Signal-dependent noise removal in pointwise shape-adaptive DCT domain with locally adaptive variance

Alessandro Foi, Vladimir Katkovnik, Karen O. Egiazarian

Abstract: This paper presents a novel effective method for denoising of images corrupted by signal-dependent noise. Denoising is performed by coefficient shrinkage in the shape-adaptive DCT (SA-DCT) transform-domain. The Anisotropic Local Polynomial Approximation (LPA) - Intersection of Confidence Intervals (ICI) technique is used to define the shape of the transform's support in a pointwise adaptive manner. The use of such an adaptive transform support enables both a simpler modelling 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, in terms of both numerical error-criteria and visual appearance, with sharp detail preservation and clean edges. Simulation experiments demonstrate the superior performance of the proposed algorithm for a wide class of noise models with a signal-dependent variance, including Poissonian (photon-limited imaging), film-grain, and speckle noise.