoising techniques, based on Bayesian least-squares optimization procedures, using prior models for the wavelet
coefficients that account for the correlations between the image components. Within this framework, multicomponent
prior models for the wavelet coefficients are required that a) fully account for the interband correlations
between the image components, and b) approximate well the marginal distributions of the wavelet coefficients.
For this, multicomponent heavy tailed models are applied. We analyze three mixture priors: Gaussian scale
mixture (GSM) models, Laplacian mixture models and Bernoulli-Gaussian mixture models. As an extension of
the Bayesian framework, we propose a framework that also accounts for the correlation between the multicomponent
image and an auxiliary noise-free image, in order to improve the SNR of the first. For this, a GSM prior
model was applied. Experiments are conducted in the domain of remote sensing in both, simulated and real
In this paper a new denoising technique for gray valued images is presented. The proposed technique is best suited for flat or textured images affected by relatively low noise levels, where we aim at high quality reconstruction of tiny image structures and fine details. To avoid the attenuation of these fine image details, we replace the common wavelet thresholding and shrinking rules by an averaging step over a certain region of consistent edge directions. This region is obtained by first extracting the pixels that belong to an "oriented structure". We develop a classification algorithm which extracts the oriented structures by using directional information from the wavelet detail images. After this classification step we perform an adaptive averaging: each pixel is averaged over a window that depends on the detected structures in its neighbourhood. We demonstrate the visual improvement of our method over two spatially adaptive wavelet shrinkage methods.